quantum computing chip manufacturing project
DESCRIPTION
• Determined the optimal location of a computer chip manufacturing plant. Conducted an analysis on machine count and type, as well as the ideal factory¬ layout of the machines. Confirmed selections by forecasting a 5 year demand and production rate using Arena software on a team of 15 fellow industrial engineering students. Declared “Best Senior Team Project” at end of the year competition.TRANSCRIPT
MEMO
Date: May 2, 2013
To: Professor Barnes
From: Team Qu-Chips Ahoy!
RE: Final Report
This document is discussing our team’s final report. Our project is focusing on Qu-Chip
manufacturing.
Casey Oronzio
Max Khalilullah
Ross Hochman
Alan Sweet
Dave Kimmer
Ryan Danahy
Steve Caiola
Johnathon Thomas
Joe Renninger
Evan Hughes
Ben Tidd
Greg Swinburn
Phil Moore
Kevin Icken
Garrett Lubniewski
Kyle Christoffersen
Ben Bulson
INDUSTRIAL AND SYSTEMS ENGINEERING SSIE DEPARTMENT
MICROFABRICATION
QU-CHIP
Team Qu-Chips Ahoy!
Submitted in fulfillment of the requirement of ISE492
Spring Semester, 2013
Systems Science & Industrial Engineering Department T.J. Watson School of
Engineering & Applied Science State University of New York at Binghamton
Table of Contents Executive Summary.............................................................................................................1
Introduction ..........................................................................................................................2
Critical Assumptions ............................................................................................................2
Location ...............................................................................................................................3
Mexico .....................................................................................................................3
China ........................................................................................................................4
Korea ........................................................................................................................5
United States ............................................................................................................6
Nevada .....................................................................................................................8
Machines ..............................................................................................................................9
Machine Decision Process .....................................................................................10
Packaging ...........................................................................................................................12
Demand ..............................................................................................................................13
Forecasting .........................................................................................................................16
The Forecast ...........................................................................................................16
Forecast Analysis ...................................................................................................17
Forecasting Issues ..................................................................................................19
Aggregate Planning ............................................................................................................20
Decision Variables .................................................................................................20
Objective Function and Decision Variable Costs ..................................................21
Constraints .............................................................................................................22
Discussion of Results and Sensitivity Analysis .....................................................23
Utilization of ILP ...............................................................................................................25
Plant Layout .......................................................................................................................25
Nodal Layout .........................................................................................................27
Grid Layout ............................................................................................................28
Evaluation Chart ....................................................................................................29
Final Facility Layout ..............................................................................................30
Production Layout .................................................................................................31
Work Cell Methodology ........................................................................................31
Work Cell Flow......................................................................................................31
Layout Expansion ..................................................................................................31
Finances .............................................................................................................................32
Initial Investment ........................................................................................................32
Cost to Furnish .......................................................................................................33
Miscellaneous Supplies Cost .................................................................................33
Cost to Test the Machines ......................................................................................33
Startup Raw Material Cost .....................................................................................33
Startup Cost of Utilities .........................................................................................33
Initial Plant Renovations And Redesigns ..............................................................33
Startup Tooling Inventory Cost .............................................................................33
Salary Cost ....................................................................................................................34
Inventory Cost ...............................................................................................................35
Property Cost ................................................................................................................35
Machine Cost ................................................................................................................35
Shipping and Installation Cost ...............................................................................35
Utilities Cost ..........................................................................................................35
Manufacturing Overhead Cost ...............................................................................36
Black Box Cost ......................................................................................................36
MACRS Analysis for Machine Depreciation ................................................................38
Taxes ..............................................................................................................................39
Property Tax...........................................................................................................40
Federal Tax ............................................................................................................40
Health Insurance and Benefits .......................................................................................40
Inflation ..........................................................................................................................41
Qu-Chip Profit ...............................................................................................................41
Rate of Return ................................................................................................................41
Finance conclusion and Sensitivity Analysis .................................................................42
Simulation ..........................................................................................................................45
The Model ..............................................................................................................45
Inspection ...............................................................................................................48
Meeting the Historical Data ...............................................................................................52
Alternate Solution Proposal/Fitting Demand Forecast ..........................................54
Additional Sensitivity Analysis .............................................................................57
Sustainability......................................................................................................................60
Strategy and Management Approach .....................................................................60
Integrated Strategic Approach ...............................................................................62
Economic Impact ...................................................................................................62
Code of Conduct ....................................................................................................63
Climate Change and Energy Efficiency .................................................................63
Water Conservation ...............................................................................................64
Waste: Reduce, Reuse, and Recycle .....................................................................64
Chemical Waste .....................................................................................................65
Landfill Impact From Finished Goods ...................................................................65
Reducing Air Emissions ........................................................................................66
Performance Summary and Goals .........................................................................66
Ethics..................................................................................................................................66
Career Growth and Development ..........................................................................66
Communication and Recognition ..........................................................................66
Diversity .................................................................................................................67
Health, Safety, and Employee Wellness ................................................................67
Performance Summary and Goals .........................................................................67
Supply Chain Responsibility..................................................................................68
Supplier Environmental Impact .............................................................................68
Contributions to Society ........................................................................................68
Education ...............................................................................................................68
Community Engagement and Employee Volunteerism .........................................69
Empowering Women .............................................................................................69
Recommendations ..............................................................................................................69
Conclusion .........................................................................................................................70
References ..........................................................................................................................71
Appendix ............................................................................................................................74
1
Executive Summary
The following case study was conducted with the goal of drawing a conclusion of the optimal
business and production model to best produce a profit by manufacturing quantum computing
chips. The information provided to our team by upper management consisted of the expected
demand data along with a list of machines and their specifications that could be purchased for
each stage of the production process.
With the majority of the case study reliant upon our machine selection and floor set up, the first
step in determining the optimal facility is the number of machines needed. In order to do so we
had to calculate the ideal number to be both productive and satisfy demand. After determining
the amount of machines needed we then deciphered the optimal location of a plant. We also
determined its type and size. Nevada proved to be the optimal choice for reasons that include but
are not limited to: lack of corporate income taxes in state, low utility cost, and a capable
workforce in the area.
Due to the nature of the product this our company manufactures, it is necessary to have all
offices and production in one facility. This information was used to better determine the optimal
size of the facility. Research discovered cell based manufacturing is ideal for our product. Each
cell would contains one NUR etching machine, six Whitworth laminating machines, and one
Rudd mounting machine, along with a Beta insulator. The twelve Alpha insulators were to the
located outside the cells as the first step of the process. This works because the product can go
from them to any cell to complete the process.
A pull system will be used throughout each individual cell. In order to keep up with forecasted
demand it is necessary for the plant to contain eight cells. This setup will allow production of
120,000 to 140,000 chips per four week period.
In order for this plant to operate at capacity, 151 employees are required including both
production and office staff. Assuming the state average pay for similarly tasked employees about
one million dollars can be expected to be spent per period on wages. Our earnings through the
first year are expected to top 43 million dollars. Net profit the following 4 years in millions of
dollars are expected to be 53, 59, 62, and 64 respectively. This demonstrates a rate of return
eclipsing 28 percent which is well above the 15 percent minimum.
2
Introduction
Our team, Qu-Chips Ahoy! was given the opportunity to design a manufacturing plant. We were
told that we would be producing a new computer chip called the Qu-Chip. The company we
would be designing the plant for is called Microfabrication. We were given general information
to start the project, including that this is the first sealable quantum-computing chip. It is made
with an ion trap and qu-bit and requires a 6 step process. The first step being the alpha insulator,
followed by an etching machine and next a laminating machine. In ensuing step the chip goes
through a beta regulator and then a mounting. Finally the chip is inspected and shipped.
The alpha insulator, beta regulator, and mounting machine have only one option for our team to
purchase. The etching and laminating machines have 4 and 5 purchase options respectively. The
inspection process, which looks into the chips voltage, follows an equation that consists of a
Weibull distribution. There was a given range for this voltage test. The packaging process can
utilize either automated or skilled manual labor.
There are four countries that may serve as plant locations: China, Korea, Mexico, and the United
States. We were also given the revenue per chip and the cost to scrap at each step of the process.
This plant will run seven days a week and twenty-four hours a day. There are two twelve hour
shifts each day. Maintenance is required once a month and will stop production for four hours.
One of the major requirements is that there must be at least a minimum fifteen percent rate of
return on this entire investment. Below is the flow of the process:
Critical Assumptions
The simulation required a detailed list of assumptions to be generated by our team in order to
develop our model. Once an assumption was made, we added it to a running list of write-offs for
Professor Barnes to evaluate and critique. Of these assumptions, the following list stands out as
critical for the development and design of our Arena model: maintenance schedule, material
handling setup times and conveyor parameters.
As a team, we proposed scheduling our maintenance downtime on a flexible entity release
schedule that was dependent on slower demand periods. The proposal met the requirement that
maintenance must be done within a four week period, but it did not necessarily have to be done
for the exact four hour time slot on the second shift of the first Friday of the month. The
proposal was denied, so the model accounts for this given requirement.
Start
Receive Qu-ChipStore in
α InsulatorCommence
EtchingCommence Laminating
Store in β Regulator
Commence Mounting
Inspect completed Qu-Chip
Package Qu-Chip
End
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When the project’s requirements were given to us, setup times were not addressed. After
discussing this with Professor Barnes, we were informed that the system was “highly
automated.” With this knowledge, we proposed that accounting for transfer times in our model
sufficed as meeting the requirement for setup times. This was accomplished in our sequential
route modules. Route times are labeled as a user-defined variable “Transfer Time.”
There were three assumptions proposed and signed-off under the critical conveyor parameter
assumption. The first assumption addressed the height of the conveyor system. The only given
requirement pertaining to the conveyor system was that after inspection, the QuChips were to be
transported to shipping on a 14” tall conveyor. We proposed, and had written-off, that we could
establish our conveyor heights to the specific heights of the machines in our manufacturing
process. Next, we had to establish a logical materials handling speed for our “Transfer Time”
user-defined variable. We proposed this could be set to a common manufacturing human factor
figure. The same goes for the third proposal – conveyor carrying capacity.
Multiple assumptions had to be made for our financial analysis to ensure logical and accurate
costs could be calculated in an effort to establish a practical rate of return for this project. The
following are the most crucial assumptions that were made:
Investors are present and are the ones funding the project, as a multi-million dollar bank loan
would be infeasible. Our investors will have enough money needed as long as there is a 15%
rate of return
Due to the fact that 80% of the revenue is lost on a chip that fails inspection at the end of
production, 20% of the revenue is therefore the net profit of the chip.
Machines have salvage values at the end of the five-year projections, but a MACRS analysis can
be used for tax deductions.
Annual and hourly wage salaries will be based on statewide averages from the US Department of
Labor, which takes into account different seniority. The averages are based on 40-hour
workweeks.
Marketing, sales, and customer service are all done at a different location within the company.
This is simply a manufacturing plant. Therefore, those employees do not need to be included in
our cost breakdown.
Location
Mexico
After doing research we were able to decide on the following major advantages and
disadvantages of creating our manufacturing facility in Mexico. We had a total of three major
advantages for Mexico. Mexico has the largest airport in Latin America. This is the Mexico City
International Airport and is also the 44th largest airport in the world. Mexico is also part of the
North American Free Trade Agreement. This would allow our team more freedom when trading
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and transporting goods in North America. According to Mexican labor laws overtime begins
after forty eight hours of work each week. This would be an advantage as overtime begins in the
United States after forty hours of work each week.
On the opposite note, we also had a total of three disadvantages for Mexico. According to
Mexican labor laws employees are given more vacation days for every year they work for the
same employer. This could pose a problem over time as our business grows. The labor laws in
Mexico regarding vacation time are somewhat complicated and pose a problem. There is also
only one main highway in Mexico which is unable to accommodate the needs of the country.
This roadway was extended in 2005 but still does not meet the transportation needs of the region.
Although there are other transportation options, the highway system would need to be used
heavily and this poses a major problem for our team.
The largest disadvantage is that Mexico has a large amount of drug related crime. Therefore the
team decided not to choose to build the facility in Mexico. Approximately fifteen percent of
Mexicans have reported being victims of crime in 2012. The only other country in the world with
a higher figure was South Africa. The Mexican drug war has resulted in over sixty thousand
deaths and twenty thousand missing persons. There are approximately one hundred thousand
members of major drug cartels in Mexico. The following is a list of the major drug cartels
operating in Mexico: La Familia Cartel, Gulf Cartel, Juarez Cartel, Los Negros, Los Zetas,
Sinaloa Cartel, South Pacific Cartel, Tijuana Cartel, Los Caballeros Templarios. The drug related
crime and transportation issues are too much of a risk. Thus the team ultimately decided to
pursue different countries to build the facility.
China
Originally our team’s first thought was that China would have the cheapest employees, thus
making it a better option than other countries. After some research it became apparent that this
was not the case. China’s environmental, and ethical negatives outweighed their educational and
economical positives. Even though China has some of the cheapest labor, it does not mean that
this is the labor to entrust with the new Qu-Chip product.
China’s positive aspects include their minimum wage which is roughly 165 United States dollars
a month for full time employees within the Shanghai district. Another reason that China was
considered was due to their impressive education system. Education (primary and middle school)
in China is mandatory, as well as inexpensive, for citizens between the ages of 6 and 15. The
only costs include paying small fees for textbooks and uniforms. However, in the rural areas of
China there are many students who stop their education at the age of 15.
Looking at China’s work ethics, they receive many more days off for holidays then the United
States. Each employee receives eleven days off for national holidays yearly. They also receive
seven day holidays for Spring Festival and National Day. More than 150 million workers leave
their jobs to spend time with family and friends during these week breaks. With nearly all
businesses closing for these holidays, our team took this as a considerable issue. There is also a
risk for counterfeit money and corruption that is becoming an even bigger issue in China. Over
one-third of the managers stated that corruption is a major problem that hinders incoming
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business from seeking real estate in China.
China also has big complications with their current pollution regulations. Hundreds of thousands
of premature deaths and respiratory illnesses have been caused by the exposure to air pollution.
China’s water is also unfit for consumption due to industrial waste. China has tried to implement
laws that would punish those companies with poor environmental standards. The "Measures on
Environmental Information Disclosure" law is extremely similar to the Toxics Release Inventory
in the United States, which has been accredited with being one of the most successful tools for
reducing industrial pollution in the United States.
Another area of concern is China’s safety in the workplace. Over 83,000 workers died in work
accidents back in 2009, according to the State Administration of Work Safety. The State
Council’s Work Safety Commission enforces health and safety regulations. Yet the State Council
has been rather ineffective at enforcing these regulations.
Korea
The research we conducted on South Korea made the country look like a better area to
manufacture than one might initially assume. At first glance the country seems like a small
country under political pressure from its militant northern neighbor North Korea. It is also a
small country with little land to support a large manufacturing plant. However as we delved into
the labor laws and standards of the country we were pleasantly surprised.
As recently as 2007, South Korea amended The Labor Standard Act of 1997, to adopt more
Western employment ideals. This alteration dissolved the traditional ideal of lifetime
employment arrangements. Furthermore they utilize labor unions which are also common in the
United States. Yet they do lean on some rather strict weekly hour requirements. For example the
absolute maximum work week an employee is allowed is 56 hours per week. Furthermore that
amount of hours is also only allowed to be worked if their average weekly hours are below 44
hours a week for an entire year.
In addition to some of these complex labor laws, the minimum age in South Korea is also higher
than it is in the United States. Compared to the minimum wage in the United States South
Korea’s minimum wage is roughly ten United States dollars. Although this may prove a moot
point in a few years because the United States may raise their minimum wage to nine dollars an
hour. Despite this possibility the higher minimum wage in South Korea will still most likely
drive up cost of the skilled labor that we will require from the workers on the floor. This is due to
the advanced technology and automation on the machines required to manufacture quantum
chips. The South Korean population is very well educated which may also drive up the costs of
employees and might make manufacturing operators somewhat difficult to find.
Another area of concern with starting a business in any country is the crime rate. Despite the
high level of political tension being maintained by those in North Korea, South Korea has a very
low crime rate. Their crime rate is actually lower than the United States’ crime rate.
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Although the crime rate is low, the bellicose and unpredictable nature of the situation in North
Korea has ultimately steered us away from placing our company there. In recent headlines the
volatility and violent nature of North Korea has flared up and become the most threating it has
been since the death of their last leader Kim Jong Il. As recently as early April of 2013, North
Korea’s new leader Kim Jong Un has made nuclear threats towards both South Korea and the
United States. As violent rhetoric has increased it has pushed our interests to countries with more
secure political climates as a place to start our business.
One final concern that has caused us to consider other countries to construct the quantum facility
is the environmental issues that have been a concern in Asia recently. There have been many
clean air issues in South Korea much like in China. Quantum technology is a very new
technology and we do not want the manufacture of the chips to come under political
environmental pressure. Therefore overall due to the volatility in multiple facets of the political
climate in South Korea and the higher cost of labor, we have decided to build the quantum
manufacturing facility in another country.
United States
The United States was one of the highly considered countries proposed where we could locate
our Qu-Chip manufacturing facility. Being one of the wealthiest countries in the world, our team
knew this would be a viable option. We did research on many key aspects of the United States
that would provide evidence as to how successful our manufacturing system could be. Our main
focuses were on labor laws, including laws on working hours and wages, pollution, crime,
education, and taxes throughout the United States. From this research we then needed to narrow
down our search to a specific location within the U.S. which could best station our facility.
The body of labor law within the United States is an important factor to consider when
determining the United States’ location viability. It is this body of law which mediates and
regulates the rights of workers, employers and labor unions.
The United States’ Federal laws set standards for worker’s rights and override most state laws.
These rights are generally limited. Federal law establishes minimum wages and overtime rights
for most workers in the private and public sectors. In addition, Federal laws set minimum
workplace safety regulations. State laws are a more detailed outline of worker’s rights and vary
from state to state. State laws are able to expand both required wages and workplace safety
regulations. Both Federal and State laws protect workers from employment discrimination,
making your marital status, race, gender, religion, national origin and age independent from the
hiring decision.
The United States allows employees to be hired at will if they do not possess a collective
bargaining agreement or individual employment agreement. The United States have unions,
which positively influence the labor force, but negatively affect money-making corporations.
Unions are allowed in all states that are not right-to-work states. Right-to-work states allow
businesses to be more profitable and include the states: Alabama, Arizona, Arkansas, Florida,
Georgia, Idaho, Indiana, Iowa, Kansas, Louisiana, Michigan, Mississippi, Nebraska, Nevada,
7
North Carolina, North Dakota, Oklahoma, South Carolina, South Dakota, Tennessee, Texas,
Utah, Virginia and Wyoming.
Working hours within the United States seem high compared to other countries around the
world, but could be useful from a corporate standpoint. To date, the United States considers full
time 40 hours a week, 8 hours a day. Workers must be paid minimum wage and overtime, 1.5
times regular pay, if 40 hours a week is exceeded. However the United States allows workers to
work 2 full shifts a day, making the maximum workable workweek 80 hours a week, at 16 hours
a day. Children under the age of 18 cannot work dangerous jobs, such as manufacturing, and
children under the age of 16 cannot work in general. Although child labor can reduce costs, it is
not allowed in the United States.
The United States’ minimum wage is $7.25 an hour, defined by Federal law. This is a national
standard, meaning that the entire United States’ population abides by these standards. States
laws’ have the ability to expand minimum wages given to workers. The ability to expand varies
from state to state. Washington has the highest minimum wage of $9.25 an hour, while Georgia
and Wyoming have the lowest minimum wage of $5.15 an hour. Each state's’ minimum wage
rate is different and falls within the aforementioned range. From a business standpoint, it makes
sense to pick a location within the United States that allow for the cheapest wage to boost
revenue.
Pollution is another factor that needs to be considered when choosing an ideal location for a Qu-
Chip manufacturing plant. Pollution can hinder the manufacturing process and have many
indirect costs that may hurt future revenue. Types of pollution that need to be considered are air,
land, and radioactivity.
Air quality has always been an issue in the United States. In general, the west coast has far
greater issues with air pollution than the east coast, making the east coast a more attractive
location for manufacturing. States like California, Texas, Ohio, Alabama, Pennsylvania,
Michigan and Utah have issues with pollution and should be avoided. In addition ozone
depletion is an issue in the United States. The United States has the second highest carbon
dioxide emissions in the world, next to China.
Water pollution may have unforeseen effects on our manufacturing process. Money may have to
be spent to purify water needed for machinery, cooling and other relevant purposes. California,
Illinois, Washington, Hawaii, New Jersey, Ohio and Pennsylvania all have some polluted bodies
of water, which is undesirable for our manufacturing process.
The United States has some radioactive areas which are extremely undesirable. Three Mile
Island in Pennsylvania contains radioactive materials, meaning that we should stay away from
that state.
Overall the total crime rate of the United States is similar to that of other highly developed
countries. Crime rates vary in the U.S. depending on the type of community. Within metropolitan
statistical areas both violent and property crime rates are higher than the national average. In
cities located outside metropolitan areas, violent crime was lower than the national average,
8
while property crime was higher. For rural areas, both property and violent crime rates were
lower than the national average. Overall, New England had the lowest crime rates, for both
violent and property crimes, as well as the lowest homicide rates. Southern states had the highest
overall crime rates. Almost all of the nation’s wealthiest twenty states, which include northern,
mid-western, and western states such as Minnesota and California, had crime rates below the
national average. New England states also had the country’s highest median household income,
while southern states have the lowest.
We want to locate our facility in a place with minimal crime in order to reduce any risk to the
property, our building, our product, and most importantly the workers. Placing our facility in an
area with low property and violent crime rates will alleviate a certain degree of stress from the
manufacturing workforce and will look more appealing to our upper management. We will be
operating with an expensive and high quantity of inventory so it is vital that we take this
precaution.
The United States has fallen to “average” in international education rankings, placing 17th in the
developed world for education, according to a global report by education firm, Pearson. The
United States spends a huge amount per student on public schools. However, the US is ranked
37th in the world in education spending as a percentage of GDP. (Most of the leading countries
are third world, ranked high because of low GDP.)
A 2009 study found that U.S. students ranked 25th among 34 countries in math and science,
behind nations like China, Singapore, South Korea, Hong Kong and Finland. While an “average”
education system may not be ideal for the U.S., this could provide an adequate manufacturing
labor force for those who are less educated. Additionally it will provide the opportunity to find a
select group of highly educated research and development professionals.
In order to maximize profit, we need to have an inexpensive and easily accessible way of
transporting and selling our Qu-chips. Accordingly, the cross-border shipping from the U.S. to
both of its neighboring countries is robust. The North American Free Trade Agreement has
reduced barriers and tariffs, facilitating cross-border trade. Additionally, the U.S. International
Trade Commision institutes a free tariff on processors and controllers, whether or not combined
with memories, converters, logic circuits, amplifiers, or other circuits.
Nevada
Our group decided the United States would be the ideal location for our Qu-Chip manufacturing
facility from the aforementioned factors including labor laws, wages, pollution, crime, education,
and taxes. The United States offers many advantages over China, Mexico, and South Korea.
Specifically, the United States treats ethnic workers well, has little discrimination, can fire
unproductive workers at will and has very few tariffs while exporting. Although the United
States have strong unions in a few states, have average education, and some polluted areas; we
feel the United States will allow our business to thrive.
Our group felt Nevada was the ideal state to create a manufacturing facility because of its
superior geographic location, taxes, utility costs and shipping costs. Firstly, Nevada is close to
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the Pacific Ocean, allowing our facility to easily transport processors overseas to neighboring
countries. In addition, Nevada has 2,000 railroads, allowing our processors to be shipped
throughout the continental United States. Nevada’s economy has been growing over the years
and has very low taxes. Property taxes are very low in Nevada and corporate taxes do not exist.
Coupled with business tax incentives, Nevada allows businesses to thrive.
Machines
We were given a variety of machines to choose from to use in our manufacturing plant. The first
machine given was the Alpha Insulator. This machine is used to insulate the chip and can only
hold one at a time. The initial insulator costs $114,000 and an additional $7,900 per machine.
The next step in the process is the Etching. Four machines were given with different service rates
and distributions. They are also from multiple places in the world and therefore must be
purchased with different currencies. The different currency rates and trends can be found in the
appendix. A table of the Etching Machines costs, US dollar Equivalents, service rates, and
distributions can be seen below.
Etching Machine Cost Currency USD Equivalent Service
Rate
Distribution
NUR 101,473,057 Yen $1,084,809.25 31 Exponential
Epsilon 60 829,977.64 Pounds $1,276,888.68 60 Erlang 3
Laufenback 1,136,844.15 CHF - Swiss Franks $1,235,700.16 30 Exponential
Smooth 1,829,407.48 Euro $2,439,209.97 31 Erlang 5
The laminating machine is the next step. At this step there was also four machines to
choose from also consisting of different costs from various currencies. The laminating machines
also contained statistics on service rate for each run through the machine, the amount of times
the chip must be processed through a machine, the service rate of one pass through the machine
and a time that the chip must get to the Beta regulator before it becomes waste. The times for a
chip to be completely processed were the same for each machine (with the same distribution),
but had different times to waste. These numbers can be seen in the table below.
Laminating
Machine
Cost Currency USD
Equivalents
Service
Rate
Ts < Tw Distrib
ution
# time
processed
NUR 29,845,016 Yen $319,062.50 .05 .2 .035532 i.i.d.
Exp.
4
Whitworth 154,189.86 Pounds $238,169.02 .05 .2 .04583 i.i.d.
Exp.
4
10
Bonfort 433,730.75 Franks $471,446.47 .04 .2 .05328 i.i.d.
Exp.
5
Kropf 175,318.21 Euro $233,757.61 5 .2 .02475 i.i.d.
Exp.
4
The chip must then pass through the Beta Regulator. This machine can only hold one chip at a
time, but it is very important to transport the chip between the laminating machine and Beta
Regulator very quickly because the chip can become scrap if too much time passes. The
regulator costs $250,000 each. The next step in the process is the Mounting machine. There was
only one option for the machine and it is called the Rudd. It is listed at $1,520,000 for the
machine. It contains two paths that the chip can follow. The two probabilities of the path are as
follows: 99.6 % of the time 365 chips can be processed in an hour; 0.4% of the time the machine
can process 14 chips per hour.
A few guidelines were presented to us with the information of the machines. They are as follows:
We were given the option to purchase the machines by March 8th
or May 1st. Through analysis of
the currency equivalent trends, we decided that it was safe to wait until May 1st to make our
purchases. This would also allow us extra time to do more research on the optimal amount of
machines to purchase.
There were two machines (one etching machine and one laminating machine) that were titled
“NUR.” If these two machines were purchased by us then there would be a 5% reduction in costs
for each NUR machine purchased. This discount was taken into account while the machines
were being analyzed for optimal costs and efficiencies. We decided that this discount was not
enough to cause us to purchase the NUR in both the etching and laminating processes.
We were given the cost of scrap at different processes in the manufacturing system. The later the
chips are scrapped in the process, the larger the cost will be to do so, because of all of the time
and resources used to bring the chip to that point. If the chip has to be scrapped at the beginning
of the process it will cost 5% of the revenue of the chip. If the chip is scrapped at the Laminating
machines it will cost 20%, at the Beta Regulator it will be 60% and 80% at the inspection.
Machine Decision Process
This particular assignment presented a few choices when it came to selecting machines for the
plant; so when the time came to choose the best machines available for purchase several factors
had to be taken into consideration. Size, overall cost, and production rate were among the
important characteristics. Our team determined that given the high profitability of the finished
chips, production rate was the most important factor; and that comparatively, the size and cost of
the machines were the least important characteristics. Once it was decided that size and cost
would be looked at after production rate was evaluated, it was time to begin the analysis.
The only two processes where a choice of machine was present were the laminating and etching
processes. There was a choice of 4 different machines for each of the processes.
11
When it came to the laminating machine, the service rates for all four choices were practically
identical. Therefore, a simple numerical comparison of cost and chip scrapping potential
resulted in the Whitworth machine being the superior choice. While it must be noted that the
Bonfort machine did have a somewhat lower scrap potential, it was far more expensive and this
extra cost outweighed its small benefit especially given the large amount of machines that would
be needed. We also saw that the Kropf machine and Whitworth were very close in cost, but the
Whitworth seemed to have a scrap time that nearly doubled the Kropf’s. In the end we decided
that the Whitworth was the best decision for the laminating process. Below is a graph of the costs
for each machine that would be associated with the amount of demand that we may see in the
future. Kropf and Whitworth clearly stand out as the cheapest alternatives. This graph was made
by using a weekly demand from 0 to 80,000 chips per week. The costs for the laminating
machines came from the amount of machines needed to reach the weekly demand. This was
determined by the average production per week calculated by the given service rate.
The selection of the etching machines was not as simple because it did require a comparison of
the production rates. These production rates, or service rates, were given in terms of a probability
distribution. The distribution type and relevant parameters were provided and allowed our team
to evaluate the production rate using Arena. By creating a simple Arena model, which can be
seen below, the production outputs of all four machines could be compared side by side while
they are all given the same input demand. As you can see, the NUR and Laufenback machines
both produced the highest results; so the NUR machine, being cheaper, became the optimal
choice. Below is also a graph of the comparison of costs for the machines depending on the
weekly demand based on strictly the averages of the machine production times. The Epsilon 60
is much cheaper than the rest of the machines, but when run through Arena, it was obvious that
its probability distribution caused it’s overall production to run much slower than anticipated by
using strictly the production averages.
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13
Packaging
Packaging is an essential part of the manufacturing process that our group needs to be concerned
with. It doesn’t matter how many Qu-Chips our plant can produce if none of our products get to
the customer without being destroyed. Because of the delicacy of these processors, padding and
effective packing dimensions are a must. In addition, packaging dimensions must be compatible
with standard pallet dimensions to ensure that a each pallet is fully utilized and profits are
maximized.
An essential part of the manufacturing process is pallet dimensions. We could have the perfect
padding and packaging dimensions, but without considering pallet dimensions, we may lose
revenue to wasted space. To ensure maximum profits, a 100% utilization of pallet volume is
ideal. The standard pallet dimension in North America is 48 inches wide x 40 inches long. These
dimensions account for 30% of all wooden pallets in the world and is a good reference for our
manufacturing process. These pallets can easily hold a ton (~2,000lbs). Because each package is
very light, weight should not be a factor to consider when packaging.
Now that our pallet dimensions have been determined, we must calculate the size of the package
containing the Qu-Chip. A typical processor is 42.5mm x 45.0mm in size. The box
encompassing this processor is 3.5 x 3.5 x 2.5 inches. This allows the package to be amply
padded with protective materials in order to protect our Qu-Chip. Because we are selling to
individual customers, Qu-Chips will be packaged in individual boxes.
Our average daily demand of Qu-Chips is approximately 4,200 processors. Because a pallet is 48
inches x 40 inches, 209 boxes of Qu-Chips can be fit onto 1 layer of the pallet. Assuming we
allow 15 layers per pallet, we can fit 3,135 Qu-Chips onto 1 standard pallet.
Our group has chosen to purchase a packaging machine because of the large expected daily
demand of processors. Over the course of time, a packaging machine is an economical advantage
over hiring multiple workers at minimum wage. Because of the monotony of packaging
processors, our group would need to worry about supervising unskilled workers in order to
ensure high utilizations rates. A machine would be able to work faster than hiring unskilled
laborers and we would be able to accurately forecast output.
Our group found a machine called a Multifunction Automatic Boxing Machine which costs
$35,000 and can box 50-90 Qu-Chips per minute. This machine can automatically do jobs such
as manual folding, padding, paper box opening and box sealing. Over an 8 hour workday this
machine can box up to 43,200 Qu-Chips a day, which easily meets our daily demand.
Demand
On February 12, 2013 the demand data was released to our team. The data was given to us on
blackboard in the form of a .txt document. In order to transfer the data from blackboard to a text
document, we decided to simply copy and paste all of the data. We used Notepad as the template
for the text document. We then saved the file and imported it into Microsoft Excel. We quickly
14
found out that the way that the data was presented in the text file caused the data to be too large
to fit into Excel. The problem was that the data took up too many columns for Excel to handle.
We then researched many methods on line which included different data extraction methods and
VBA scripts that were unsuccessful. We finally ended up going into the text document and
manually splitting up the columns so that they could fit in Excel. This was done by going
through the data and carefully placing separations in the text by pressing enter. To put into
perspective of how large this data is, we also put the demand data into word. The demand data
filled up 4,400 pages at size 11 font and therefore will not be included in our appendix. After
splitting the data into a reasonable amount of columns and rows in the text document, we were
able to move the data to Excel. The data was then simplified and arranged by weekly data. There
are 105 weeks of data (two years). After a discussion with Professor Barnes, we concluded that
the data given to us in the .txt document was inter-arrival times. Unfortunately, the data was still
too large to be added to our report, but we were able to summarize the data in the form of a chart.
This made the data more visual and we were able to see the seasonality of our demand and its
trends over the past two years. We summed up the amount of demand arrivals of each week to
receive a total count of the demand in week intervals. This can be shown in the figure below.
A few problems we found with the data were that there were a few missing data points. These
empty data points were disregarded due to the fact that there were very few compared to the total
amount of data that we received for each week. In the figure above it can be seen that there is a
large drop in week 42 at which there was zero demand data. This demand was disregarded and
we skipped over this week during the forecasting process. The forecasting process used the
summed up weekly demand data as its initial data source.
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15
We also used this demand data to calculate average inter-arrival times that could be used in our
model. In order to simulate when the chips will be arriving at our station we decided that we
would need to use the inter-arrival times given. The average inter-arrival times for each week are
shown in the graph below. Week 44 was disregarded once again when analyzed due to lack of
data.
By looking at the data we were able to pick weeks with the fastest, medium, and slowest, slowest
and average inter-arrival times. The data for these weeks was then run through ExpertFit
individually in order to fit a distribution to the week’s demand. These distributions were then
used in our simulation in order to model different scenarios. We found that week 105 had the
fastest average inter-arrival rate of 0.00514 hours. Using both a KS test and Chi Square test we
found that a good distribution fit for this week’s inter-arrival times would be a Pearson Type
VI(E). In Arena this distribution would be represented by the equation 0.476927 * GAMM(1,
1.000538)/GAMM(1, 93.786243). An Arena simulation was run just to test this arrival rate to
ensure that it was accurate. The simulation was run for 24 hours a day, 7 days a week and
replicated 100 times. The results showed that on average there were 32,877 chips entering the
system with the use of this distribution while the original demand showed 32,380 chips.
The medium average inter-arrival time is represented by week 91. The average inter-arrival time
found in this week is .00944 hours. Using the same methods as above we were able to see that
the week’s inter-arrival distribution had a good fit with a Weibull distribution. In arena the
distribution found is shown as GAMM(0.009466, 0.996866).
The slowest average inter-arrival time is represented by week 85. The slowest inter-arrival time
found in this week is 0.01829 hours. Again we used the KS and Chi-Squared test in order to find
a good fit for the week’s inter-arrival distribution. We concluded that a good fit was a Gamma
distribution which is written as GAMM(0.018431, 0.992540).
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16
Forecast
Part of the scope of this project was to plan the production of Qu-chips for five years. In order to
do this we needed to develop a forecast of expected demand for the Qu-chips over the
aforementioned five year period. The data we were given was experienced demand over a two
year period. Ideally, forecasts should be made for much smaller time periods than five years
because demand can change in so many ways over a time frame of that length. This is especially
true when the forecast is made with only two years of data. However, a forecast needed to be
made and we had to do the best we could with that data we were given.
The Forecast
Forecasts can be made using several different methods. One of which is when a static model is
developed. The most basic form of a static model includes a level, trend, and a seasonal factor.
These three make up the forecast’s systematic component and can either be added, multiplied, or
added and multiplied together depending on the nature of the demand. For example, a
multiplicative systematic component is the product of all three components. In contrast, an
additive systematic component is found by adding all three. The level is synonymous with the y-
intercept of a linear regression equation. The trend is the same as the slope, and a seasonal factor
is used when demand shows cyclical increases and decreases demand over a certain period of
time. The second component of the forecast is the error component. The error should not try to
be forecasted because trying to do so is impossible. We, the forecasters, just needed to be aware
that this component would be present in our forecast no matter which method we decided to use.
Another method for forecasting is to use an adaptive forecast. Like the name suggests, these
forecasts change as more demand data is collected over time. These can end up being more
accurate because they take advantage of the latest trends or shifts in demand. Examples of
adaptive forecasts include the moving average method, simple exponential smoothing, Holt’s
model, and Winter’s model (Choppa & Meindl, 2010).
We ultimately decided to use a static model because we felt it was the most practical for our
needs. This was because we will not be able to adjust our forecast using updated demand data.
More specifically, we used a mixed systematic component which is expressed as:
Systematic component = (level + trend) x seasonal factor
There is more than one way to compute a forecast of this type. We followed a video made by
Dr. Jim Grayson, a professor of Management Science and Operations Management at Augusta
State University. Dr. Grayson’s video explained how to make a static forecast using Microsoft
Excel. Following the steps Dr. Grayson presented in his video, we developed our own Qu-chip
forecast model. The demand data we were given and used to develop our forecast is shown in
the next figure.
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Forecast Analysis
The original forecast we created using Dr. Grayson’s procedure was developed by forecasting all
52 weeks in the upcoming five years. This gave us a total of 260 forecasted data points. As
some of our team members have learned through taking Prof. Nagen’s Supply Chain
Management class is that an aggregate forecast is usually more accurate than a disaggregate one.
For example, if we were assigned the task of forecasting the number of cars that will be sold by
G.M. next year it would be much easier to forecast the total number of cars sold instead of
forecasting how many would be sold by their color. Not only would it be easier, but the forecast
itself would be more accurate. A possible explanation for this would be that the error component
for the aggregate forecast would be smaller compared to the sum of the individual error
components of the forecasts based on color. The individual forecasts would either overestimate
or underestimate the number of cars of that color that would be sold. If an aggregate forecast
was made some of those error components would have canceled each other out. This same
concept was applied to our data. Although we didn’t have to forecast different colored Qu-chips,
we could still make an aggregate forecast by grouping the weeks into periods. To simplify the
forecast, we decided to group every four weeks together for a total of 13 periods per year (as
opposed to creating 12 months of unequal lengths). We remade the forecast using 26 periods
and created a new forecast that contained a total of 65 periods which is much smaller than 260
weeks. To verify our decision, we calculated the Mean Absolute Deviations (M.A.D.) of the two
forecasts. The M.A.D. is calculated by summing the absolute values of the difference between
the demand in period i and the forecast for period i. That total is then divided by the total
number of observed/forecasted periods. Since the M.A.D. for our second forecast, the one using
the periods of four weeks, had four times as much data per observation as the original forecast
we divided its M.A.D. by four. This resulted in the second forecast having a lower and thus
more desirable M.A.D. of 1466.678 versus the 1655.253 of the original. The figures below are
the two forecasts graphed versus the original demand data, followed by the complete final
forecast.
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Forecasting Issues
In creating our forecast, we experienced several issues in terms of validity. The first issue dealt
with testing the accuracy of the forecast. As mentioned in the previous section, we calculated the
Mean Absolute Deviation of the two forecasts to determine their accuracies. However, the
M.A.D. should be used to test data that was not used to develop the forecast model. For
example, if we were given two extra years of data for a total of four, we could have used the first
two years to create the forecast model and then extrapolated it over two years. By extrapolating
it over two extra years we could have then compared the forecast versus the two extra years of
recorded demand data. Our M.A.D.s were calculated using demand data that was used to make
the forecast, therefore it is safe to say the values are biased. If we had only one more years’
worth of data we could have properly tested the accuracy of our forecast. Since we were given
only two years of data we had to use both to make the forecast, otherwise the forecast for year
two would have been identical to the demand experienced in year one.
A second issue we had to deal with also involved the amount of data we were given. Forecasts
are usually made with several years’ worth of data, or a large amount of whatever the time period
may be. In our case, we had two years and needed to forecast for five. A more appropriate
projection would have been for six months, or even less. This follows a basic principle of
statistics; a small sample size tends to lead to inaccuracies. There is no reason to below our
forecast should be any different. Two years of data is not nearly enough to make an accurate
forecast for five years, especially for such an immature product like Qu-chips. However, this is
what we were given and we had to make due.
The third and final major issue we encountered had to do with the demand data. This issue also
ties in with the fact that we only had two years’ worth. As previously mentioned, our demand
data seems to be trending with seasonality. Due to that observation, we forecasted our expected
20
demand by including a level, trend, and a seasonality factor in the model. But if one were to take
a closer look at the last 12 data points of the demand data, it looks like the data is beginning to
spike up and down. This hints at the possibility that the demand may begin to stabilize or
develop a new trend/seasonality. However, we were faced again with the sample size issue. We
were given 104 weeks of data (105 if the week whose demand was zero that we omitted was
included) and to change the forecast based off of 12 of those weeks would not make sense. We
ultimately left the forecast alone but felt this issue needed to be addressed.
Aggregate Planning
Aggregate planning can be used to determine capacity changes, production and inventory levels,
whether to subcontract or not, the number of times backlogging/stocking out is necessary, and
the hiring and firing of workers over a specific time period. For our model, we looked only at
changing capacities, hiring workers, and production and inventory levels. We assumed
backlogging orders and stock outs would not be acceptable to our customers. Another reason we
did not include them in our model was because we had no knowledge of the cost that would be
associated with not meeting our demand. We also did not have confirmed demand data so we
would be forecasting stock outs and that did not seem sensible. Subcontracting was not an
option because our task was to have total control of the production of the Qu-chips, nor did we
have any means of assigning a price to do so. Firing workers not only meant that our workforce
would be reduced, but our capacity would be as well. This was because in our model the number
of workers was directly tied to the number of machines we purchased. We would not sell our
machines due to low levels of demand, only to buy more once demand appeared to be increasing
again. This would be too costly and not a proper allocation of resources.
The mathematical model we used to plan our Qu-chip production was an integer linear program
(ILP). This is similar to a linear program (LP), except all of the decision variables can only have
values that are integers. Using an ILP was necessary because we could not produce half a chip
or hire three tenths of a worker. LPs and ILPs have three primary components, an objective
function, decision variables, and constraints. The objective function and constraints all consist of
linear expressions, hence the term linear program. The objective function can be maximized,
minimized, or set to equal a value. The decision variables are assigned costs and are found in
both the objective function and the constraints. The constraints limit the values of decision
variables either individually, or according to some linear relationship of two or more. The value
of each decision variable multiplied by its respective cost determines the value of the objective
function. The following subsections go into further detail about the objective function, decision
variables and constraints used in our ILP.
Decision Variables
Our ILP contained 11 different types of decision variables. Two were related to our workforce,
Ht represented the number of workers hired in period t, and Wt represented the total number of
workers in period t, which were those hired plus how many there were in period t-1. The next
two constraints were inventory related. Pt was the number of Qu-chips produced in period t, and
It was the number of Qu-chips in inventory at the end of period t. Dt was not changed in the ILP,
but was used to represent the forecasted demand for period t. This was used to help determine
21
the production and inventory levels. The final six constraints were related to the three different
kinds of machines we needed to purchase, etching, laminating, and mounting. Et represented the
number of etching machines used in period t, and NEt represented the number of new etching
machines purchased at the beginning of period t. Similar notations were used to represent the
number of laminating and mounting machines, using Lt and NLt, and Mt and NMt respectively.
Objective Function and Decision Variable Costs
The objective function did not use all of our decision variables, as only Ht, It, NEt, NLt, and NMt
were included. Each decision variable was multiplied by its respective cost to give the following
equation:
z =
where CH = 3,336.48
CIt = [76.91(P/F,1.76/13%,t)](A/P,1.76/13%,65)
CEt = [1084809.25(P/F,3/13%,t)](A/P,3/13%,65)
CLt =[238169.02(P/F,3/13%,t)](A/P,3/13%,65)
CMt =[1520000(P/F,3/13%,t)](A/P,3/13%,65)
*The terms (P/F,3/13%,t) and (P/F,1.76/13%,t) and were omitted from period 1 when
calculating CIt, CEt, CLt, and CMt
We decided to use annual worth values to represent the costs because it seemed to fit the nature
of the analysis best. The production forecast was already distributed throughout the five year
time frame, so it made sense to calculate the machines’ costs so they could be assigned to the
specific period when they needed to be purchased. The same logic was used to account for
hiring new workers. By using the decision variables representing the purchase of a new
machine, we ensured that only one value for each machine would be used in the calculation of z.
These values are not representative of the actual price we will pay for each machine over the five
year planning horizon, as annual worth values are used to compare alternatives and not to show
how much is actually paid per period. A more accurate financial analysis will be covered in the
finance section of this report.
The final two costs used were the cost per worker, and the cost of chips in inventory. We
assumed the workers’ pay to already be annualized for our time periods, and CH was determined
by researching the average machine operator wage in Nevada. The average hourly rate, $19.86
was then multiplied by the number of hours each worker would work per period, 168 (it is not
160 because of the 12 hour shift schedule we will implement). For the ILP only, we used the
profit margin to represent the holding cost per period per chip, IT. Since there is no inventory tax
in Nevada, we needed a value to see if inventory costs would affect the ILP’s output. Originally,
we only included production costs and not inventory costs. Both solutions were similar, but the
model accounting for an inventory cost suggested we add two cells in period 63 instead of one in
period 60.
22
Constraints
There were nine primary types of constraints used in our model, but in total we included 589.
Below are the general equations of the nine different types. Each constraint type was altered for
the first one or two periods because the initial manufacturing conditions had to be determined
and this required slight adjustments to the constraints.
Constraint General Equation, **for t = 3 to 65, ***for t = 2 to 65
Workforce** Wt = Wt-1+ Ht
Capacity*** Pt = 20040 * Et
Inventory*** Dt = It-1 + Pt - It
Etching Machines** Et = Et-1 + NEt
Laminating Machines** Lt = Lt-1 + NLt
Mounting Machines** Mt = Mt-1 + NMt
Worker to Cell Ratio*** Wt = 12 * Et
Etching to Laminating Ratio*** Lt = 6 * Et
Etching to Mounting Ratio*** Mt = Et
The following table shows the constraints used in periods one and two, if applicable.
Constraint Period 1 Period 2
Workforce W1 = 0 and H1 = 12 * NE1 W2 = H1 + H2
Capacity P1 = 20040 * NE1
Inventory D1 = P1 – I1
Etching Machines E1 = 0 and NE1 => 1 E2 = NE1 +NE2
Laminating Machines L1 = 0 and NL1 => 6 L2 = NL1 +NL2
Mounting Machines M1 = 0 and NM1 => 1 M2 = NM1 +NM2
Worker to Cell Ratio H1 = 12 * NE1
Etching to Laminating Ratio 6 * NE1 = NL1
Etching to Mounting Ratio NE1 = NM1
All of the decision variables were also assigned a lower bound of zero (i.e. a non-negativity
constraint), and they could only take on integer values (1, 2, 3…). For the capacity constraints,
the value of 20040 was calculated using three numbers. The first was the mean service rate per
laminating machine, which was five per hour. The second number was six, for the number of
laminating machines per cell. The final value was the total number of hours per week.
Throughout the duration of a period, the only scheduled down time for the machines is four
hours on the first Friday of every month. This left us with 24 x 7 x 4 – 4 = 668 hours of
production time per machine per period. 668 multiplied by 30 (six machines producing an
average of five Qu-chips an hour) gave us an estimated 20040 Qu-chips per cell per period.
Although there will only be 12 periods of down time per year, we gave all 13 periods one for the
sake of simplicity.
23
Discussion of Results and Sensitivity Analysis
The final optimal solution of the ILP revealed to us our hiring and production schedule for the
five year time frame. In period one we will hire 72 full time workers to operate the machine
cells. Each cell consists of three workers or 12 over a total of four different shifts, an etching
machine, a mounting machine, and six laminating machines. Thus, we will purchase six cells at
the start of production (six etching machines, 36 laminating machines, and six mounting
machines). We will not purchase or hire more workers until period 63. At that time we will hire
24 more workers and purchase two more machine cells. We will end period 65 with 96 full time
machine operators, eight etching machines, 48 laminating machines, and eight mounting
machines. For periods one through 62, we will aim to produce 120240 Qu-chips, or an average
of 30060 per week. In periods 63-65 production levels will increase production to 160320 Qu-
chips per period.
Despite the seasonality trend in our forecast, we plan on producing a constant number of Qu-
chips per period. This is because we have chosen to follow a level production strategy. A level
strategy means inventory is built up during periods of low demand in anticipation of future
periods of demand that exceed production capacity for that period. This will allow us to not have
to purchase more machines than are actually needed. As shown below, we plan on having large
amounts of inventory that will fluctuate over time. Since the Qu-chips are so small and can be
packaged together very easily, we will be able to store them without dedicating too much space
aside to store them. Our inventory policy will be first-in-first-out, meaning the chips that have
been in queue for the longest will be sold first, and the chips produced that period will be used if
our inventory levels don’t satisfy the period’s demand. During the period with the highest
projected inventory level will have about five periods, or 20 weeks of inventory. Although this
is a large amount of inventory, it will be built up in anticipation of future demand. The level
strategy will lead to much better employee morale than laying off workers when demand is low,
which is known as a chase strategy. A chase strategy would not be feasible because our
inventory costs are much lower than the cost of constantly buying and selling machines, as well
as hiring and firing workers.
Finally we want to address the possibility that our demand forecast is not accurate. As
mentioned earlier in our forecasting section, we noted how the demand data we were given
appeared to possibly be stabilizing. If our actual demand does begin to stabilize we would still
be able to meet demand for about a year and a half before we would have to purchase more
machines. We used the last 12 weeks of demand data to make three periods of four weeks. A
linear regression formula was calculated from the three periods and is shown below:
If our demand does indeed follow that formula, we would not need to purchase another cell until
at least period 20 or 21 (approximately a year and a half). The graph below shows the
relationship between our forecast, our production schedule, and the linear regression equation.
24
0
50000
100000
150000
200000
250000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64
Nu
mb
er
of
Qu
-Ch
ips
Period
Production vs Forecast vs Sensitivity Analysis
Linear Regression
Demand Forecast
Production Schedule
0
100000
200000
300000
400000
500000
600000
700000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64
Nu
mb
er
of
Qu
-Ch
ips
Period
Projected Inventory Levels By Period
Qu-Chips
25
Utilization of ILP
The results of the ILP were not directly used in our model of the system. Instead the results were
used as a base to start off our simulation. A problem that arose with the creation of the ILP is that
the calculation is missing two main factors. The first factor is that scrap is not considered. When
the process is run about 5% of the chips are scrapped in our simulation and they are not
accounted for in the ILP. This is not a large number, but it does cause the ILP to become less
accurate than originally expected. The second factor is that machine time averages were used,
but the distributions were disregarded. This was done to produce an estimate on how many chips
could be produced. We took into consideration the Mean Value Theorem (MVT) which states
that the more data is collected, the higher chance that the data points will converge to the mean.
In this case we thought the MVT would suffice, but of course it is still an addition to the
inaccuracy of the calculation. We also realized that we were not in control of how many chips we
are receiving to produce each week, but we decided that we would like to use this to propose an
alternative to the current state. With these problems kept in mind, we decided to start our model
with the found solution and then adjust the numbers to the best of our ability with our Arena
model in order to find the most accurate optimal solution.
Plant Layout
This section of the report covers every aspect of the facility’s layout. The sizes of the separate
departments are analyzed, as well as the concepts behind the layout of the production area.
Overall the layout of our plant was designed to minimize the amount of space utilized while
including all of the necessary components of a functioning plant.
The team began the process of physically laying out the plant by brainstorming a list of
departments. The departments we listed are as follows: Office, Engineering/IT, production,
warehouse, shipping/receiving, maintenance/tool, and lunch room. Then each department was
assigned a percentage of total plant space, which was reviewed and approved by Professor
Barnes. The table below lists all the percentages
Once the size of each cell and the number of cells necessary were determined, the team was able
to derive the square footage of the departments. Each cell is 4,843 square feet large, and our
ARENA model determined that eight cells were necessary at start up. Utilizing this information
Department Percentage
Office 10%
Engineering/IT 5%
Production 41%
Warehouse 22%
Shipping/receiving 14%
Maintenance/Tool 4%
Lunch Room 4%
Total 100%
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and some estimates for travel lanes within the process space, we were able to determine a
production area estimate for the plant. The combination of square footage estimate and
percentage of floor space was used to determine an accurate estimate for overall plant size. The
size estimates for each department are listed below
The team then determined the importance of department location within the plant, by using a
simple relationship chart and general plant layout rules. The relationship chart has a six number
priority code assignment to determine the priority of each department in relationship to another.
This technique is based off of the Manufacturing Facilities by D.R. Sule. The relationship chart
is displayed below.
Nodes Production Shipping/Recieving
Storage
Warehouse Cafeteria Office Maint Engineering/IT
Production - 4 4 1 2 3 3
Shipping/Recieving - - 3 1 2 2 2
Storage
Warehouse - - - 0 1 0 0
Cafeteria - - - - 1 0 0
Office - - - - - 1 3
Maint - - - - - - 1
Engineering/IT - - - - - - -
Each of the values in the chart represents the importance of the relationship between the two
departments. The higher the value the more important the respective departments are adjacent to
one another. The key for the relationship chart is listed below with specific values and their
corresponding meanings.
Qu-Chips-Ahoy (8 Cells)
Department sqft. % m2
Office 11481 8% 1067
Engineering/IT 8611 6% 800
Production 58841 41% 5466
Warehouse 31573 22% 2933
Shipping/receiving 20092 14% 1867
Maintanence/Tool 5741 4% 533
Lunch Room 7176 5% 667
Total 143514 100% 13333
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Nodal Layout
From this relationship chart a nodal representation was determined, in which each department is
represented by a simple box, known as a node. The lines between each node represent a
department’s importance in regards to one another. The numbers of lines between the nodes are
equal to the priority code that has been assigned by the relationship chart. This representation
allowed the team to visualize the importance of certain departments in relation to another, and
subsequently arrange the nodes so the highest priority departments adjacent to each other. The
nodal representation is displayed below.
Value Priority
4 Absolutely Necessary
3 Especially Important
2 Important
1 Ordinary
0 Unimportant
-1 Undesirable
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Grid Layout
Once the facility size estimates, relationship chart, and nodal representation were complete the
team was then able to construct a grid representation. The grid consists of twenty five squares in
a five by five pattern. Each square in the grid represents 5100 square feet of floor space. The
floor space for each department was divided by the space of one grid to determine the number
necessary number of blocks for the grid representation. The department assigned to each block is
denoted by the initials shown in both the table and the grid representation.
Department Sqft. Grids
Office (O) 11481 2
Engineering/IT (E/IT) 8611 1
Production (P) 58841 11
Warehouse (W) 31573 5
Shipping/receiving (S/R) 20092 3
Maintenance/Tool (M/T) 5741 1
Lunch Room (LR) 7176 1
Total 143514 24
1 Grid 5300 Total Space 143514
O O LR P
E/IT P P P
S/R P P P
S/R P P P
S/R W W P
W W W M/T
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Evaluation Chart
To evaluate the efficiency of the layout, the relationship chart is combined with the distance from
one department to the next. The higher the total number the less effective the layout. The number
on the right in each cell represents the importance of interdepartmental relationship, while the
number on the left signifies the distance on the grid. The relationship value is multiplied by the
distance value in each cell. All of the cells are summed up to determine the final score of the
layout. A zero associated with distance notes that the departments are directly adjacent in the
grid and therefore not charged to the final sum. The final sum for the team’s grid layout was
twenty five which is considered to be an effective design. This design achieved the best score on
the evaluation matrix by comparison other designs and their respective matrices. This particular
design achieved a twenty, where others scored twenty five and above. A copy of the old design
evaluation chart can be found in the appendix of this report.
Nodes Production Shipping/Receiving Storage
Warehouse Cafeteria Office Maint Engineering/IT Totals
Production (P) - 4 X 0 4 X 0 1 X 0 2 X 0 3 X 0 3 X 0 0
Shipping/Receiving (S/R) - - 3 X 0 1 X 3 2 X 1 2 X 0 2 X 0 5
Storage Warehouse (W) - - - 0 X 3 1 X 3 0 X 0 0 X 3 3
Cafeteria (LR) - - - - 1 X 0 0 X 6 0 X 2 0
Office (O) - - - - - 1 X 6 3 X 0 6
Maint. & Tool (M/T) - - - - - - 1 X 6 6
Engineering/IT (E/IT) - - - - - - - 20
30
Final Facility Layout
31
Production Layout
The production floor of our plant is comprised of eight cells and an Alpha Insulator staging area.
Each cell is identical to each other. The Alpha Insulator staging area is composed of twelve
Alpha Insulators directly to the left of the cells. On the right hand side of the production floor are
the manufacturing cells.
Each cell structure begins with a NUR machine house in the center, surrounded by six
Whitworth machines, with 3 machines on either side. One Rudd mounting machine is placed
opposite the NUR at the other end of the cell. The eight cells are separated into two rows of four,
and arranged so that both rows line up next to each other horizontally. When developing this
layout, we chose to make the two rows exact mirror images of each other, by flipping one entire
row, with one 5-meter wide lane in between the two rows.
The purpose of the aisle is to allow hand carts to move through and drop off/pick up material
from the Alpha insulator at the end of each row to the closest NUR in each cell. The aisle
continues to the end of the row, and then wraps around both rows and returns to the shipping and
receiving area to the left of the production floor. On the opposite end of the plant, to the right of
the production floor is the warehouse where materials and products will be stored. Above the
production floor on the opposite side of the aisle is the space for the offices and employee
cafeteria. Finally, below the production floor and opposite the transportation aisle are the last two
departments, maintenance and engineering/information technology.
Work Cell Methodology
The main reason why we decided to use the work cell approach is because it allows us to pace
the rate of work in each cell based on the bottleneck of the process. The bottleneck of the process
is the etching process, which we placed at the center of the work cell. That way it can feed the
other machines in the cell, and essentially only allow a queue to develop before the bottleneck.
We also went with the cellular model due to the large amount of automation in the process. Each
different type of machine in the cell is automated; therefore the role of the operators in the cell
will be moving the part from machine to machine. They will also have to make sure the
machines are functioning properly. In cell layout we decided that we only need three operators
per cell to perform these duties, because each is operator responsible for multiple machines in
different parts of the cell. There is one operator on the right, left, and bottom (or top depending
on where the cell is located in the plant) to ensure that production runs smoothly.
Work Cell Flow
There are essentially only three different steps of the process: etching, laminating, mounting. The
machines utilized in the facility correspond to these different process steps. As I mentioned
before, the NUR etching machines are the center of the facility work cells. In each work cell
there is one NUR machine that is surrounded by six Whitworth laminating machines. The chip
passes through one of the Alpha insulators, to the left of the manufacturing cells, and then
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proceeds into the NUR. After passing through the NUR the chip goes to one of the Whitworth
machines. Next the chip goes through one of the Beta Regulators, and then finally to the Rudd
mounting machine.
Once the chip has been mounted it is then placed in a protective container. It is then transported
via a hand carts to the shipping and receiving area. There it is inspected, properly packaged, and
prepared to ship.
Finances
Initial Investment
There are many costs that need to be accounted for when starting a business. The initial
investment that we will be asking for will cover all of these costs so that our business can begin
to run and start making a profit. The initial costs that will be covered with the investment is the
cost of the property and building, the cost of the machines, the cost to furnish the building and
property, miscellaneous supplies cost, the cost to test the machines, startup raw materials, startup
cost of utilities, initial plant renovations and redesigns, and start up tooling inventory. The initial
costs of the property and machines will be discussed later in the report and they total to
33
$7,035,700 and $45,938,649.04, respectively. Rather than creating inaccurate estimates for the
rest of the startup costs, $30,000,000 has been allocated to cover them. Below is a description of
what each of these costs would entail.
Cost to Furnish
This cost is very hard to determine. There is an endless list of items needed to furnish the plant to
make it a comfortable place for people to work. These items include computers, televisions,
lunchroom services, desks, chairs, goggles, rugs in office spaces, etc. This cost also takes into
account initial fencing and security costs, as well as any needed landscaping.
Miscellaneous Supplies Cost
There are many supplies needed for the business to run smoothly. These supplies mainly allow
workers to effectively do their jobs. There are many little costs, such as pencils, paper, mops,
goggles, and gloves, but collectively they can equate to a fairly large initial cost.
Cost to Test the Machines
This cost is simply the cost of some extra initial raw materials and tools costs. It also takes into
account the wages of the workers who will be doing the testing.
Startup Raw Material Cost
In order to start producing, we will need money to purchase raw materials. In this case, we need
to purchase the pre-fabricated chips. This is likely a very large cost. The company will need
investors to supply us with enough money to enable us to continuously buy raw materials until
we are able to purchase raw materials ourselves.
Startup Cost of Utilities
As soon as the building needs to be used, utilities will be needed. Just like raw materials, the
company can’t begin to produce without the funds to purchase utilities. We will need enough
money to pay for utility costs until we are able to fund that ourselves.
Initial Plant Renovations And Redesigns
This cost is based on our plans of purchasing a used building. Upon purchasing a used building,
it will need to be renovated and remodeled to fit our proposed plant layout and other needs. This
cost is almost impossible to predict, as it is solely based on our unknown future purchased
property.
Startup Tooling Inventory Costs
This cost is much like the utility and raw material cost, in that we need initial startup money to
be able to begin production. It is important to have enough money to have an ample tooling
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inventory so that machines can be ran and maintenanced properly. We need enough money to
last until we can fund this cost ourselves.
These costs all are associated with the amount of money needed to initially start production, and
provide enough money to get to the point of the company making enough profit to support these
costs. For our evaluation, we will provide a very rough estimate of an overall initial start up cost
to account for these unknown costs. Because of the uncertainty, we will provide a sensitivity
analysis to provide a top end value of dollars that can be spent on these costs in order to maintain
the investor’s 15% rate of return.
Salary Cost
For every employee that the plant requires, the hourly wage as well as annual wage was
generated through the United States Department of Labor. We accessed the Bureau of Labor
Statistics to find each wage. Each number is also specific to the state of Nevada where our plant
location will be. The numbers from this website are averages over the entire state so each wage
also includes wages based on seniority and skill for each job title. For example, our Upper
management executive will make $90.48 an hour, which results in making $188,210 a
year. From top to bottom, the plant will have 28 different job titles that will cover every aspect
of manufacturing the chip.
Each job that is offered in our proposal has a specific duty which is why we have delegated
numerous positions for each job title. The proposed plant is split into eight different
cells. Because of this, many job titles needed more workers. Each cell has its own computer,
mechanical, industrial, electrical and quality engineer. Also, each cell has its own supervisor and
inspector. Also, the schedule for the workers inside each cell is split into four shifts. Since there
are eight cells with four machinists in each cell, 24 machinists are needed during any given
shift. Since we have four shifts, a total of 96 machinists will need to be employed. During each
shift, security will also be needed. There will be one security guard at the gate, two inside the
security room, and one at the entrance/exit each shift. With four needed during any given shift,
16 will need to be employed. Also during each shift, the plant will need four material handlers
will be needed, one forklift operator, and two warehouse inventory operators. The plant will also
need professional staff and maintenance staff that will work regular 8 hour days, 7 days a
week. During the regular work day times, six maintenance and eight repairmen, eight janitors,
and a few executives will be needed.
One thing to note is that in our evaluation of total salary costs, we have not included sales
representatives, marketing and sales managers, or customer service representatives. We decided
not to include those positions because we made an assumption that those positions would be
better suited at one of the company’s other, more corporate oriented, locations. We do
acknowledge that these positions still do exist and have include these employees in our
estimation of yearly overhead costs.
After employing every available position being offered, the plant will need a total of 249
employees to run properly which results in an annual cost of $996,465.60. All workers while
being employed will receive free health care, benefits, and workmen’s compensation.
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Inventory Cost
In the state of Nevada, there is no inventory cost. So, the rest of the cost for inventory would
come in overhead costs, including the cost to upkeep the warehouse they are held in. So, for our
financial analysis, we will not include inventory cost since overhead an upkeep costs will already
be included elsewhere.
Property Cost
To find the property cost needed for our evaluation, we decided to purchase a used facility. We
did research on current properties on the market and came up with average costs for properties
that meet our needs. We found three properties currently for sale in Nevada and their average
building size was 177920.667 square feet, which gives enough room for our proposed layout
with room left over for expansion. The properties sit on an average of 8.83 acres of land which
also allows for additional expansion later on if needed. The price of these properties largely were
based on the age and size of the facility and the size of the plot of land. The average price we
found on facilities that met our needs was $6616666.67. So, for our evaluation, we used $7
million for our initial price of the property. From there we were able to calculate an initial real
property transfer tax. The tax resulted in an additional $35700 added on to the initial price.
Another property cost we took into account was the yearly property tax. The property is taxed at
3.2% of the property value. We used the asking price as the property value for calculations. The
property tax results in a yearly payment of $224000.
Machine Cost
As discussed in the arena simulation section, a total of 85 machines will be ordered. we propose
to order 8 NUR, 48 whitworth, 8 rudd, 12 alpha insulators, 8 beta insulators, and one packaging
machine. The machines will be paid for completely in the first period with a total price of
$37,310,052.30. This cost also includes Nevada sales tax.
Shipping and Installation Cost
On top of purchasing the machines, all 85 of the machines need to be shipped and installed. This
will bring the total cost of the machines once installed to $45,938,649.04. Unfortunately, many
trucks, drivers, and permits will be needed just to get the equipment safely to the facility. These
costs alone will be quite substantial. On top of that all of the machines must be unloaded, moved
into the plant, potentially reassembled, and then must undergo rigorous testing to make sure that
all functions are working properly before manufacturing can even begin. Because of the
elaborate nature of this process, a percentage of the total machine cost before tax was used to
reasonably calculate the shipping and install costs. For the cost estimation we have estimated that
the shipping cost would be 10% and the installation cost would be 15% of the total machine cost
before tax.
Utilities Cost
Every commercial building will have extensive utility costs, and this manufacturing plant is not
any different. The building will need to be equipped with a full HVAC system and require an
endless supply of energy. According to the United States Energy Information Administration, on
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average most commercial buildings cost $1.51 per square foot per month for energy. The
proposed plant will be 143,514 square feet. This will give a total cost of $216,706.14 per
month for utilities. When converting to cost per period, it will be a total of $200,06.44 each
period for the entire building. $1.51 per square foot is an average cost for commercial buildings
and it is a fair price given our plant.
Manufacturing Overhead Cost
Overhead costs are some of the most expensive costs any company incurs and are often the
hardest to calculate. They’re so difficult due to the vast costs that fall into this category.
Overhead costs are essentially all costs that are not a direct result of the product being
manufactured. Examples of these overhead costs would be: utilities, rent, communication
systems, taxes and insurance, environmental costs, and even machine depreciation. Some of
these costs are easily calculated with the information we have been given regarding the project,
while others are much more complicated to determine. Most companies in this situation would
most likely model a majority of their overhead costs that they cannot calculate off of similar
existing companies or from previous experience or historical data. Unfortunately we do not have
the luxury of being able to accurately do any of these. Since many of these costs will not be
known until the plant is up and running, we therefore have grouped and “black boxed” a majority
of these expenses to make it easier to estimate the total overhead cost. This “black box” method
allows us to allocate a set budget to a group of items without having to calculate the cost down to
every mop and bolt. Essentially, this method acts as a “top-down” approach to calculating
overhead. As a result, these estimates have the potential to be inaccurate but we have done as
much research as possible to ensure that they are as realistic as possible. We have also done a
sensitivity analysis to show how the overhead cost could affect the final rate of return if it has
been estimated incorrectly. Below are the black box categories that we have come up with and
have allocated 200 million dollars towards, annually.
Black Box Cost
The facility that we have proposed to be build is strictly a manufacturing facility for qu-chips.
This means that all employees that aren’t directly related to the manufacturing and operation of
the plant work at a separate headquarters office that we are assuming has already been
established by the company. Essentially, the proposed plant does not have marketing, sales,
technical and customer service, and other supervisors on site. These positions are employed at a
different location and therefore are not added into our salary costs. Though we do not have the
quantity and salaries of these support roles, their paychecks still must come from the profits of
the qu-chips. Therefore, the period cost of these external workers will be represented in the
estimated overhead cost.
To ensure that our facility is kept clean at all times that it is open; we suggest hiring 8 janitors
full time. They will be entitled to clean all the bathrooms, offices, entrance/exits, as well as the
shop floor path in between all of the cells. All of the cells will be cleaned by the machinists that
work within them to avoid confusion. Given that the proposed building is 143,514 square feet,
there will be a lot of area to cover. With over 200 employees working at the plant, it will need to
be cleaned daily, which is why eight janitors will be needed. The total price per year with 8
janitors is $217,040.
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All of the supplies needed for cleaning will be ordered at the end of every period. There will
also be an account for the janitors that can be accessed for any other cleaning supplies needed
throughout the year. The cost of these supplies will be covered under the predetermined
overhead cost of $20,000,000. If more funds are needed, the proposed plan has more funds that
can be allocated towards the overhead cost.
To ensure that our facility and surrounding areas are maintained continually, we suggest having
an on-site facilities department. Their responsibilities will include all indoor/outdoor facility
maintenance, inspection of the facility, and facility improvement projects. They will not be
responsible for any issues pertaining to the machines. Some projects that they would be involved
in would be replacing lights, installing televisions, building repairs, etc. They will handle any
job that requires maintenance whether it is a large job or a small job.
It is difficult to allot an amount of money for facility maintenance since it will change day to
day. Some jobs will cost a lot of money as well as take a lot of time to complete, while other
days only light bulbs will need to be replaced. Since facility upkeep is an unknown number,
the overhead cost will cover the upkeep. This will insure that we will always have enough
money on hand to perform any sort of maintenance, repair, or improvement needed on the entire
building at any given time.
Since we will be dealing with a multi-million dollar facility with very confidential information,
we believe it will be essential to have an on-site security department. This department will
include an office with highly versatile cameras, gated entrance with security booth, I.D. badges
with scanners, automated door locks, metal detectors upon entrance, etc. Each employee will
have to go through all security measures upon entering and exiting the building and facility.
We suggest using an in-house staff of trained security guards. By doing this, we will be able to
train the guards according to our standards and procedures. We suggest hiring a security guard
for the front gate, two in the security room to monitor the cameras, and one at the entrance/exit
for the building. This will be a total of four people, each making roughly $22,000 a year. All
other necessary items that the security guards will personally need (walkie-talkies,flashlight, etc.)
as well as items inside the security offices will be taken from the overhead cost.
To ensure that the facility is being environmentally friendly, certain steps need to be carried
out. A trash service will be needed to empty the dumpsters that are on-site. We suggest having
dumpsters for recyclables, paper, and general trash. The dumpsters will be filled up by the hired
cleaning service.
The company will ensure that all hazardous materials will be controlled and cared for in the
correct manner. All chemical waste will be disposed of properly; all facilities using water will
practice the appropriate conservation techniques (efficient toilets, capturing waste water, etc.),
managing electronic waste, and reducing air emissions.
We suggest having three garbage dumpsters on site; one for general trash, one for recyclables,
and one for hazardous wastes. The dumpsters will be emptied twice a month. Each dumpster
will cost roughly $300 per month, which will be $1000 total for all three, taken from the
predetermined overhead cost. All other costs will be covered under the predetermined overhead
cost as well.
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To ensure that all employees will be properly protected, certain measures have to be taken. We
suggest supplying protection equipment such as gloves, eye protection, ear protection, skin
protection, footwear, hard hats, etc. The cost will also include other facility safety equipment
such as fire extinguishers, gas detectors, first aid supplies, signage, employee wash stations, etc.
The price for all these items will not be very expensive to upkeep. Each month, new items will
need to be ordered but it will not be a substantial amount of money. The predetermined
overhead cost will have enough to cover this cost as well.
To ensure that the facility is technologically sound, we suggest the company is continually up to
date with their computer systems. This will include up to date software, hardware, server
communication, etc. Each cell will be given its own computer as well as each office will have a
computer. The security room will have multiple computer screens with cameras all over the
entire property to always be monitoring. There will also be all necessary attachments including
printers, scanners, fax machines, etc available whenever. We also suggest supplying most
managerial employees with a company cell phone. Under the software program that the
company will be run off of, all transferred information will be confidential.
The facility will need to continually be supplied with typical office supplies and any other
supplies needed to operate a business on a daily basis. The office areas will need to be equipped
with desks, chairs, cubicles, etc. All of these necessities will also be covered under the business
insurance plan.
The facility will need multiple insurance plans. The plant will need general liability insurance in
case of lawsuits. Each worker will need to have workmen’s compensation insurance in case of
injuries. Each worker will also need health care coverage; however, we recommend not covering
their entire family under the health insurance. Only top executives and managers should have
full health insurance as well as their families.
MACRS Analysis for Machine Depreciation
During the 1980’s, the United States introduced the Modified Accelerated Cost Recovery System
(MACRS) for depreciable assets. Since the proposed business will include roughly $37,000,000
in machines, this is very important to the company. The main goal of MACRS is to encourage
economic growth by offering tax incentives for depreciable assets, which our machines fall
under. Essentially, this system enables corporations to write off a percentage of the machines
value at the end of the tax year as a deductible.
Since most life duration of manufacturing machines last three to five years, we suggest using a
three year MACRS recovery period. By using a three year recovery period, the total purchase
price of the machine will be deducted over four years from the company’s total taxes. However,
the deductions can be spread out over five year periods or seven year periods as well which can
be found in the appendix section. The amount of money that can be deducted each year for each
machine are calculated as well in the same document for reference.
MACRS is a simple system that uses a predetermined depreciation rate each year. For the three
year recovery period plan, the first year’s rate is 33.33%. For example, the NUR machine costs
$1084809.25, after one year an amount of $216,961.85 can be deducted from the total taxes for
that given year. By the fourth year, the last amount of $62,485.01 will be deducted from the
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taxes and the entire cost of the machine will be accounted for. We then added the sales tax to
the machine prices and multiplied that number by the total number of the given machine. This
gave us the total amount of money that would be tax deductible each year for each machine.
Taxes
When starting a new business in the United States, the new business is subject to both federal
and state taxes. Federal and state taxes differ in many ways, but they both need to be payed in
order for the business to legally exist. Our proposed new location is in the state of Nevada in the
United States of America. So, therefore, the business is subject to taxes from the Nevada State
Department of Taxation as well as the United States Internal Revenue Service.
According to the Nevada Department of Taxation, the total list of taxes taken in the state include
“sales and use tax, modified business tax, cigarette tax, other tobacco products tax, liquor tax,
real property transfer tax, bank branch excise tax, lodging tax, insurance premium tax, tire tax,
and short term lessor (passenger car), live entertainment, and exhibition facilities fees” (Nevada
Department of Taxation, 2013). In this case, the business is subject to sales and use tax, modified
business tax, and real property transfer tax. A main reason for our selection of Nevada for our
location is because of the lack of business income taxes, franchise taxes, estate taxes, inventory
taxes, and taxes on corporate shares. Also, there are fees that are required when starting a new
business. The only fee that we will be subject to is the Sales Tax Permit which is a one-time $15
fee. Additionally, all businesses must pay unemployment insurance to the Nevada Department of
Employment, Training, and Rehabilitation and a property tax must be paid as well.
The sales tax is a tax on all “tangible personal property” that is transferred (Nevada Department
of Taxation, 2013). Also, the business is allowed to include the tax in the price of an item that is
to be sold. We recommend doing this in the sale of the qu-chips, as it is a normal practice in
American industry. If the company decides to do this, they must state that the state sales tax is
included on the bill of their product.
As described by the Nevada State Department of Taxation, the use tax “is imposed upon the
storage, use or other consumption… of tangible personal property”(Nevada Department of
Taxation, 2013). This tax will apply to the business on any materials we purchase outside the
state of Nevada. Basically, if the state of Nevada didn’t collect a sales tax on the purchased item,
then it is subject to use tax. However, most companies are registered to pay Nevada sales tax,
and Nevada recognizes sales tax that is paid in another state. If the tax in that state is less than
the Nevada state tax, then the rest of it has to be made up in use tax.
The sales and use tax rates are equal in Nevada. Statewide, they are both 6.85%, however local
counties can add on up to 1.25% to the tax. For the purposes of our evaluation, we will assume
that we will have to pay included sales tax on our materials to make these chips. This will cover
the need for a sales tax, and take care of the use tax as well. We will use the maximum rate of
8.10% sales tax to make sure that sales tax is well covered.
The modified business tax is a tax on a company’s paid employee wages. The tax is also on
wages with health insurance benefits deducted. It is a tiered tax, and the cutoff is $62,500. With a
company that has more than $62,500 in wages, the tax includes a $312.50 tax plus 1.17% of the
40
wages over $62,500. So, to figure out the tax, we will deduct $62,500 from our total wages, tax
1.17% of what is left, and add $312.50. The tax is due quarterly.
The tax rate for Unemployment Insurance in Nevada is 2.95% of wages, up to the wage limit. In
2013, that limit is $26,900. The wage limit has changed every year, and there doesn’t appear to
be any trend.
Property Tax
The property taxes in Nevada are different from county to county. The tax rates are anywhere
from 1.7767% to 3.66%, but most are around 3.2%. So, for the purposes of our project, we will
use a 3.2% property tax. The property tax includes real property as well as personal
property. The building will be our real property which costs a total of $7,035,700. Our personal
property will be everything inside the proposed manufacturing plant. This will cost a total of
$37,310,052.30. After adding both property costs together, that gave us our taxable property for
the first year. However, using the Modified Accelerated Cost Recovery System, the tax will be
decreased each year by the depreciated amount from the cost of the machines. Essentially in
year 0, our total property cost was $44,310,052.30 and after our machine depreciation, we
deducted a total of $12,435,440.43 so after year 1 our property tax was $31,874,611.87. Since a
three year machine depreciation factor was used, after year 4 the plant was only paying property
taxes on the value of the building, which is $7,035,000.
Federal Taxes
The federal taxes that will apply to the business are employment taxes and excise taxes.
Employment taxes include the federal income tax, social security and Medicare tax, and the
Federal Unemployment tax. These taxes, with the exception of the Federal Unemployment tax
and the business’ federal income tax, will be withheld from the employee’s wages. The Federal
Unemployment tax will be paid by the business. Also, the Federal Unemployment tax, along
with the state unemployment taxes are used to pay unemployment compensation. The Federal
Unemployment tax is 6% on the first $7,000 paid to each employee. This tax can be credited
again up to 5.4% based on state unemployment taxes. The federal income tax is 34% of the
yearly income on all businesses with over $18 million in taxable income per year. Our proposed
business will make over $18 million in taxable income each year, so we will use the 34% tax
rate.
Health Insurance and Benefits
Most companies offer benefits to their full time employees. For our evaluation, we will
recommend giving health insurance and short and long term disability. According to the Bureau
of Labor Statistics, manufacturing companies pay, on average, $3.04 per hour on health
insurance and $.15 per hour on disability for their employees receiving benefits (United States
Department of Labor, 2009). We will use these average rates for our evaluation.
41
Inflation
Inflation is the rate at which the cost of goods and services increases over time. Every year
inflation is present and in our multiple-year cost analysis inflation must be accounted for.
Essentially, inflation causes every cost the company incurs over every period to increase; from
utility costs to salary costs. Even profits on the qu-chips themselves are subject to inflation
because we made the assumption that the net profit is a fixed number. Therefore, since the
production cost will inflate and the profit must stay the same, the total revenue of the chip must
increase at the rate of inflation. The inflation rate we used for all cost calculations was 1.76%
annually. We determined this by taking the average of the recent inflation rates between June
2012 to April 2013. This inflation rate was then modified to ~.14% to account for inflation every
period, rather than just yearly.
Qu-Chip Profit
The only positive cash flow this plant will receive will be directly from the profit made on
selling qu-chips. We have determined that the profit per qu-chip will be 20% of the $384.56
revenue, or $76.91. To calculate the total profit each period we created an excel file with all of
the qu-chip data.
To calculate how many chips will be on hand each period, the total failures per period
(seen below) must be first removed from the amount made per period (121033/period) to get an
amount on hand. The demand is then taken from this amount on hand and all additional chips
will go into inventory.
In months that do not have enough on hand to fulfill demand, chips will be taken out of
inventory. Additionally, when there is not enough chips made in a particular period and there are
not enough in inventory either, the demand for that period will be fulfilled as best as it can and
the inventory will go negative. This negative inventory will allow for later periods to fulfill the
missed demand of prior periods. An example of this negative inventory can be seen in period 64.
Also, we will not use any deductions when demand was not met for a particular week.
Scrap costs are calculated by taking the number of failed chips at the alpha insulator,
laminating machine, beta insulator, and inspection and multiplying them by the amount lost at
each station (5%, 20%, 60%, and 80%, respectively). This cost is $218,295.48 per period before
inflation. The final net profit of the qu-chips per period is the difference between the profit
received from the sale of the chip and the scrap cost per period. The net profit over a five year
period, before inflation, will be $516,269,369.48.
Rate Of Return
A few different methods were used in calculating the rate of return. An inflated internal rate of
return, an actual internal rate of return, and a modified rate of return. All rate of return
calculations can be found in the appendices and the attached excel document under the “Rate of
42
Return” tab. The inflated internal rate of return was our basis to see where exactly we would be
at in regards to the minimum acceptable rate of return, or MARR. This was calculated simply by
using an internal rate of return function in excel and all inflated yearly losses and gains. Since the
inflated rate of return is, by definition, an inflated number, the actual internal rate of return
removes inflation. It uses the following equation in order to do this:
Actual ROR=((1+Inflated ROR)/(1+Inflation Rate))-1
Though useful, the issues with both of these rates are that they have the potential to have
multiple outputs for the ROR and that they do not properly calculate reinvestment rates. Due to
this, the calculated ROR is a very optimistic number and not too accurate. To take all of this into
account a modified rate of return was used to calculate a more realistic external rate of return.
This modified rate of return, or MIRR, uses both the rate at which extra funds will be reinvested
and the rate at which funds are borrowed when there is negative cash flow. These rates are called
the investment rate and the borrowing rate, respectively. According to economists, it is safe to set
the investment rate to the MARR and the borrowing rate to the weighted average cost of capital,
or WACC. When calculating the MIRR using the excel function, we used 15% for the
investment rate and found out that, due to limited periods where cash flow was negative, the
borrowing rate could be any reasonable percentage and not change the MIRR output. Therefore,
we used a borrowing rate of 10% because the MARR is commonly greater than the WACC.
Below is the actual excel function used to calculate the MIRR.
=MIRR(Annual values, borrowing rate, investment rate)
Finance Conclusion and Sensitivity Analysis
In order to give a rate of return, much of the calculations depend on the unknown initial startup
costs and additional yearly overhead costs. It is our best estimate that we haven’t accounted for
about $30 million in startup costs, as well as $20 million annually in unpredictable overhead
costs. We used these costs in our final evaluation to find that our proposal will result in a 28.07%
rate of return for its investors. To be very precise, the total investment that we would ask for
from the investors is $82,974,349.04. Our final ROR (MIRR) calculation can be seen below:
Since so much of our costs come from unknown or unpredictable start up and overhead costs, we
decided to offer a sensitivity analysis on our final conclusion. Our sensitivity analysis shows that,
43
keeping the initial additional unknown startup cost of $30 million, we could afford to spend
$36.9 million annually in additional unknown or unpredictable overhead. The analysis can be
seen below.
Likewise, if we keep our initial estimate of $20 million in unknown or unpredictable annual
overhead, then we can afford to spend up to $89 million in additional unknown start up costs.
Both of these sensitivity analyses result in a 15% rate of return for our investors. So, even if we
are considerably off on our estimate of the unknown costs, there is a large probability that the
investors will still get their 15% rate of return. The analysis can be seen below.
A third sensitivity analysis that we decided to run was to decrease our profits. Decreasing our
profits takes into account possible errors on almost anything in our calculations. For example, it
takes into account errors on taxes, forecast, and utility costs, and more. For the analysis, we
examined what percentage our profits could be incorrect, and still meet our investor’s desired
15% rate of return. So, through trial and error, we found that a decrease of 40% in our profits
results in a ROR (MIRR) of about 15%. So, our profits could be 40% lower, and we can still
meet our MARR.
44
45
Simulation
The Model
The framework of the model utilizes Arena’s sequence data module included in the project bar’s
advanced transfer options. Station and route modules are defined at the beginning and end of
entity transfers between cells. Each route module follows the user-defined sequential order of
steps. Arena automatically defines the attributes: Entity.Station (M), Entity.Sequence (NS) and
Entity.JobStep (IS). These attributes record statistics of the Qu-Chip entity’s current station
location, the sequence order and position within the sequence, respectfully. Transfer times are
included in the route modules by the user-defined “Transfer Times.” The model serves two
purposes: meet the observed demand data requirements given and provide analysis for our
proposed alternative solution. The following text will discuss the model’s design for meeting the
observed demand data and the following section will go through changes made for the approved
proposal.
To initiate the sequence, Qu-
Chip entities are assigned a part
index upon arrival that increases
as it moves downstream from its
current route module to its next
station module. The index
matches the step number of its
current position in the sequence
46
data module via attribute ‘Part Sequence (Part Index)’ in this assign module. Qu-Chip entities
now enter the ‘Order Release Station’ and are transferred from the Qu-Chip Arrival Cell to the
Alpha Insulation Cell by the ‘Route to Holding Cell’ route
module.
Entities immediately enter a decision module to determine if
an Alpha Insulator is available, else they are scrapped and
labeled in the ‘Accumulated Scrap’ set that is defined at
each scrap record module to keep track of where each entity,
if scrapped, is disposed. Entities that seize an Alpha
Insulator are routed, while seized, to the etching station.
This allows our model to accurately represent the
requirement for chips to instantaneously enter etching
without leaving insulation. The etching cell is the most
complex concept of our model and will be a main focus
point of this discussion.
Upon arrival to the etching cell, the entities remain seized in a hold module under the ‘Wait for
Signal’ condition to be satisfied from the ‘Release Signal’ module downstream. Arena’s signal
module requires an entity present to broadcast a value to hold modules waiting for its signal. At
this point, the question arises; how can an entity pass through a condition that requires a signal
from a downstream module? I would not mind hearing the answer to this question from
Rockwell themselves. Nonetheless, this riddle was cracked by implementing a second create
module releasing an entity at time 0.0 labeled ‘Create Release Signal Entity,’ arriving with the
same distribution as the Qu-Chip entities. It immediately enters a hold module to scan for the
condition ‘NQ (Alpha Insulation Capacity) >= 1 && NQ (Etching Process.Queue) == 0’ to
prevent two errors: a cease of flow from
alpha insulation due to missing a signal
release because another entity would not be
able to reach the signal module and a
premature release of an entity causing a
forbidden queue to form at the etching
process. Upon meeting this condition, the
new entity is assigned the attribute ‘(MR
(Etching Machines) - NR (Etching
Machines))’ to be used as the optimal limit
value of release from alpha insulation. The
signal module then emits a value of ‘1’
which matches that of the ‘Hold in Alpha Insulation’ module and the etching machines are then
immediately seized. While this is happening, the new entities are being disposed of as they fail
the decision module condition if ‘Entity Type’ named ‘QuChip.’ Qu-Chip entities are now
47
specified a TNOW attribute to initialize the time interval from etching machine release to the
laminating station for yet another scrap condition.
Once routed to the Laminating
Cell, Qu-Chips seize a laminating
machine and enter the decision
module for Tw calculation. Under
the condition ‘TNOW – Initiate Tw
<= 0.04583’ entities over this time
requirement are recorded in the
‘Accumulated Scrap’ set and
release their seized resource for
upstream entities. Entities under
the time requirement are delayed
by the laminating process service
time and then transfer to the Beta Regulation Cell.
The entities must be able to immediately seize a beta regulator. If the entity can seize a resource,
it holds, with its seized resourced, for an available mounting machine. When beta regulator
resources are at capacity, the Qu-Chip is recorded in the ‘Accumulated Scrap’ set and is
disposed. This requirement is very similar to the entities trip through the Alpha Insulators; a
service time is required at neither the alpha insulators nor beta regulators but they must seize
these resources prior to moving along their sequential order.
When the chips enter the Mounting Cell, 99.6% enter the primary mounting process. The
remaining Qu-Chips are serviced by the secondary process which takes substantially longer than
the primary option.
At this point, the Qu-Chip entity is processed
and transferred to the Inspection Cell. The sequential steps followed by the majority of Qu-
Chips are almost complete as they now route from the manufacturing cells to inspection.
48
Inspection
The penultimate step of the Qu-Chip manufacturing process is inspection. Each Qu-Chip must
pass this final test, before being packaged and shipped out. Inspection consists of an ion-trap test,
which validates that each Qu-Chip can tolerate voltage within a specified range. The given
voltage range is:
In order to calculate the voltage of each Qu-Chip, we will use a modified version of Ohm’s Law:
The variable represents current, and is defined in amps. In this case, it is a discrete value, fixed
at 0.01 amps. The variable represents resistance, which is measured in ohms. As opposed to
current, resistance is a continuous value, and based on a normal distribution with two parameters.
The mean, , is 4 ohms while the standard deviation, , is 0.10 ohms. Below is a graph of the
probability density function (pdf) corresponding to :
Finally, the variable is defined by a
three parameter Weibull distribution.
Typically, the Weibull distribution
consists of a shape parameter, , and
scale parameter, . However, with a
three parameter Weibull, a shift
parameter, , is also considered. The
shift parameter, also referred to as
location, slides the distribution across
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
3.7
3.7
3
3.7
6
3.7
9
3.8
2
3.8
5
3.8
8
3.9
1
3.9
4
3.9
7 4
4.0
3
4.0
6
4.0
9
4.1
2
4.1
5
4.1
8
4.2
1
4.2
4
4.2
7
4.3
Normal PDF (μ=4,σ=.1)
0
5
10
15
20
25
30
35
2.9
8
2.9
91
3.0
02
3.0
13
3.0
24
3.0
35
3.0
46
3.0
57
3.0
68
3.0
79
3.0
9
3.1
01
3.1
12
3.1
23
3.1
34
3.1
45
3.1
56
3.1
67
3.1
78
Weibull PDF (α=271,β=3.141,Nu=0)
49
the x-axis. Shape was set at 271, and scale was given as 3.141. The shift parameter is vital to the
inspection process. When meeting with the client, a value for this particular parameter was not
given, and as a team we were required to calculate it in order to minimize failure. In order to
calculate this value, we began by graphing the probability density function of the two parameter
Weibull distribution, which can be seen in this graph.
After examining the pdf, the mean of the distribution was found to be 3.13435. When plugging
in this value as , ohms law outputs a voltage value of , which is clearly outside the
range of the voltage tolerance. This initial calculation illustrated the necessity of using a three
parameter Weibull distribution. In order to calculate a proper shift value, we needed to find the
mean of the voltage range, which came out to be 28.84495 volts. With this information, we
calculated a corresponding value to give us the mean voltage, when the normal distribution
gave its mean value of 4. This value was discovered to be -0.429. Therefore, since the two
parameter Wiebull produced a mean of 3.13435, then the shift parameter, Nu, needs to be set at
-3.56335.
In order to test this value, we turned to Arena to simulate a rudimentary inspection process. The
validation was not successful, however. We determined that because of the different shape of the
Weibull distribution, and the fact that the final voltage formula also depends on variable defined
by a normal distribution, the most desirable value is actually not the mean. When testing the Nu
value at -3.56335, it provided a very good starting point to find the optimal value we needed. So,
we proceeded to test for the optimal Nu value by trial and error. The final setup that we had in
Arena is showed in the picture below:
The Qu-Chips start in the
Create module (Create 1),
which was originally set to
run 1000 chips through our
process. From there, the
chips were assigned a
voltage. This was especially
difficult because Arena does
not allow the user to input
an expression that raises a
value to the power of a
distribution. However, it
does allow raising e to the
power of a distribution. So,
we had to change our
equation from to . As shown in the picture above, the Assign
Voltage module assigns a voltage variable with the new equation. So, once a voltage is assigned,
50
the Qu-Chip goes through a Decide module (Pass Inspection?) which separates the Qu-Chips
into 3 different Dispose modules; Failed High, Failed Low, and Passed. As their name suggests,
the Qu-Chip goes to the Failed High module if their assigned voltage is higher than the 34.8317
volt spec limit, to the Failed Low module if their assigned voltage is lower than the 22.8582 volt
spec limit, and to the Passed module if their assigned voltage is within the spec range.
Our trial and error session was originally set up to run 1000 Qu-Chips through the process,
starting with the mean Nu value of -3.56335. Using 1000 chips, we were able to get a rough
estimate of what the optimal value would be. Our first run resulted in 22 failures in the Arena
model, all in the Failed High module. To check to see if our model was set up correctly, we ran
the same test using functions and equations in Excel. Part of our Excel worksheet can be seen
below:
The worksheet confirmed that our
Arena model was set up correctly
because it resulted in anywhere from
15 to 25 failures, all in the Failed
High column. These results were
similar to the Arena results. So we
went ahead and did trial and error
tests for some values to narrow
down our search. We came up with
an optimal value of -3.548, but we
knew that the true optimal value
could be more precise. From there,
we were able to get more precise
values using trials with 10000 Qu-
chips. After completing the trials for
10000 chips, we determined that we
needed to have even more precise
data. So, our final tests were run
using 30000 chip trials. The specific
data from our trial and error tests
can be seen in the excel chart below.
Nu Failed High Failed Low Total
-3.546 28 25 53
-3.547 33 17 50
-3.548 34 10 44
-3.549 38 5 43
-3.55 45 4 49
-3.5491 38 5 43
-3.5492 38 5 43
-3.5493 38 5 43
-3.5494 38 5 43
-3.5495 40 5 45
-3.5496 41 5 46
Arena Trial and Error
10000 Chips
Nu Failed High Failed Low Total
-3.56335 22 0 22
-3.56 17 0 17
-3.55 5 1 6
-3.548 3 1 4
-3.545 1 4 5
-3.547 3 2 5
-3.546 2 3 5
Arena Trial and Error
1000 Chips
Weibull Normal Voltage Failed High Failed Low
-0.4243 3.954629 27.90972226 pass pass
-0.4747 3.977392 35.39264441 fail pass
-0.4354 3.973231 29.50923867 pass pass
-0.4085 3.894098 25.55076195 pass pass
-0.4275 3.847218 27.54791339 pass pass
-0.4172 3.960828 27.05276146 pass pass
-0.4333 3.801039 27.95332047 pass pass
-0.4184 4.089887 28.08093501 pass pass
-0.4184 3.87 26.58118479 pass pass
-0.4328 3.905498 28.65491277 pass pass
-0.4197 4.05276 27.99390661 pass pass
-0.4426 3.834807 29.44530781 pass pass
-0.416 4.01238 27.25144618 pass pass
-0.4145 3.956918 26.68647382 pass pass
-0.4336 3.922132 28.89066642 pass pass
-0.422 3.851726 26.89459524 pass pass
-0.4259 3.916151 27.84542058 pass pass
-0.4467 4.144069 32.41389859 pass pass
-0.4129 3.811775 25.52531892 pass pass
Nu=-3.56335
51
As you can see from the Excel charts, our final
optimal value was Nu= -3.54875. It resulted in
the same amount of failures (150) per 30000 as
two other Nu values, but outperformed them both
when comparing values for 10000 trials. Our
optimal Nu value resulted in 41 failures per
10000 chips, which is a success rate of 99.59%.
We found our optimal Nu value to be -
3.54875
using the Arena results below:
The screenshot to the right of the page shows the
inspection cell as it is in our model. Unlike other
decision modules in our simulation design, entities
that fail here meet the N-way condition by
exceeding either the low or high voltage tolerance
requirement. Instead of simply recorded these
failures alike in the ‘Accumulated Scrap’ set,
entities are kept apart for practical purpose. It is
designed in this manner for our record of
cumulative failures on both ends of the tolerances
in the case one failure heavily outweighs its
counterpart, such a trend can be recognized and
addressed accordingly. QuChips within the
acceptable voltage range are now routed to the final
stage of their production cycle.
These entities enter our highly-automated
packaging cell and make a quick transfer to
shipping. This concludes the model discussion
Nu Failed High Failed Low Total
-3.5492 133 22 155
-3.549 131 22 153
-3.5491 132 22 154
-3.5489 131 22 153
-3.5488 127 23 150
-3.5487 124 26 150
-3.5486 124 27 151
-3.5485 121 31 152
-3.54875 127 23 150
30000 Chips
Arena Trial and Error
52
specific to the given demand data. Following this review is a look at our simulation’s results
shadowed with sensitivity analysis carried out in Arena’s OptQuest and Process Analyzer.
Meeting the Historical Data
Prior to addressing our forecasted demand, we focused on meeting the 2 year historical data as
accurately as possible. After choosing the proper resources as well as their quantities, the next
step was to execute an Arena simulation to illustrate production, and ensure that the demand can
be met. In order to show this, we chose to simulate a week of low demand, average demand, and
high demand. This allowed us to examine how closely we could meet these demand levels,
adhering to their corresponding inter-arrival rates. The inter-arrival rates are defined by
probabilistic expressions, discovered through using the aforementioned ExpertFit tool. Each
demand was simulated with an 8 cell setup, which includes 8 etching machines, 48 laminating
machines, and 8 mounting machines. Also included are 8 Alpha Insulators, and 8 Beta
Regulators. Analysis related to the forecasted demand, which will be discussed later, will advise
the use of 10 Alpha Insulators. However the periods of production based on the historical data
met the demand better with 8 Alpha Insulators. This conclusion was reached by comparing the
simulation outputs of each demand level, with first 8, and then 10 Alpha Insulators. The
conclusion to go with 8 was obvious given the results
It is also important to note, that we found it impossible to exactly meet the demand via
simulation. Since pre-fabricated Qu-Chips arrive simultaneously as orders pour in, we are
constrained by the demand count. And due to inspection, which has a failure rate of
approximately 0.5%, we cannot ship an amount of Qu-Chips equivalent to our demand count. So
for example, if 200 orders are placed, and 200 Qu-Chips are pushed to our manufacturing
process, the most we would be able to ship is roughly 199 Qu-Chips. This concept will become
more evident as we examine each level of demand.
We identified week 85 as the lowest demand week. The demand
in this week is 9180, and the inter-arrival rate was defined by a
Gamma distribution. The simulation concluded in 166.056 hrs,
slightly shorter than a full week (168 hours). This is simply due
to the probabilistic nature of the inter-arrival times, and the
terminating condition which was previously discussed. Out of
the 9180 Qu-Chips ordered and process, 9139 were shipped.
There were no fails at any point outside of the inspection
process, where 41 failed. Due to the constraints placed on the
historical data, and the inability to simply run more chips
through the process, it is impossible to perfectly match supply
with demand. Nonetheless we can successfully ship 99.55% of
the demanded Qu-Chips.
Category Units
Demand 9180
Fail to seize Alpha 0
Fail to seize Beta 0
Fail at Tw 0
Fail - High Tolerance 29
Fail - Low Tolerance 12
Total Fails 41
Qu-Chip Cycle Time .2474 hrs
Shipment Count 9139
Time Elapsed 166.0056 hrs
Low Demand
53
Week 91 was rated an average demand week. During this
week, the demand count stood at 17801 Qu-Chips. A Gamma
distribution was used to define the inter-arrival rate in this
particular week. The simulation took 167.8316 hours to
complete. Of the 17801 Qu-Chips ordered, the manufacturing
process was able to ship out 17699 units. Hence we were able
to meet 99.43% of the demand. The discrepancy is again most
attributable to the inspection step, where 88 failed due to
exceeding the high tolerance and 12 failed low due to falling
under the low limit. Unlike the low demand week, the average
demand period resulted in 2 failures caused by a lack of Alpha
Insulators available upon their arrival. Qu-Chip cycle time for
an average demand week came in at .2473 hours, almost
identical to that of the low demand week.
The demand peaked in week 105. The demand count in this period spiked to 32877 Qu-Chips,
thus presenting the biggest challenge in terms of matching the supply with demand. This also
meant that week 105 experienced the quickest inter-arrival rate. Again, the rate was modeled
with the probabilistic expression Gamma distribution. With the 8
cell setup, the manufacturing system was able to ship 32234 Qu-
Chips. In this scenario, 434 Qu-Chips failed because there was
no Alpha Insulator available when pushed into the system. As
with all three demand levels, no Qu-Chips failed at the Beta
Regulator step, most likely because of the zero service time
associated with the Beta Regulator step, and the speed with at
which Qu-Chips pass through mounting. With the increased
demand, this scenario does result in 37 failures due to a queue in
front of the laminating process. In terms of inspection, 150 chips
failed high and 22 failed low. Adding these failures together,
only 643 of 32877 Qu-Chips failed. Thus 98.04% of the demand
was met. The simulation completed in 167.4656 hours and the
cycle time for a Qu-Chip fell at .2504 hours per chip, slightly higher than the previous two
demands.
When comparing each of the three demand levels, some telling statistics emerge. As a
confirmation of the Arena model, there exist no queues prior to Alpha Insulation, etching, or
mounting. If there were any queues in front of these processes, then those Qu-Chips would be
immediately scrapped. Through the low and average demand levels, there is actually no waiting
time in front of any of the processes. In the high demand week, there does exist some waiting
time in front of laminating, which is allowed, yet constrained by the given tw value. In the case of
historical high demand, 37 chips fail due to tw, which is reflected in the output results. The
maximum waiting time in front of the laminating machines reached 0.068688 hours, exceeding
tw. Therefore, logically speaking, it makes sense that some failure occurred at that point.
Category Units
Demand 17801
Fail to seize Alpha 2
Fail to seize Beta 0
Fail at Tw 0
Fail - High Tolerance 88
Fail - Low Tolerance 12
Total Fails 102
Qu-Chip Cycle Time .2473 hrs
Shipment Count 17699
Time Elapsed 167.8316 hrs
Avg. Demand
Category Units
Demand 32877
Fail to seize Alpha 434
Fail to seize Beta 0
Fail at Tw 37
Fail - High Tolerance 150
Fail - Low Tolerance 22
Total Fails 643
Qu-Chip Cycle Time .2504 hrs
Shipment Count 32234
Time Elapsed 167.4656 hrs
High Demand
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Utilization provides insightful, yet largely predictable metrics. When looking at all three
scenarios, the etching machine utilization increases from .2226, to .4431, and ultimately to .7801.
However, the maximum utilization in all three periods reached 1, and all 8 are utilized at least
once in each demand period. Laminating instantaneous utilization increases in a similar pattern,
starting at .2323 in the low demand week, and moving to 0.811 in the high demand period.
However the maximum units busy is not consistent over the periods. For low demand, only 25 of
the 48 laminating machines are used at least once. That number jumps to 44 in the average
demand week, and finally reaches 48 in the week of high demand. When examining the Beta
Regulators and mounting machine usage, it is clear that at no point is all of their scheduled
capacity consumed. Even in the high demand period, only 6 of 8 mounting machines are used,
and 1 of 8 Beta Regulators used. All 8 of each would still be required due to the cell layout our
group is utilizing. See the below tables for further output metrics:
Alternate Solution Proposal / Fitting Demand Forecast
Modeling a real-life
simulation under these
requirements quickly proved
troublesome. Although the
given conditions stray far
from commonality, staying
true to the requirements was
our primary goal. Alpha
insulation requires immediate
action upon Qu-Chip arrivals
and varying interarrival times
make any possibility of
control futile. As each
arriving entity equals one unit of demand, a model must not only keep up with order arrivals, but
also process entities without failure. When considering these obstacles, our model’s results left
Average Maximum Average Maximum Average Maximum
Category Value Value Category Value Value Category Value Value
Alpha Insulation 0.01102282 0.75 Alpha Insulation 0.02851647 1 Alpha Insulation 0.1729 1
Etching Machine 0.2226 1 Etching Machine 0.4318 1 Etching Machine 0.7801 1
Laminating Machine 0.2323 0.5208 Laminating Machine 0.4431 0.9167 Laminating Machine 0.811 1
Beta Regulator 0 0.125 Beta Regulator 0 0.125 Beta Regulator 0 0.125
Mounting Machines 0.02145445 0.375 Mounting Machines 0.03994529 0.625 Mounting Machines 0.072252002 0.75
Average Maximum Average Maximum Average Maximum
Category Units Units Category Units Units Category Value Value
Alpha Insulation 0.08818253 6 Alpha Insulation 0.2281 8 Alpha Insulation 1.383 8
Etching Machine 1.7808 8 Etching Machine 3.4545 8 Etching Machine 6.2411 8
Laminating Machine 11.1509 25 Laminating Machine 21.2691 44 Laminating Machine 38.9285 48
Beta Regulator 0 1 Beta Regulator 0 1 Beta Regulator 0 1
Mounting Machines 0.1716 3 Mounting Machines 0.3196 5 Mounting Machines 0.578 6
Low Demand - # Busy Average Demand - # Busy High Demand - # Busy
Low Demand - Instantaneous Utilization Average Demand - Instantaneous Utilization High Demand - Instantaneous Utilization
55
us optimistic but we knew it needed improvement. The first step to improvement relied on
obtaining control of order arrivals. When our supplier accepted our agreement to define an
arrival rate, a proposal was born. This sign-off allowed us to implement a material-ordering lead
time as quick as Just-In-Time. With the added flexibility, modifications were made to our
simulation model to fit the LP parameters and forecasted demand with a constant entity release
scheduled across demand periods. In place of data distributions fit to historical demand, our
create modules’ time between arrivals parameters are now the user-defined variable ‘Proposed
Arrival Time.’ This allowed us to utilize Arena’s OptQuest tool to optimize an arrival time to an
objective function under the following constraints:
56
Among the feasible solutions, these parameters yield the most optimal results for our model.
Explanation of the objective function is available in the image taken from OptQuest’s objective
function builder. The first constraint is in place to keep the new arrival time expression a real
number and the second requires that throughput, the ‘demand count’ record module, be equal to
or greater than 120,240. This value is our uniform demand requirement for periods 1-62 of our
five-year forecast. With our ‘Proposed Arrival Time’ attribute and control solutions adjusted
according to OptQuest’s recommendation, the results are seen here. While entities arrive in a
uniform rate of one every 0.30 minutes, our manufacturing line is capable of shipping processed
Qu-Chips to meet our 28-day demand with only 793 entities scrapped.
Though the proposed model’s results are exciting, further sensitivity analysis is required to verify
the outcomes. Since our proposal’s principal element of improvement relies on the regulation of
material ordering, we exploited this control in our analysis. Using Arena’s Process Analyzer,
behavioral inconsistencies amongst scenarios following historical arrival distributions and
uniform rates are observed.
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The scenarios under comparison represent the four modified simulation models discussed in this
report. Differing only by their create module’s ‘time between arrivals’ expression, the results
yield undeniable incongruities. Scenario three stands out for producing the highest Qu-Chip
throughput, but its accumulated scrap is 2,665. Observed uptrends of scrap are sometimes
compromised for throughput, but is this one of those situations? Our proposed alternative offers
1,872 fewer scrapped Qu-Chip entities, however, most of scenario three’s failures are observed
at the two lowest-costing
failure points on the
manufacturing line. For
supplementary analysis, the
third scenario’s outputs
shown here represent average
user-specified responses after
five replications in Arena
with the same parameter
measures performed above.
Please note that the ‘Demand
Count’ is the same response as ‘QuChip Arrivals.’ While this value proves consistent with only
17 additional entity arrivals, the same cannot be said for the shipment count. When the model
replicates for a longer period of time it cannot keep up with the discontinuity of the observed
interarrival data distribution fit. When the arrival distribution encounters numerous surges of
arrivals pertaining to their assigned values, the manufacturing line loses fluidity and enormous
queues form at time-dependent intervals as is the case for the increased Tw scrap and failures at
alpha insulation. When run for a short time, the line can keep up with small spikes of demand but
it cannot be relied on for everyday production. Again, scenario three has the most volatile
probability of arrival surges because its rate of arrival was fit during our most demanding
observed week but the average and low demand fits of the first two scenarios will not offer the
throughput required during our forecast period.
Additional Sensitivity Analysis
The original linear program called for a uniform production of 120,240 QuChips in periods 1-62
of our forecasted demand with the use of six sets of manufacturing cells (6 Alpha Insulators, 6
Etching Machines, 36 Laminating Machines, etc…). An order point at week 63 increased this
figure to eight cells to meet a demand of 160,320 uniformly-produced QuChips per period.
Alpha insulators are located outside the manufacturing cells for entities to seize them upon
arrival, and because of the cost advantage of adding additional insulators to our order we decided
to purchase 12.
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Limitations present in the real-world such as transfer times, machine failures, scrapped entities
and multiple service distributions are accounted for in our simulation model. It is unrealistic to
assume our model identically matches our real-world facility but it is extremely thorough for the
time frame given and certainly covers more of these issues than the linear program can. After
making adjustments to the LP to include service times for a few of these issues, we came to the
mutual conclusion that eight cells were required at the start of week one. The comparison above
was run with the same bounds on
resource controls as this image shown
but the objective function was set to
minimize accumulated scrap. In this
scenario, 25 simulations were executed
for a varying rate of three to six
replications a peace with the objective
function to maximize shipment count
(throughput). As expected, OptQuest
recommends a quicker entity arrival rate
and one additional alpha insulator
capacity.
One additional work cell yields 185,211 QuChips per period. Notice a need for additional alpha
insulation.
59
With two additional cells our manufacturing line can produce 202,703 chips per period.
Failure rates can easily be defined to a resource in the advanced process data module. The sensitivity
analysis will use a normal distribution, starting with a mean uptime of 166 hours with a std. deviation of 2
hours. Resource down times will be set for 2 hours down time with a std. deviation of 1 hour. These
numbers are used because they cover failure rates around the clock on a 24-hour manufacturing facility.
In the scenario that numerous resources are assigned to the same failure expression, Arena will allocate
the downtime to all resources assigned that expression in the case it experiences a failure. This is not
ideal for this system which is why the five resource set names are observed by themselves.
A total of three scenarios will be run from our optimal model: NORM(166,2) , (166,1) , No Failure and
downtimes NORM(2,1) , (1, 0.5), No Failure.
NORM (166, 2) uptime with NORM (2, 1) downtimes.
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NORM (166, 1) uptime with NORM (1, 0.5) downtimes.
The expected results of this performance measure should indicate a steady drop in throughput as results
are read from “NO FAILURES SCHEDULED” “NORM (166, 1) uptime with NORM (1, 0.5)
downtimes” ” NORM (166, 2) uptime with NORM (2, 1) downtimes.” Run conditions are set to five
replications per scenario.
As anticipated, scheduled failures dramatically skewed the results of these scenarios. Since our machines
are considered, as far as we believe, to be a top-of-the-line product it seems unrealistic to believe any
given machine is expected to fail once a day. Nonetheless, running a test like this drives home the
importance of each and every machines role in keeping the production flow optimal.
Sustainability
Strategy and Management Approach
At the forefront of a revolutionary process in computer chip manufacturing we understand that
corporate responsibility and business must go hand in hand. The corporate objective should be
for continuous improvement to balance care of the planet, care for people, and profit as well as
education and inspiration of the next generation. It is imperative to create long term business
values and insist that transparency an essential part of how the operation is run. Transparency
holds the company accountable and encourages two-way dialogue with employees which
maintain our commitment to ethics. We are basing our business model off Intel Corporation’s
plan. Intel Corporation is the world’s largest semiconductor chip manufacturer. Their
sustainability motto is, “Focus on responsible business practices helps us mitigate risks, reduce
costs, protect brand value, and identify new market opportunities.” (Intel, 2010)
All the people that are associated with the business from the suppliers of the raw materials for
the product, the manufacturing facilities that produce the product, and the customers that buy the
product should be engaged in the sustainability planning of the company as a whole.
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Employees can add to the continuous improvement of all aspects of the business through the
process of an integrated system of open-door policies that give the employees access to
management at all levels. In turn this provides a direct means for creativity and improvement.
Employee surveys, weekly or daily feedback tools to keep the employees informed of what is
happening within the facility, and quarterly meetings for all employees and management that
provide question and answer sessions can result in exellent communication within all levels of
the organization. Utilizing these practiesmakes it easy to track performance and identify key
areas for improvement.
Facility visits or conferences with all the suppliers can be used in order to establish clear
expectations and improve efficiency throughout the supply chain. Risks can be more openly
identified and solutions determined. Participation in the Electronic Industry Citizenship
Coalition (EICC) to hold suppliers to a set of standards is also highly recommended.
Customers can be included to provide valuable insight into the quality and services provided by
the company. A third party Customer Feedback Program can be implemented to obtain the
surveyed feedback. It turn it can analyze the areas that are performing at a high level and those
that need improvement. This is an excellent method of to determine whether or not the quality
control methods of the company need to be addressed.
Communities can be involved as well. Community advisory panels could be eatablished
established in order to get direct feedback on how the company is viewed by the public. A few
other options to addess community concerns are two way forums, need assessments, and
perception surveys. Company employees should be placed on community non-profit boards and
commisions in order to maintain extensive working relationships with local educators and
institutions. All these measures are designed to establish good working relations between the
company and the community.
Involvement with local government policy and legislation efforts aim to foster credible
trustworthy relationships and show that the company is a valuable corporate citizen. Overall this
also demonstrates the support of a healthy public policy environment.
A Comprehensive Approach
“Approach is specifically designed to help you turn sustainability challenges into business
advantages.” (Rockwell, 2009)
Achieve your business goals
“At the core is a fundamental belief that corporate investment in the environmental and social
responsibility must strengthen business performance which in order to be truly successful it
must: reduce environmental impact, achieve a genuine economy in the use of resources, deliver a
return on investment, and improve the equity of your company.”
Enhance your sustainability
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“Three major corporate sustainability objectives include: energy conservation and
efficiency, environmental responsibility and resource management, safety for workers,
machinery, processes, and products.”
Integrated Strategic Approach
Economic Impact
There are four basic levels of Economic Impact:
Direct Impact : Company sells products, provides wages to employees, and pays taxes
Indirect Impact: Company pays suppliers and creates business for resellers, who in turn
generate employment
Induced Impact: Consumer spending by the company employees and suppliers
stimulates additional economic activity
Productivity Impact: The use of products containing Qu-Chips result in productivity
gains in the economy
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Money invested in a site in Nevada will include an economic impact to the region of non-
company jobs that result from operations in the state, company employees volunteering in the
local community, and providing improvements for local schools and non-profit organizations.
Code of Conduct
Our code of conduct is modeled after Intel Corporation’s and establishes a simple mission
statement of which all other aspects of the business must comply. This guides the behavior of the
officers, employees, and suppliers. The principles promote ethical conduct and compliance with
applicable local and federal laws and regulations. The code also expresses the adherence to non-
discrimination, antitrust, anti-corruption, privacy, health and safety. Employee impacts are both
long and short term on the community and the environment when they are making business
decisions. All employees should receive proper training in the code of conduct when they join
the company and annually thereafter. Multiple channels will also be provided to allow employees
to raise ethical questions and issues. This will be highly encouraged. Subjects can be simple or
include such important topics as human rights principles.
“Conduct business with uncompromising integrity and professionalism.” (Intel, 2010)
Political Accountability
All political affiliations and political contributions including the details of the accountability of
the senior management and Board of Directors level should be readily available. A Model Code
of Conduct for the Corporate Political Spending should be established. Transparency should be
the main focus to establish trust with the community.
Environmental Sustainability
Recognize that consumer usage of the product accounts for largest overall carbon footprint.
An employee-initiated sustainability network should be established. This chartered employee
group provides employee networking, volunteering, and educational opportunities which align
with the corporate environment focus areas.
Climate Change and Energy Efficiency
Industry accounts for 40% of total worldwide energy consumption. (Rockwell, 2009)
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Green building design should incorporated into the facility. Leadership in Energy and
Environmental Design (LEED) certification should be the goal of the facility. This can also
include renovations of LEED criteria such as the use of low-VOC paints, low-flow faucets and
toilets, and non-adhesive carpets and furniture which are recyclable at the end of their lives.
The company should also invest in renewable/green power if at all possible. As part of the U.S.
EPA’s Green Power Partnership program the company should commit to purchase renewable
energy. Partnering with third parties to complete solar or wind installations in the state of Nevada
as well as on the facility itself could add to community support of regulatory obligations and
programs. All of the company facilities should conform to the Energy Star Program
Requirements.
Water Conservation
Water conservation is a huge responsibility in the industry. “Intel estimates that it takes 16
gallons of water to produce a single chip, by comparison, producing one pair of jeans takes 2900
gallons, one hamburger takes 634 gallons, and one cup of tea takes 9 gallons.”
In an analysis of the water Footprint the findings suggest that the largest impact of water use is
from the direct operations as well as the production of electricity. The smallest portion comes
from material suppliers.
Partnering with the local community to address sustainable water issues is recommended.
Implementing a progressive water management system to reduce daily demand or
implementation of a facility to treat the plant’s waste water from the plant so that it can be reused
is highly recommended. Capturing wastewater to use in cooling towers is another beneficial
possibility. To
f
The performance of the facility’s water use and reuse is a trademark with other semiconductor
companies. Participating in environmental benchmarking activities with other industry members
of the World Semiconductor Council (WSC), Semiconductor Industry Association (SIA), and
International SEMATECH Manufacturing Initiative (ISMI) will allow the company to be
appropriately compared. This will demonstrate transparency, establish risk assessment, and areas
for improvement.
Waste: Reduce, Reuse, and Recycle
Installation of more efficient lighting in the facility, smart system controls, boiler and chiller
water system improvements, cleanroom heating, ventilation, air conditioning, and heat recovery
should all be pursued. Employees should continue to identify new opportunities to minimize
waste and recycle/reuse materials from large scale process improvements to everyday actions.
These might be composting programs for cafeteria waste, or the sale or donation of waste
materials that cannot be used by our facility to environmental industries such as wind or solar.
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Chemical Waste
Sustainable projects that can be undertaken by the company on an ongoing basis include
improvements in the use of paper. Reduced paper usage can be encouraged among employees to
receive electronic credit card, retirement, and payroll documents. Personal codes on the company
printers can also raise the awareness of printing volumes. Travel and ground transportation can
include booking only hotels that are labeled as green hotels and rentals of vehicles can have a
minimum standard of miles per gallon as well as Carbon emmission levels. Office supplies and
the suppliers can also be tested for sustainability. Marketing initiatives can also be printed closer
to their final destination to reduce shipping and delivery related emissions.
Landfill Impact From Finished Goods
Electronic waste (e-waste) such as computers and mobile phones are a global concern.
Involvement in the EPS’s Plug-In to eCycling campaign is a must. This campaign is designed to
build public and private support for proper recycling of used electronics. A collection site at the
local facility and drop off points in the local and state community would be an excellent positive
relationship builder with the community.
Product packaging is also an environmental concern. Reducing the waste generation and
emissions through better design of the trays and packaging materials used to ship products is key.
Reducing the tray and packaging weight can also help reduce the transportation costs and fuel
consumption. Constant improvement into the type of material used for the packaging and the
packaging design to allow for a more dense packing of the chip compared to the volume of the
shipment; can ultimately improve the environmental impact as well.
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Reducing Air Emissions
Joining the EPA’s Center for Climate Leadership and playing an active role is highly
recommended. (EPA.gov/climateleadership) EPA’s Center for Corporate Climate
Leadership serves as a resource center for all companies looking to expand their work in the area
of GHG measurement and management. The Center was launched in 2012 to establish the norms
of climate leadership by encouraging companies with emerging climate objectives to identify and
achieve cost-effective GHG emission reductions. They also aim to aid more advanced companies
to drive innovations by reducing their greenhouse gas impacts in their supply chains and beyond.
A company policy is needed to minimize the emissions of both volatile organic compounds
(VOC’s) and hazardous air pollutants (HAP’s). Thermal oxidizers and wet scrubbers should be
used to neutralize and absorb gases and vapors where necessary.
Performance Summary and Goals
An overall goal in order to reduce the company’s carbon footprint, purchase green power, and
invest in energy saving projects. Employee compensation should be implemented to further
encourage employees to take action recommended actions. Establishing goals for each year and
meeting or exceeding those goals, while communicating these goals to the public, is a great way
to show transparency, community, and environmental awareness.
Ethics
The company should be viewed by the employees as a great place to work and that should be
reflected in the corporate philosophy and management practices. Cultivating open and direct
communications, rewarding and recognizing the employees, and investing in career development
and leadership should be major company goals.
Career Growth and Development
Employees should be encouraged to continuously learn. This can include on the job learning,
classroom learning, and by connecting with others. Working with managers to align job
assignments with strengths and interests as well as the needs of the organization is beneficial to
the individual and to the company as a whole. Providing resources and tools by encouraging
employees to connect with managers and senior leaders. The company should also provide
mentoring relationships, open forums, quarterly events, employee groups, and social media
channels. Employees should also be given an opportunity to do rotational or temporary job
assignments that expand the skill levels of the employee.
Communication and Recognition
Open door policies should enable employees to speak directly with all levels of management.
Concerns, ideas, problems, and workplace issues can be addressed. Meetings, surveys, question
and answer sessions about workplace improvements and innovation should happen relatively
frequently. Also multiple tools equipment should be used encourage sustained improvement
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practices, rewards, and leadership practices. This can include a designated website to collect the
data. All of this can lead to increased employee satisfaction and reduced employee turnover.
Diversity
Diversity in the workforce is an integral part of creating the company’s competitive strategy and
vision. Many studies show that employees working in a diverse environment tend to feel more
fulfilled, creative, and productive on the job. Continuously striving to attain a work environment
that honors, values and respects all employees should be the goal. The company will comply
with all applicable laws and provide equal employment opportunity for all applicants and
employees without regard to race, color, religion, sex, national origin, ancestry, age, disability,
veteran status, marital status, sexual orientation, gender identity. This includes reasonable
accommodations for disabled employees. The employment, advancement and retention of
women in technical and leadership areas should also be a focus of the organization.
A perfect example of a changing corporate world is the installment in 2012 of a woman in charge
of Lockheed Martin. The President and CEO is now a woman, Marillyn A. Hewson.
Creating programs designed to promote cultural awareness as well as frequent company events
that give employees the opportunity to share their heritages and connect with other employees in
conjunction with intercultural training can create better working relationships between the
employees.
Health, Safety, and Employee Wellness
Physically and mentally fit employees can be more productive as well as enjoy a better quality of
life.
Cumulative Trauma Disorders (CTD’s) and Repetitive Stress Injury (RSI) can be key problems.
Proper training and ongoing emphasis on proper techniques such as rest breaks can reduce the
number of these injuries. Proactive policies and early symptom detection can also reduce these
issues. An established Environmental Health and Safety (EHS) policy to provide a safe and
injury free work place is essential. Safety training, orientation sessions, and continued on the job
training can be utilized. Electrical safety, control of hazardous materials, chemical safety, and
ergonomics should all be addressed. ISO 14001 and ISO 18001 should seriously be considered.
Performance Summary and Goals
Continuous improvement in this area should be established as a goal. Additionally annual data
should be collected as well as surveys of employees.
Supply Chain Responsibility
The supply chain should have very clear expectations in terms of their business ethics and
sustainability. Suppliers should work with the EICC and other associations as well as collaborate
with us on broad initiatives. The tracing and eliminating of conflict minerals from the electronics
supply chain is of key importance. Purchase of raw materials in the supply chain that may have a
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link to human rights atrocities must be eliminated. Therefore transparency of the suppliers is of
vital importance. US Conflict Minerals Law applies to materials originating (or claimed to
originate) from the DRC as well as the nine adjoining countries: Angola, Burundi, Central
African Republic, Congo Republic (a different nation than
DRC), Rwanda, Sudan, Tanzania, Uganda, and Zambia.
Supplier Environmental Impact
Suppliers must actively support the company’s responsibility and goals by creating their own
corporate responsibility strategy or policy that sets aggressive goals in which they engage and
audit their own suppliers. They must make sure that all the links in the supply chain have the
same rigorous standards in terms of sustainability. This includes worker safety, ethics, human
resources, and environmental management.
“The EICC Code sets forth performance, compliance, management system, and reporting
guidelines. It also provides assessment and audit procedures across key areas of social
responsibility and environmental stewardship. It covers human rights issues and labor standards
related to child and forced labor, freedom of association and collective bargaining, diversity and
non-discrimination, working hours and minimum wages, ethical practices, and worker health and
safety.”
Supplier continuous quality improvement is another area of interest. To drive our suppliers’
improvements we should use our tools and management systems to help improve them while
making scheduled visits as well as acknowledging them publicly. A measure of risk across all
suppliers in the supply chain needs to be generated along with an annual audit of those at the
highest risk level needs to be performed.
Contributions to Society
Through collaboration, creative applications of technology, strategic giving, transforming
education, and increasing economic opportunity; the company makes the community where our
facilities are located better places to live and work. Investments in the community are beneficial
to both parties.
Education
Investment in education expands opportunities for young people, while also benefiting the
company. Education is the foundation of innovation and as a technology company the success of
the company rests on the availability of skilled workers, a healthy technology ecosystem, and
knowledgeable customers. In turn, the health of local economies, including those where our
employees live and work-depends on access to technology and quality education. International
studies show that education plays a pivotal role in fostering labor productivity and economic
growth.
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Community Engagement and Employee Volunteerism
Shared value with the community that a facility is located is paramount. Constructive
relationships with a community allow for valuable feedback that helps to improve performance.
Encouraging meaningful volunteerism of employees positively impacts their satisfaction and
pride. It also helps to attract and retain talented people. Volunteer work can be business plans for
non-profits, after school education, volunteering for local clubs… The possibilities are limitless.
Science fairs and local contests with the local community and educational centers can inspire
young innovators and encourage students to become actively involved in their education.
Employees can serve as mentors for the participants.
Annual community giving campaigns of food, clothing, school supplies, holiday gift drives are
also a worthwhile way to become a good community stewardship.
Empowering Women
As with before in the section covering diversity, women need to be an integral part of the
company policies as well. To foster innovation and drive economic growth the company needs to
enable everyone, especially women with education and entrepreneurial skills. This is a shared
goal with governments, NGOs, and development agencies. By empowering girls and women, we
also improve the lives of their children, families, and communities.
Recommendations
Our group realizes that the decisions we are making and analyzing now will be over a five year
span. With that said, we are aware that there is plenty of opportunity for variability in the system
after our proposal is made. We believe that after implementation of our processes, the system
must be managed properly. As of now our forecasting is based off of 2 years of history. As time
goes on the size of the Qu-chip history will increase and allow for more accurate forecasts. We
will also be forecasting for use on smaller timelines. Instead of having a forecast trying to predict
the demand of chips within five years from now, we will be interested in looking at the near
future, such as the next two periods (two months). Creating a moving forecast and reducing our
scope will be very beneficial for the company as more and more history is collected for our Qu-
chips. In addition, the moving forecast will also allow us to see if and when capacities have to be
increased and therefore more machines have to be purchased. We may also see an increase in
technology in the future that may allow for new and better decisions to be made for our
production process and these ideas need to be dealt with accordingly.
As of now we have noticed a very unfortunate production aspect that we believe can be
improved significantly. The demand data given to us reveals that the demand for chips is
equivalent to the arrival of the chips. This is a very troubling situation. Our process will always
produce scrap up to about 5% and in that case demand will not be met. Not only that, but since
we do not control the amount of chips we receive to manufacture, we have no way of making up
that demand lost. If demand is not met, then it simply will not be taken care of in the description
of the arrival process we were given. We have come up with a proposal that will allow our
company to schedule chip shipments to our system. This will give us more control of our
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manufacturing process in many ways. For example, it will allow us to make up any demand that
we lost in scrap and give us the opportunity to create inventory for future large demands with the
use of material resource planning (MRP). We will also be able to set a production schedule in
which we manufacture the same amount of chips every week in order to decrease the amount of
variation on the shop floor and ensure that our capacity is being utilized effectively. Ideally, we
believe that this goal can be accomplished if our supplier is willing to ship unmanufactured chips
to us on a schedule that we create instead of having individual chips show up at varying rates
throughout the week. We believe this tactic will be extremely beneficial to both the supplier and
us.
Our team would also recommend that an existing structure be purchased for the manufacturing
plant, instead of building a brand new manufacturing plant. This would save a lot of money and a
lot of time. We would also recommend becoming an active member of the EPA’s Center for
Climate Leadership. EPA’s Center for Corporate Climate Leadership acts as a resource center for
companies that want to develop their work in GHG measurement and management.
Conclusion
To meet the requirements of this project we utilized analytical forecasting techniques to
determine the projected demand of our product. We forecasted our data five years. After creating
this forecast we were able to analyze what machines would serve our plant best. Once we
understood which machines provided an economic advantage, we utilized Arena to analyze
which machines functioned best. In the end we decided to buy the NUR etching machine,
Whitworth laminating machine, and Rudd mounting machines. With types of machines chosen,
we then went to Arena again to simulate the entire process. Then we decided we would need
eight NUR machines, forty-eight Whitworth, and eight Rudd, eight Beta Insulators, and twelve
Alpha Insulators. With these factors determined we organized our plant into eight work cells
consisting of these machines.
Finally after a thorough examination of all factors of the project, the anticipated rate of return for
our Qu-Chip manufacturing business is above fifteen percent. The final rate of return based on
our calculations is approximately twenty-eight percent. This is right on the goal presented to us
at the beginning of the project. Therefore the amount of capital proposed will be a worthy
investment.
71
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74
Appendix
Money Conversions
75
76
America Europe Swiss Franc Japan Great Britain
USD Euro Chf Yen GBP
Pounds
1 0.75 0.92 93.54 0.65
1.333333 1.086957 0.010691 1.538462
77
Demand Charts
Data Summary Table of Fastest Average Inter-arrival Week
Data-Histogram Plot of Fastest Average Inter-arrival Week
78
KS Test of Fastest Average Inter-arrival Week
Chi-Square Test of Fastest Average Inter-arrival Week
Arena Output of Fastest Average Inter-arrival Week
79
Data Summary Table of Medium Average Inter-arrival Week
Density Histogram Plot of Medium Average Inter-arrival Week
80
KS Test of Medium Average Inter-arrival Week
Chi Square Test of Medium Average Inter-arrival Week
Data Summary Table of Slowest Average Inter-arrival Week
81
KS Test of Slowest Average Inter-arrival Week
Chi Square Test of Slowest Average Inter-arrival Week
82
Density-Histogram of Slowest Average Inter-arrival Week
83
Location
Old Design Evaluation Chart:
Nodes Production
Shipping/
Receiving
Storage
Warehouse Cafeteria Office Maint
Engineering/
IT Totals
Production (P) - 4 X 0 4 X 0 1 X 0 2 X 0 3 X 0 3 X 0 0
Shipping/Receiving
(S/R) - - 3 X 3 1 X 3 2 X 1 2 X 0 2 X 0 14
Storage
Warehouse (W) - - - 0 X 3 1 X 4 0 X 3 0 X 5 4
Cafeteria (LR) - - - - 1 X 0 0 X 5 0 X 3 0
Office (O) - - - - - 1 X 3 3 X 0 3
Maint. & Tool
(M/T) - - - - - - 1 X 4 4
Total - - - - - - - 25
84
0
5000
10000
15000
20000
25000
30000
35000
93 94 95 96 97 98 99 100101102103104
Nu
mb
er
of
Qu
-Ch
ips
Week
Given Demand
Given Demand
Forecasting
Last 12 Weeks of Demand
This graph shows
weekly demand.
These are the
regression line
values when this
demand is
aggregated into
3 periods of 4
weeks.
Slope: 1719
Intercept: 101320
Aggregate Forecast in Excel
85
There are several more values that we omitted.
86
$0.00
$5,000.00
$10,000.00
$15,000.00
$20,000.00
$25,000.00
$30,000.00
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64
AW
($
)
Period
AW When Purchased In Period t
Etching Machine
Laminating Machine
Mounting Machine
Aggregate Planning
Linear Program Screen Shot
AW Objective Function Values
87
Employees’ Salaries
Finan
ces
Price
(ho
urly)
Price
(avg US $) an
nu
alQ
uarte
rly
Salary Co
st
Pe
riod
Salary
Co
stq
uan
tity
An
nu
al
Un
em
plo
yme
nt
Insu
rance
Disab
ility and
He
alth in
suran
ce
cost
Qu
arterly
total salary
ann
ual salary
cost w
ith
be
ne
fits
Total salary
cost/p
erio
d
Total salary co
st/
pe
riod
with
be
ne
fits
Emp
loye
es
Up
pe
r Man
agem
en
t/top
exe
c$90.48
$188,210.00$47,052.50
$14,477.691
$793.55$93.67
$47,052.50$194,833.60
$14,477.69$14,987.20
marke
ting an
d sale
s man
agers
N/A
$0.00$0.00
1$0.00
$0.00$0.00
bu
sine
ss op
eratio
ns sp
ecialists
$32.51$67,620.00
$16,905.00$5,201.54
1$793.55
$35.70$16,905.00
$74,256.00$5,201.54
$5,712.00
finan
cial spe
cialists$27.79
$57,800.00$14,450.00
$4,446.151
$793.55$30.98
$14,450.00$64,438.40
$4,446.15$4,956.80
com
pu
ter sp
ecialists
$21.52$44,770.00
$11,192.50$3,443.85
5$793.55
$24.71$55,962.50
$51,396.80$17,219.23
$19,768.00
com
pu
ter h
ardw
are e
ngin
ee
rs$45.85
$95,370.00$23,842.50
$7,336.158
$793.55$49.04
$190,740.00$102,003.20
$58,689.23$62,771.20
ele
ctrical/ ele
ctron
ics en
gine
ers
$42.16$87,690.00
$21,922.50$6,745.38
8$793.55
$45.35$175,380.00
$94,328.00$53,963.08
$58,048.00
sales re
ps
N/A
$0.00$0.00
1$0.00
$0.00$0.00
custo
me
r service
rep
sN
/A$0.00
$0.001
$0.00$0.00
$0.00
packe
rs, package
rs, han
d$11.93
$24,810.00$6,202.50
$1,908.464
$731.90$15.12
$24,810.00$31,449.60
$7,633.85$9,676.80
parkin
g lot atte
nd
ants/se
curity
$10.55$21,940.00
$5,485.00$1,687.69
16$647.23
$13.74$87,760.00
$28,579.20$27,003.08
$35,174.40
pu
rchasin
g agen
ts$25.87
$53,820.00$13,455.00
$4,140.002
$793.55$29.06
$26,910.00$60,444.80
$8,280.00$9,299.20
bo
okke
ep
ing, acco
un
ting, an
d au
ditin
g clerks
$17.65$36,710.00
$9,177.50$2,823.85
2$793.55
$20.84$18,355.00
$43,347.20$5,647.69
$6,668.80
secre
taries an
d ad
min
istrative assistan
ts$25.91
$53,900.00$13,475.00
$4,146.152
$793.55$29.10
$26,950.00$60,528.00
$8,292.31$9,312.00
office
clerks, ge
ne
ral$15.20
$31,620.00$7,905.00
$2,432.312
$793.55$18.39
$15,810.00$38,251.20
$4,864.62$5,884.80
me
chan
ical en
gine
ers
$38.97$81,060.00
$20,265.00$6,235.38
8$793.55
$42.16$162,120.00
$87,692.80$49,883.08
$53,964.80
Ind
ustrial En
gine
er
$36.24$75,370.00
$18,842.50$5,797.69
8$793.55
$39.43$150,740.00
$82,014.40$46,381.54
$50,470.40
insp
ecto
rs, teste
rs$18.15
$37,760.00$9,440.00
$2,904.628
$793.55$21.34
$75,520.00$44,387.20
$23,236.92$27,315.20
qu
ality en
gine
ers (all o
the
r)$40.85
$84,970.00$21,242.50
$6,536.158
$793.55$44.04
$169,940.00$91,603.20
$52,289.23$56,371.20
Man
agem
en
t/ Sup
erviso
rs/ Team
Lead
s$45.66
$94,970.00$23,742.50
$7,305.388
$793.55$48.85
$189,940.00$101,608.00
$58,443.08$62,528.00
Janito
rs$13.04
$27,130.00$6,782.50
$2,086.928
$793.55$16.23
$54,260.00$33,758.40
$16,695.38$20,774.40
Co
mp
ute
r Ch
ip Te
chn
icians
$15.18$31,570.00
$7,892.50$2,428.46
1$793.55
$18.37$7,892.50
$38,209.60$2,428.46
$2,939.20
Mach
inists
$19.86$41,320.00
$10,330.00$3,178.46
96$793.55
$23.05$991,680.00
$47,944.00$305,132.31
$354,048.00
Main
ten
ance
Cre
w$26.57
$55,270.00$13,817.50
$4,251.548
$793.55$29.76
$110,540.00$61,900.80
$34,012.31$38,092.80
Too
l Crib
Op
erato
r$12.16
$25,290.00$6,322.50
$1,945.384
$746.06$15.35
$25,290.00$31,928.00
$7,781.54$9,824.00
payro
ll and
time
kee
pin
g clerks
$18.85$39,220.00
$9,805.00$3,016.92
2$793.55
$22.04$19,610.00
$45,843.20$6,033.85
$7,052.80
Ware
ho
use
Inve
nto
ry Op
erato
r$12.16
$25,290.00$6,322.50
$1,945.388
$746.06$15.35
$50,580.00$31,928.00
$15,563.08$19,648.00
hu
man
reso
urce
s (no
t payro
ll and
time
kee
pin
g)$16.76
$34,860.00$8,715.00
$2,681.542
$793.55$19.95
$17,430.00$41,496.00
$5,363.08$6,384.00
Ge
ne
ral main
ten
ance
and
rep
air wo
rkers
$21.21$44,110.00
$11,027.50$3,393.08
8$793.55
$24.40$88,220.00
$50,752.00$27,144.62
$31,232.00
Forklift O
pe
rator
$18.00$37,500.00
$9,375.00$2,884.62
4$793.55
$21.19$37,500.00
$44,075.20$11,538.46
$13,561.60
Mate
rial Han
dle
r$21.97
$45,700.00$11,425.00
$3,515.3816
$793.55$25.16
$182,800.00$52,332.80
$56,246.15$64,409.60
$877,645.38$996,465.60
88
Machines Currency USD Equivalent additional cost quantity Total w/o tax Total plus tax Shipping/Delivery Cost
Installation
Cost
Total w/ tax,
shipping/delivery,
Nur 101473057 Yen $1,084,809.25 8 $8,678,474.00 $9,381,430.39 $867,847.40 $1,301,771.10 $11,551,048.89
whitwort 154809.86 Pound $238,169.02 48 $11,432,112.96 $12,358,114.11 $1,143,211.30 $1,714,816.94 $15,216,142.35
Rhug $1,520,000.00 8 $12,160,000.00 $13,144,960.00 $1,216,000.00 $1,824,000.00 $16,184,960.00
Alpha $114,000.00 $7,900.00 12 $208,800.00 $225,712.80 $20,880.00 $31,320.00 $277,912.80
Beta $250,000.00 8 $2,000,000.00 $2,162,000.00 $200,000.00 $300,000.00 $2,662,000.00
Packaging $35,000.00 1 $35,000.00 $37,835.00 $3,500.00 $5,250.00 $46,585.00
$34,514,386.96 $37,310,052.30 $3,451,438.70 $5,177,158.04 $45,938,649.04
Utility/ Property per sq ft. sq. ft Price per month ($1.51 per sq. ft)Price per Periodenergy cost 95.1 143514 $216,706.14 $200,036.44
Plant Proposed lot price real property transfer tax Total building cost 1 year property tax
Need 170000 sq. ft (=3.9 acres) 6000000 30600 6030600 192000
7-8 million dollars (depending on lot size and age) 7000000 35700 7035700 224000
8000000 40800 8040800 256000
Machine Cost with Tax
Utilities
Property Estimates
89
Taxable Deductions
NUR (Etching)
MACRS, $
Year Depreciation Book Value Total Machines Property Cost Taxable Property
0 $9,381,430.39 $37,310,052.30 $44,310,052.30 $31,874,611.87
1 $3,126,830.75 $6,254,599.64 $41,183,221.55 $15,290,293.62
2 $4,170,045.81 $2,084,553.83 $37,013,175.74 $9,764,674.88
3 $1,389,389.84 $695,163.99 $35,623,785.90 7000000
4 $695,163.99 $0.00 $34,928,621.91
Whitwort
MACRS, $
Year Depreciation Book Value
0 $12,358,114.11 $34,928,621.91
1 $4,118,959.43 $8,239,154.68 $30,809,662.48
2 $5,493,181.72 $2,745,972.96 $25,316,480.76
3 $1,830,236.70 $915,736.26 $23,486,244.06
4 $915,736.26 $0.00 $22,570,507.80
Mounting
MACRS, $
Year Depreciation Book Value
0 $13,144,960.00 $22,570,507.80
1 $4,381,215.17 $8,763,744.83 $18,189,292.63
2 $5,842,934.72 $2,920,810.11 $12,346,357.91
3 $1,946,768.58 $974,041.54 $10,399,589.34
4 $974,041.54 $0.00 $9,425,547.80
Alpha Insulator
MACRS, $
Year Depreciation Book Value
0 $225,712.80 $9,425,547.80
1 $75,230.08 $150,482.72 $9,350,317.72
2 $100,329.34 $50,153.38 $9,249,988.38
3 $33,428.07 $16,725.32 $9,216,560.32
4 $16,725.32 $0.00 $9,199,835.00
Beta Regulator
MACRS, $
Year Depreciation Book Value
0 $2,162,000.00 $9,199,835.00
1 $720,594.60 $1,441,405.40 $8,479,240.40
2 $961,009.00 $480,396.40 $7,518,231.40
3 $320,192.20 $160,204.20 $7,198,039.20
4 $160,204.20 $0.00 $7,037,835.00
Packaging
MACRS, $
Year Depreciation Book Value
0 $37,835.00 $7,037,835.00
1 $12,610.41 $25,224.59 $7,025,224.59
2 $16,817.66 $8,406.94 $7,008,406.94
3 $5,603.36 $2,803.57 $7,002,803.57
4 $2,803.57 $0.00 $7,000,000.00
90
Qu-Chip
qu-chip
gross net Period Made Demand Made after failureInventory sold Scrap Cost Profit before Scrap Profit after Scrap
$384.56 $76.91 1 121033 103801 120240 16439 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
2 121033 96246 120240 40433 96246 89 0 0 704 793 $218,295.48 $7,402,279.86 $7,183,984.38
3 121033 89805 120240 70868 89805 89 0 0 704 793 $218,295.48 $6,906,902.55 $6,688,607.07
4 121033 87757 120240 103351 87757 89 0 0 704 793 $218,295.48 $6,749,390.87 $6,531,095.39
5 121033 80724 120240 142867 80724 89 0 0 704 793 $218,295.48 $6,208,482.84 $5,990,187.36
6 121033 71639 120240 191468 71639 89 0 0 704 793 $218,295.48 $5,509,755.49 $5,291,460.01
7 121033 69517 120240 242191 69517 89 0 0 704 793 $218,295.48 $5,346,552.47 $5,128,256.99
8 121033 61306 120240 301125 61306 89 0 0 704 793 $218,295.48 $4,715,044.46 $4,496,748.98
9 121033 67559 120240 353806 67559 89 0 0 704 793 $218,295.48 $5,195,962.69 $4,977,667.21
10 121033 88446 120240 385600 88446 89 0 0 704 793 $218,295.48 $6,802,381.86 $6,584,086.38
11 121033 112063 120240 393777 112063 89 0 0 704 793 $218,295.48 $8,618,765.33 $8,400,469.85
12 121033 140884 120240 373133 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
13 121033 141371 120240 352002 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
14 121033 121119 120240 351123 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
15 121033 112100 120240 359263 112100 89 0 0 704 793 $218,295.48 $8,621,611.00 $8,403,315.52
16 121033 104414 120240 375089 104414 89 0 0 704 793 $218,295.48 $8,030,480.74 $7,812,185.26
17 121033 101855 120240 393474 101855 89 0 0 704 793 $218,295.48 $7,833,668.05 $7,615,372.57
18 121033 93534 120240 420180 93534 89 0 0 704 793 $218,295.48 $7,193,699.94 $6,975,404.46
19 121033 82871 120240 457549 82871 89 0 0 704 793 $218,295.48 $6,373,608.61 $6,155,313.13
20 121033 80286 120240 497503 80286 89 0 0 704 793 $218,295.48 $6,174,796.26 $5,956,500.78
21 121033 70691 120240 547052 70691 89 0 0 704 793 $218,295.48 $5,436,844.81 $5,218,549.33
22 121033 77781 120240 589511 77781 89 0 0 704 793 $218,295.48 $5,982,136.71 $5,763,841.23
23 121033 101674 120240 608077 101674 89 0 0 704 793 $218,295.48 $7,819,747.34 $7,601,451.86
24 121033 128633 120240 599684 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
25 121033 161481 120240 558443 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
26 121033 161810 120240 516873 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
27 121033 138438 120240 498675 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
28 121033 127954 120240 490961 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
29 121033 119022 120240 492179 119022 89 0 0 704 793 $218,295.48 $9,153,982.02 $8,935,686.54
30 121033 115954 120240 496465 115954 89 0 0 704 793 $218,295.48 $8,918,022.14 $8,699,726.66
31 121033 106344 120240 510361 106344 89 0 0 704 793 $218,295.48 $8,178,917.04 $7,960,621.56
32 121033 94103 120240 536498 94103 89 0 0 704 793 $218,295.48 $7,237,461.73 $7,019,166.25
33 121033 91055 120240 565683 91055 89 0 0 704 793 $218,295.48 $7,003,040.05 $6,784,744.57
34 121033 80076 120240 605847 80076 89 0 0 704 793 $218,295.48 $6,158,645.16 $5,940,349.68
35 121033 88002 120240 638085 88002 89 0 0 704 793 $218,295.48 $6,768,233.82 $6,549,938.34
36 121033 114902 120240 643423 114902 89 0 0 704 793 $218,295.48 $8,837,112.82 $8,618,817.34
37 121033 145203 120240 618460 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
38 121033 182079 120240 556621 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
39 121033 182249 120240 494612 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
40 121033 155756 120240 459096 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
41 121033 143809 120240 435527 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
42 121033 133630 120240 422137 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
43 121033 130053 120240 412324 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
44 121033 119154 120240 413410 119154 89 0 0 704 793 $218,295.48 $9,164,134.14 $8,945,838.66
45 121033 105334 120240 428316 105334 89 0 0 704 793 $218,295.48 $8,101,237.94 $7,882,942.46
46 121033 101824 120240 446732 101824 89 0 0 704 793 $218,295.48 $7,831,283.84 $7,612,988.36
47 121033 89461 120240 477511 89461 89 0 0 704 793 $218,295.48 $6,880,445.51 $6,662,150.03
48 121033 98224 120240 499527 98224 89 0 0 704 793 $218,295.48 $7,554,407.84 $7,336,112.36
49 121033 128131 120240 491636 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
50 121033 161773 120240 450103 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
51 121033 202676 120240 367667 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
52 121033 202687 120240 285220 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
53 121033 173074 120240 232386 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
54 121033 159663 120240 192963 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
55 121033 148238 120240 164965 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
56 121033 144151 120240 141054 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
57 121033 131965 120240 129329 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
58 121033 116566 120240 133003 116566 89 0 0 704 793 $218,295.48 $8,965,091.06 $8,746,795.58
59 121033 112593 120240 140650 112593 89 0 0 704 793 $218,295.48 $8,659,527.63 $8,441,232.15
60 121033 98846 120240 162044 98846 89 0 0 704 793 $218,295.48 $7,602,245.86 $7,383,950.38
61 121033 108446 120240 173838 108446 89 0 0 704 793 $218,295.48 $8,340,581.86 $8,122,286.38
62 121033 141359 120240 152719 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
63 121033 178343 120240 94616 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
64 121033 223273 120240 -8417 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
65 121033 223126 120240 -111303 120240 89 0 0 704 793 $218,295.48 $9,247,658.40 $9,029,362.92
$530,458,575.94 $516,269,369.48
High Demand
Quantity Bad
(Alpha Insulator)
Quantity Bad
(Laminating)
Quantity Bad
(Beta Regulator)
Quantity Bad
(Inspection)
91
Actual Profit per Period
Period Salary Cost Property Cost Machine Cost Utilities Cost Qu-chip Profit Additional Taxes Income Tax
net profit w/o income
tax
cumulative profit with
income tax
0 $0.00 $7,035,700.00 $45,938,649.04 $0.00 $0.00 $0.00 $0.00 -$52,974,349.04 -$52,974,349.04
1 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 -$45,141,488.16
2 $996,465.60 $0.00 $0.00 $200,036.44 $7,183,984.38 $0.00 $5,987,482.34 -$39,154,005.83
3 $996,465.60 $0.00 $0.00 $200,036.44 $6,688,607.07 $35,092.48 $5,457,012.55 -$33,696,993.27
4 $996,465.60 $0.00 $0.00 $200,036.44 $6,531,095.39 $0.00 $5,334,593.35 -$28,362,399.92
5 $996,465.60 $0.00 $0.00 $200,036.44 $5,990,187.36 $0.00 $4,793,685.32 -$23,568,714.60
6 $996,465.60 $0.00 $0.00 $200,036.44 $5,291,460.01 $35,092.48 $4,059,865.49 -$19,508,849.11
7 $996,465.60 $0.00 $0.00 $200,036.44 $5,128,256.99 $0.00 $3,931,754.95 -$15,577,094.16
8 $996,465.60 $0.00 $0.00 $200,036.44 $4,496,748.98 $0.00 $3,300,246.94 -$12,276,847.22
9 $996,465.60 $0.00 $0.00 $200,036.44 $4,977,667.21 $35,092.48 $3,746,072.69 -$8,530,774.53
10 $996,465.60 $0.00 $0.00 $200,036.44 $6,584,086.38 $0.00 $5,387,584.34 -$3,143,190.19
11 $996,465.60 $0.00 $0.00 $200,036.44 $8,400,469.85 $0.00 $7,203,967.81 $4,060,777.62
12 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $11,893,638.50
13 $996,465.60 $1,019,987.58 $0.00 $200,036.44 $9,029,362.92 $57,008.91 $6,340,830.98 $6,755,864.39 $12,308,671.91
14 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $20,141,532.78
15 $996,465.60 $0.00 $0.00 $200,036.44 $8,403,315.52 $0.00 $7,206,813.48 $27,348,346.26
16 $996,465.60 $0.00 $0.00 $200,036.44 $7,812,185.26 $35,092.48 $6,580,590.74 $33,928,937.01
17 $996,465.60 $0.00 $0.00 $200,036.44 $7,615,372.57 $0.00 $6,418,870.53 $40,347,807.54
18 $996,465.60 $0.00 $0.00 $200,036.44 $6,975,404.46 $0.00 $5,778,902.42 $46,126,709.95
19 $996,465.60 $0.00 $0.00 $200,036.44 $6,155,313.13 $35,092.48 $4,923,718.61 $51,050,428.57
20 $996,465.60 $0.00 $0.00 $200,036.44 $5,956,500.78 $0.00 $4,759,998.74 $55,810,427.31
21 $996,465.60 $0.00 $0.00 $200,036.44 $5,218,549.33 $0.00 $4,022,047.29 $59,832,474.60
22 $996,465.60 $0.00 $0.00 $200,036.44 $5,763,841.23 $35,092.48 $4,532,246.71 $64,364,721.31
23 $996,465.60 $0.00 $0.00 $200,036.44 $7,601,451.86 $0.00 $6,404,949.82 $70,769,671.13
24 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $78,602,532.01
25 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $86,435,392.89
26 $996,465.60 $489,289.40 $0.00 $200,036.44 $9,029,362.92 $57,008.91 $27,680,516.41 $7,286,562.57 $66,041,439.05
27 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $73,874,299.93
28 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $81,707,160.81
29 $996,465.60 $0.00 $0.00 $200,036.44 $8,935,686.54 $35,092.48 $7,704,092.02 $89,411,252.83
30 $996,465.60 $0.00 $0.00 $200,036.44 $8,699,726.66 $0.00 $7,503,224.62 $96,914,477.45
31 $996,465.60 $0.00 $0.00 $200,036.44 $7,960,621.56 $0.00 $6,764,119.52 $103,678,596.97
32 $996,465.60 $0.00 $0.00 $200,036.44 $7,019,166.25 $35,092.48 $5,787,571.73 $109,466,168.70
33 $996,465.60 $0.00 $0.00 $200,036.44 $6,784,744.57 $0.00 $5,588,242.53 $115,054,411.23
34 $996,465.60 $0.00 $0.00 $200,036.44 $5,940,349.68 $0.00 $4,743,847.64 $119,798,258.87
35 $996,465.60 $0.00 $0.00 $200,036.44 $6,549,938.34 $35,092.48 $5,318,343.82 $125,116,602.70
36 $996,465.60 $0.00 $0.00 $200,036.44 $8,618,817.34 $0.00 $7,422,315.30 $132,538,917.99
37 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $140,371,778.87
38 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $148,204,639.75
39 $996,465.60 $312,469.60 $0.00 $200,036.44 $9,029,362.92 $57,008.91 $30,473,038.25 $7,463,382.37 $125,194,983.88
40 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $133,027,844.76
41 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $140,860,705.64
42 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $35,092.48 $7,797,768.40 $148,658,474.04
43 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $156,491,334.92
44 $996,465.60 $0.00 $0.00 $200,036.44 $8,945,838.66 $0.00 $7,749,336.62 $164,240,671.54
45 $996,465.60 $0.00 $0.00 $200,036.44 $7,882,942.46 $35,092.48 $6,651,347.94 $170,892,019.48
46 $996,465.60 $0.00 $0.00 $200,036.44 $7,612,988.36 $0.00 $6,416,486.32 $177,308,505.80
47 $996,465.60 $0.00 $0.00 $200,036.44 $6,662,150.03 $0.00 $5,465,647.99 $182,774,153.79
48 $996,465.60 $0.00 $0.00 $200,036.44 $7,336,112.36 $35,092.48 $6,104,517.84 $188,878,671.63
49 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $196,711,532.51
50 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $204,544,393.39
51 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $212,377,254.27
52 $996,465.60 $224,000.00 $0.00 $200,036.44 $9,029,362.92 $57,008.91 $32,209,601.60 $7,551,851.97 $187,719,504.64
53 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $195,552,365.52
54 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $203,385,226.40
55 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $35,092.48 $7,797,768.40 $211,182,994.80
56 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $219,015,855.68
57 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $226,848,716.56
58 $996,465.60 $0.00 $0.00 $200,036.44 $8,746,795.58 $35,092.48 $7,515,201.06 $234,363,917.62
59 $996,465.60 $0.00 $0.00 $200,036.44 $8,441,232.15 $0.00 $7,244,730.11 $241,608,647.73
60 $996,465.60 $0.00 $0.00 $200,036.44 $7,383,950.38 $0.00 $6,187,448.34 $247,796,096.07
61 $996,465.60 $0.00 $0.00 $200,036.44 $8,122,286.38 $35,092.48 $6,890,691.86 $254,686,787.93
62 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $262,519,648.81
63 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $270,352,509.69
64 $996,465.60 $0.00 $0.00 $200,036.44 $9,029,362.92 $0.00 $7,832,860.88 $278,185,370.57
65 $996,465.60 $224,000.00 $0.00 $200,036.44 $9,029,362.92 $57,008.91 $33,326,024.09 $7,551,851.97 $252,411,198.45
92
Actual Profit per Period with Inflation
inflation/period 1.001353846
profit with
Inflation/period
Cumulative profit
with inflation
-$52,974,349.04 -52974349.04
$7,843,465.37 -$45,130,883.68
$6,003,705.57 -$39,127,178.10
$5,479,206.44 -$33,647,971.66
$5,363,540.94 -$28,284,430.72
$4,826,222.86 -$23,458,207.86
$4,092,955.91 -$19,365,251.94
$3,969,167.57 -$15,396,084.37
$3,336,160.98 -$12,059,923.39
$3,791,965.11 -$8,267,958.28
$5,460,969.92 -$2,806,988.36
$7,311,980.70 $4,504,992.34
$7,961,066.58 $12,466,058.93
$422,397.63 $12,888,456.55
$7,982,637.29 $20,871,093.85
$7,354,562.38 $28,225,656.23
$6,724,593.04 $34,950,249.26
$6,568,214.25 $41,518,463.51
$5,921,362.19 $47,439,825.70
$5,051,926.93 $52,491,752.63
$4,890,556.08 $57,382,308.70
$4,137,958.66 $61,520,267.37
$4,669,174.32 $66,189,441.69
$6,607,388.57 $72,796,830.26
$8,091,370.72 $80,888,200.97
$8,102,325.19 $88,990,526.16
-$21,124,102.14 $67,866,424.02
$8,124,278.64 $75,990,702.66
$8,135,277.66 $84,125,980.32
$8,012,370.06 $92,138,350.38
$7,814,029.67 $99,952,380.05
$7,053,845.63 $107,006,225.68
$6,043,640.67 $113,049,866.35
$5,843,392.58 $118,893,258.93
$4,967,159.67 $123,860,418.61
$5,576,238.87 $129,436,657.48
$7,792,771.26 $137,229,428.74
$8,234,941.39 $145,464,370.13
$8,246,090.24 $153,710,460.37
-$24,256,345.16 $129,454,115.22
$8,268,433.23 $137,722,548.45
$8,279,627.41 $146,002,175.86
$8,253,692.47 $154,255,868.33
$8,302,061.27 $162,557,929.61
$8,224,653.64 $170,782,583.25
$7,068,875.44 $177,851,458.69
$6,828,503.00 $184,679,961.69
$5,824,484.05 $190,504,445.74
$6,514,104.78 $197,018,550.52
$8,369,728.22 $205,388,278.75
$8,381,059.55 $213,769,338.29
$8,392,406.21 $222,161,744.50
-$26,454,958.97 $195,706,785.53
$8,415,145.65 $204,121,931.18
$8,426,538.46 $212,548,469.64
$8,400,143.34 $220,948,612.97
$8,449,370.38 $229,397,983.35
$8,460,809.53 $237,858,792.88
$8,128,673.50 $245,987,466.37
$7,846,732.67 $253,834,199.04
$6,710,668.75 $260,544,867.79
$7,483,497.46 $268,028,365.25
$8,518,237.99 $276,546,603.24
$8,529,770.37 $285,076,373.61
$8,541,318.37 $293,617,691.97
-$28,143,414.79 $265,474,277.18