lean-modeling of honeywell production cell · pdf filelean-modeling of honeywell . production...

Lean-Modeling of Honeywell Production Cell

Sunghoon Kim Mohammad Sachee

A thesis submitted in partial fulfillment Of the requirements for the degree of:

Bachelor of Applied Science

Supervisor: Professor Daniel Frances

Department of Mechanical and Industrial Engineering University of Toronto

March 2008

i

Abstract

Over the past few decades, more and more business enterprises are utilizing lean methodologies and its principles. Largely based on the Toyota Production System, lean manufacturing drives its processes to deliver value to the customer by producing quality products with minimal inventory and waste. Honeywell, with lean added as a major component to their already existing Six Sigma program, launched a pilot lean transformation project in their Evolved Sea Sparrow Missile (ESSM) Control Section production cell. In order to support Honeywell in implementing feasible lean transformations to the ESSM cell, verification of the ‘lean’ cell design was carried out. A discrete event simulation model which accurately reflects the operations of the ESSM cell under lean principles would be the ideal tool for 1) analyzing the material flow within a lean cell, 2) creating “what if” scenarios (or ‘playbooks’) to evaluate the effects on the cell and 3) optimizing various attributes within the cell.

ii

Acknowledgements

We would like to thank Luigi Frassetto and Avi Dan at Honeywell for their unrelenting support and understanding throughout this project. We would also like to thank Aldo Salvalaggio and Nenad Djordjevic for compiling and providing the necessary data. The completion of the project would not have been possible without their assistance, and we are truly grateful.

In addition, we thank Professor Daniel Frances for his continued input throughout completion of the thesis. His support has been greatly appreciated.

Finally, we thank our classmate and visual 8 staff Wallace Law for his support in times of need.

iii

Table of Contents

List of Figures............................................................................................... vi

List of Tables ............................................................................................... vii

1.0 Motivation................................................................................................ 1

2.0 Background ............................................................................................. 3

3.0 Why simulation? ..................................................................................... 5

4.0 Statement of objectives........................................................................... 8

5.0 Literature and Technical Review........................................................ 10

5.1 Toyota Production System...................................................................................... 10 5.2 Kaizen ..................................................................................................................... 10 5.3 Value Stream Map .................................................................................................. 11 5.4 Takt Time................................................................................................................ 11 5.5 Kanban and Pull...................................................................................................... 12 5.6 Production Leveling (Workload Balancing)........................................................... 13 5.7 Supermarket ............................................................................................................ 13 5.8 Single Piece Flow ................................................................................................... 14

6.0 Current State compared with the Future State VSM ....................... 15

6.1 Current State ........................................................................................................... 15 6.2 Future State Design................................................................................................. 19

7.0 Simulation Architecture and Design................................................... 21

7.1 Clock Time.............................................................................................................. 21 7.2 Takt Time and grouping.......................................................................................... 22 7.3 Distribution Fits ...................................................................................................... 24 7.4 Logic ....................................................................................................................... 25 7.5 Resource Allocation and shifts ............................................................................... 26 7.6 Assumptions and Model Limitations ...................................................................... 27

8.0 Analysis .................................................................................................. 28

8.1 Criteria for Analysis................................................................................................ 28 8.2 Model Data Collection............................................................................................ 31 8.3 Conventional Push System compared with the Lean Pull System ......................... 32

8.3.1 Number of units completed.............................................................................. 33 8.3.2 Average Production Lead Time ....................................................................... 34 8.3.3 Average total processing time in the system.................................................... 35

iv

8.3.4 Average total queue time in the system ........................................................... 35 8.3.5 WIP inventory (at 2 weeks into the model) ..................................................... 35 8.3.6 Discussion ........................................................................................................ 36

8.4 Comparison of “playbooks” based on varying takt time ........................................ 37 8.4.1 Number of units completed.............................................................................. 38 8.4.2 Average Production Lead Time ....................................................................... 39 8.4.3 Average total processing time in the system.................................................... 40 8.4.4 Average total queue time in the system ........................................................... 40 8.4.5 WIP inventory.................................................................................................. 41 8.4.6 Discussion ........................................................................................................ 41

9.0 Optimization.......................................................................................... 43

9.1 Batch Size of Burn-In process ............................................................................... 43 9.2 Resource Requirements .......................................................................................... 45 9.3 Number of Shifts..................................................................................................... 47

10.0 Problems Encountered ....................................................................... 50

11.0 Future Considerations........................................................................ 51

12.0 Conclusion ........................................................................................... 53

13.0 References............................................................................................ 54

APPENDIX A – Future State VSM .......................................................... 55

APPENDIX B – Distribution Fit used in the Model................................ 59

APPENDIX C – Icons used in Simul8 ...................................................... 65

APPENDIX D – Simul8 Model Screenshots............................................. 66

APPENDIX E – Resource Patterns for Single Shift Operations............ 72

APPENDIX F – Simul8 Visual Basic Code Examples ............................ 73

v

List of Figures

Figure 6.1 Simplified Bill of Materials for ESSM Control Section ................................. 15 Figure 6.2 Overview of Current State Cell ProcessesESSM ........................................... 16

vi

List of Tables Table 8.1 Deterministic Attributes of Push and Pull Model............................................. 32 Table 8.2 Deterministic Attributes of Various Playbooks ................................................ 38 Table 9.1 Number of completed units for varying batch sizes in pull models ................. 44 Table 9.2 Optimization Results for Resource Requirements on Pull 300 Model............. 46 Table 9.3 Optimization Results for Resource Requirements on Pull 225 Model............. 47 Table 9.4 Deterministic Attributes of One Shift and Two Shift Pull Models................... 48 Table 9.5 Results Collected from One Shift and Two Shift Model Analysis................... 49

vii

1.0 Motivation Honeywell Aerospace is a leading global provider of integrated avionics, engines,

systems and service solutions for aircraft manufacturers, airlines, business and general

aviation, military, space and airport operations. Honeywell is also a major proponent of

lean and six sigma operations.

More and more business enterprises are utilizing lean methodologies and its principles.

This trend is not limited only to the manufacturing sector. Many companies in non-

manufacturing industries such as the service sector and healthcare sector are stepping into

the terrain of lean. One observing this phenomenon can easily raise questions such as,

“why is the lean concept gaining much popularity in industries?”, “how does it help

companies achieve their goals?”, and most importantly “how effective are the lean

transformations?” This paper will attempt to answer these questions along the way,

through the finding of this Honeywell case study using discrete event simulation.

An important motivation for creating a simulation model for a lean production cell at

Honeywell is that Sunghoon and Mohammad completed their 16 month PEY term at

Honeywell Aerospace Ltd. located in Mississauga, Ontario. More specifically,

Sunghoon’s experience as a lean and six sigma specialist at Honeywell enabled him to

gain knowledge and experience on lean methodology as well as on manufacturing

operations. Having been deeply involved with a lean implementation on one of the

production cells, it was expected Sunghoon’s knowledge of operations and simulation

knowledge would benefit the enterprise. Furthermore, Mohammad’s experience as

1

strategic sourcing specialist adds insight to material acquisition and flow. Sunghoon and

Mohammad’s experience at Honeywell coupled with questions on effectiveness of lean

methodologies drove the simulation problem to be analyzed further.

2

2.0 Background

Largely based on the Toyota Production System, Lean Manufacturing drives its processes

to produce high quality products while reducing the use of resources such as human

effort, materials, space, and time. Lean Manufacturing gained a great deal of attention as

Toyota rose up to be the leader of the automotive industry, mainly due to their

manufacturing competency.

Attempting to follow the footsteps of the Toyota Production System, Honeywell added

lean as a major component to their already existing Six Sigma program established since

the mid 1990s. The Honeywell Operating System (HOS), modeled from the TPS, is the

Lean and Six Sigma group within Honeywell who takes initiatives to continuously

improve every aspect of business activities. With the HOS team in place, the Toronto

Honeywell Aerospace site also took the initiative in early 2007 to launch a lean

transformation project. It has decided to take a pilot approach with one of the several

products it manufactures – the Evolved Sea Sparrow Missile (ESSM) Control Section.

The ESSM is the premier defense missile for anti-ship weapons in the NATO fleet used

to protect ships from attacking missiles and aircrafts. The ESSM Control Section is a

high performance, self-contained electromechanical system providing four independent

channels of fin and jet vane actuation in response to commands from the missile

autopilot. The control section is able to steer the missile to intercept the most demanding

airborne target profiles.

3

The ESSM was thought to be the ideal candidate for launching the pilot lean

transformation for a few reasons. One reason was that the ESSM was a product which

contributed significantly to the overall sales. Also, ESSM had room to improve both in

terms of the product quality and financials. Finally, ESSM has had a consistency in its

demand from the customer with very little variability. Once the pilot lean transformation

strategy was decided Honeywell launched a Value Stream Mapping Kaizen to understand

both the current and future Value Stream Maps. The term Kaizen translates directly into

“change for the better” and refers to the concentrated continuous improvement activities.

The ESSM VSM kaizen launched in April 2007 took one week to complete. The first few

days were spent on evaluating the current value stream of ESSM production. The final

two days were dedicated to learning the lean techniques and creating a model for the

future vision of the value stream. As a byproduct of current and future state value stream

maps, Honeywell was able to plan for their transformation efforts. Since the initial Value

Stream Mapping event, the ESSM production cell and its stakeholders have been

spending a great amount of time and dedication to laying the foundation for the future

state.

4

3.0 Why simulation?

The question of why discrete event simulation is being used to model lean

transformations needs to be answered. There are several reasons why simulation is the

chosen methodology used to help support Honeywell achieves successful results in their

new ‘lean’ ESSM cell.

Firstly, simulations facilitate the identification, communication and validation of

requirements. That is, it will identify the feasibility of the Honeywell Future Value

Stream Map prior to actual implementation. The Future VSM is the ‘ideal’ state that

Honeywell envisions how the ‘lean’ cell will operate. Also, because this is a pilot

program that Honeywell has launched, they have no prior experience in implementing

lean transformations. By having the model available, Honeywell will be able to benefit

even after completion of the implementation. They will be able to answer various

questions which may arise throughout the process of transition. Thus the simulation

model will be able to verify the future state’s design and be able to identify any aspects

that need to be modified or improved.

Secondly, a simulation model has lower time and cost requirements as opposed to

building the actual system, then finding flaws and making corrections. That is, using the

simulation model of the new ‘lean’ ESSM cell will help Honeywell design and

implement feasible lean transformations thus saving time and money in the long run.

5

Thirdly, simulations easily allow adjustment for change and the resulting impact on the

overall system can be analyzed. This feature of discrete simulation models is very

effective in being able to test out various scenarios, under various circumstances, and

thus help anticipate and plan ahead. For instance, the available operational time in a

month may vary due to machine breakdowns or parts shortages, and this will definitely

have an impact on takt time and workload balancing on the cell (explained in Technical

Review section).

Also, due to optimization features built in simulation software, key attributes and their

optimum values can be determined in the model for different test scenarios. For instance,

optimum batch sizes or resource allocation can be identified for workstations depending

on changing demand. Additionally, many properties of the cell have uncertainty and

statistical distributions (such as work center processing times, test yield etc.). Simulations

can accommodate this important aspect of the model and is therefore the chosen

methodology for this thesis over other optimization methodologies such as Linear

Programming.

Lastly, simulations look and behave like the real system and navigational, business, data

logic can be embedded in the model. In this way, simulations are visually convincing to

stakeholders and easy to understand.

However, one must keep in mind that simulations have their limitations in that they

cannot fully capture the dynamics of a real production cell. The users must understand

6

the assumptions and model limitations when analyzing the results generated by the

simulation. Secondly, continuous effort must be made to eliminate the uncertainties that

exist in the model rather than accepting them.

7

4.0 Statement of objectives

• Support Honeywell in implementing feasible lean transformations to the ESSM cell

via verification of the ‘lean’ cell design

The methodology used to meet the above objective is to design and create a detailed

simulation model of an intermediate state Value Stream Map (VSM) of the ESSM

production cell. After several meetings with Honeywell focal points, the Director of

Operations and the ESSM Production Cell Team Lead, the team (Sunghoon and

Mohammad) realized some of their initial objectives had to be modified. The true

benefits of creating a simulation model of the future state VSM was discussed. The future

state VSM is the ‘ideal’ state that Honeywell is aiming to achieve, however, the expected

time frame is yet to be determined and may even be by the end of 2008. It may therefore

be more beneficial for the company if the team were to model an intermediate state VSM

of the ESSM cell – a state the cell would transform to in the much near future, rather than

the future state. In order to model the intermediate state, the following must be changed:

1) remove certain aspects of the future state VSM such as ‘supermarkets’ – this is a

feature of the cell that would only be accomplished at the very end of the lean

transformation

2) use work center processing times that are fitted closer to the current processing

times, instead of shortened processing times in the future state VSM

Please Refer to Appendix A for the Future State VSMs.

8

• Analyze how various operations work at different levels and determine work-load

balancing of individual work stations within the cell

The idea behind this lean transformation is ‘single-piece flow’ within the cell. In order to

accomplish this, the various work centers within ESSM cell must have a proper balance

of work-load, i.e. they must be grouped together based on their processing times as well

as the takt time – the maximum time allowed between consecutive units produced in

order to meet demand. In this way, a single-piece would flow through each group of

workstations that have been assigned equal work-load and the cell would produce product

at a rate sufficient to meet customer demand.

• Optimize various attributes in the model

Upon completing the simulation model, the team has analyzed various attributes

comprised of in the ESSM cell such as batch sizes, resource requirements and number of

shifts. Optimal values of these attributes will be determined for different demand

scenarios or ‘playbooks’.

• Evaluate the “Lean-Modeler” module of Visual8 software package

As one of the original goals of this thesis project, the team evaluated the “Lean-Modeler

module of Visual8. However, it was found to have several limitations such as not

allowing variability in data distributions. This was key factor that would not allow the

team to create an accurate simulation model. Also, the benefits of acquiring this software

did not outweigh its cost and therefore Simul8 is the chosen software to be used.

9

5.0 Literature and Technical Review

This section of the report will attempt to make clear what the techniques and tools

utilized in the lean transformation are. Some of the terms are mentioned above and will

also be used in the remainder of this report. Information in this section is attained from

sources [1], [2], [3] and [4] of our references.

5.1 Toyota Production System The main purpose of TPS is to achieve cost reduction so that the company is profitable

even in a slow economy. This is done through reducing 4 types of waste:

1) Excessive production resource

2) Overproduction

3) Excessive inventory

4) Unnecessary capital investments

The other main purposes of TPS other than cost reduction include:

• Quantity Control – ensuring that the fluctuating customer demand is met right on

time

• Quality Assurance – no defect is allowed into the subsequent processes

• Respect for Humanity – cares for well being for all employees

5.2 Kaizen

Kaizen (Japanese for "change for the better") means "continuous improvement" or

"continual improvement". It is an activity whose purpose is not only productivity

improvement, but also to teach people how to perform experiments on their work and

eliminate waste in business processes through standardization. People at all levels of an

10

organization can participate in a kaizen, from the CEO down, as well as external

stakeholders when applicable. Kaizen methodology includes making changes and

monitoring results, then adjusting. Smaller experiments, which can be rapidly adapted as

new improvements are suggested, replace large-scale pre-planning and extensive project

scheduling.

5.3 Value Stream Map

Value Stream Mapping is a Lean technique used to analyze the flow of materials and

information required to bring a product or service to a consumer. It is used to identify

opportunities for improvement. The technique originated at Toyota, where it is known as

"Material and Information Flow Mapping".

5.4 Takt Time

Takt time can be defined as the rate at which a completed product needs to be finished in

order to meet customer demand. In other words, it is the maximum time allowed between

consecutive units produced in order to meet demand. In a lean manufacturing

environment, the pace of production flow or pace time is set equal to the takt time.

Takt Time is defined as:

Where:

Ta = Net Available Time to Work eg. [minutes of work / day]

Td = Total demand (Customer Demand) eg. [units produced / day]

T = TAKT Time eg. [minutes of work / unit produced]

11

http://en.wikipedia.org/wiki/Lean_manufacturing

http://en.wikipedia.org/wiki/Toyota

Net available time is the amount of time available for work to be done. This excludes

break times and any expected stoppage time (e.g. scheduled maintenance, team meetings

etc).

5.5 Kanban and Pull

Kanban (Japanese for "card" or "board") is a concept related to lean and is a means by

which Just-In-Time (JIT) production is achieved. Kanban, as the name suggests,

historically used cards to “signal” the need for an item. The signals for replenishment of

production and materials can be considered as a “demand” for product from one step in

the manufacturing or delivery process to the next. Kanban maintains an orderly and

efficient flow of materials throughout the entire manufacturing process with low

inventory and work in process. It is usually a printed card that contains specific

information such as part name, description, quantity, etc.

Kanban is a pull system that determines the supply, or production, according to the actual

demand of the customers. Kanban system responds quickly to observed demand, and is

beneficial where demand is difficult to forecast. In a Kanban manufacturing environment,

nothing is manufactured unless there is a “signal” to manufacture. This is in contrast to a

push-manufacturing environment where production is continuous.

Kanban results in a production scheme that is highly responsive to customers. Kanban

lowers inventory thus reducing the risk of obsolescence and inventory-holding, as well as

reduces training costs by simplifying operations. It usually also eliminates the need for a

production schedule or Material Resource Planning (MRP) systems which rely on

12

forecasts and are labor and computer-intensive. Kanban is most beneficial when high-

volume and low-value components are involved, and when there products have short lead

times.

5.6 Production Leveling (Workload Balancing)

Workload balancing is important factor to the efficiency of the production. If all groups

of tasks had the same workload, as in processing time, then idle time would be eliminated

and be able to achieve high level of efficiency. However, in the real world there are many

factors which are subject to variation. These variations introduce waste. The difficulty is

making sure all tasks are groups so that the sum processing times for each group is close

to each other as possible. At the same time one must ensure that each sum of processing

times is below the takt time.

5.7 Supermarket

A manufacturing supermarket is, for a factory process, what a retail supermarket is for

the customer. The customers draw products from the 'shelves' as needed and this can be

detected by the supplier who then initiates a replenishment of that item. In a

manufacturing supermarket, processes pick the required parts off the shelf when they

need it. This self-service system reduces the effort required by materials management.

The shelves are then refilled as parts are withdrawn, on the assumption that what has

been withdrawn will be picked again which makes it easy to see how much has been used

and to avoid overstocking. The most important feature of a supermarket system is that

stocking is triggered by actual demand. This signal triggers the 'pull' system of

production.

13

5.8 Single Piece Flow

One-Piece Flow is a Lean Manufacturing methodology derived from the Toyota

Production System. TPS emphasizes right-sizing your batches because batches that aren’t

the right size creates waste. Forms of waste include queues, which leads to waiting time,

which leads to poor space or resource utilization, increased Work-in-Process (WIP), and

longer cycle times.

14

6.0 Current State compared with the Future State VSM

6.1 Current State

The layout of the current state of the ESSM production cell appears similar in comparison

to the future state. The current state has come a long way since the initial state in April of

2007. There have been numerous kaizen events to prepare the ESSM cell for the

transformation. Majority of these efforts were geared towards reducing total build time,

and stabilizing the process. As the result of these events, various inspection and testing

procedures have been removed or reduced, and this is reflected in the modified Work

Order cards (which lists the SSPs [Standard Shop Procedures]). Also, many of the

variations embedded in the processing times of different operations have been reduced.

However, the main transition from having a “lean” type of flow has not been introduced

to the ESSM cell.

Figure 6.1 Simplified Bill of Materials for ESSM Control Section

15

Figure 6.2 Overview of Current State ESSM Cell Processes

16

Figure 6.1 shows the Bill of Material of the ESSM control section unit built at

Honeywell. The final product requires four subassemblies. The subassemblies include

one Electrical Control Unit (ECU), one Harness, one Actuator, and one Adaptor Plate.

The ECU requires 7 different circuit card assemblies which are outsourced. The circuit

card assemblies and raw material (numerous mechanical and electrical parts) which may

be used throughout the cell are outside the scope of this thesis and will not be modeled in

the simulation.

The overview of the current state ESSM processes is shown in figure 6.2. The central

planning system receives the customer requirements information including the quantity

and the date. Based on the calculated release times from planning, production is executed.

The completed subassemblies are stored in the stock rooms until they are released again

for the control section assembly. This method not only reduces the coordination of

material flow between the final assembly and subassemblies but also increases the

inventory cost significantly. Within an assembly line (i.e. ECU line or CS line) the

material flow follows the conventional push method.

The current state of the ESSM production cell has a single line assembly procedure. This

type of assembly is carried out by one assembler from release of the unit until

completion. This method requires attention from the management in order to plan and

make efficient use of the human resources. At times, multiple units are released to the

production cell. Frequently, this causes hikes in the work-in-progress inventory of the cell

as well as the cycle time of each unit. Also, having these constant manual interventions

17

by the management makes the job of the production planner more difficult, as it requires

further attention to details and precise timing of execution. The manual management and

requirement of precise execution are few factors which may lead the cell to underachieve

their goals. Finally the flow of the push system in the current state causes poor

coordination of the assembly steps.

In the future state many of the flaws of the current state are attempted to be rectified

utilizing lean methodologies. First, groups of assembly work procedures are created such

that each group is under the takt time. This ensures that there will be a constant rate of

output of the control section. In order to achieve the highest possible efficiency, workload

balancing must be achieved. Secondly, each group of workstations must be enforced with

single piece flow. This will make sure that the line is able to sustain work-in-progress that

is as low as possible and provide a short cycle time. Finally, the kanban tools are used to

make sure that there is sufficient coordination among different work groups. This will

also ensure that the subassemblies are building to the requirements of the control section.

The kanban plays as a critical factor in providing reinforcement to single piece flow.

Although there is more detailed minor transitions in the future state model (i.e.

ergonomic workstations design) the above description entails the main changes which

will generate most of the improvements.

18

6.2 Future State Design

The design of the future state cell is based on the takt time. As explained in the above

technical review section, the takt time is the maximum time allowed to produce a product

in order to meet the demand. Therefore it is essential that each group of tasks considered

is completed under the takt time. The calculated takt time for ESSM production was 6.5

hours. If for a certain circumstance this takt time cannot be achieved then a batching will

be required. Production leveling is also very crucial. Each of the processing times of the

group of tasks should be balanced as close to each other as possible, as this will be a

significant factor in determining the efficiency of the production cell.

The ESSM production cell begins with 4 subassemblies which will be required for one kit

of the final product, the control section. The first subassembly is the ECU which acts as

the “brain” of the control section. All materials required such as circuit cards, screws and

other mechanical parts are assumed to be available for use. The first step of the ECU

build is the manual stack assembly. The ECU unit then goes into a Burn-in which

simulates and tests the performance of the unit under extreme circumstances. The

problem with this part of the assembly is that this activity takes 32 hours. Because of this

monument which cannot be broken down, batching is required. Batching allows the

production to meet the demand requirement. This particular task allows for up to 6

ECU’s to be produced in a batch. Determining the optimal batch size for this process is

one of the objectives for this paper. After completion of the final inspection, the ECU

units will arrive in to the control section supermarket.

19

The other 3 subassemblies, harness, actuator, and the adaptor plate have a combined

processing time of approximately 210 minutes on average which is less than the takt time

of 6.5 hours. Therefore all three assembly steps are combined as a group in order to

maintain a single piece flow within the group. This allows only one labour resource to be

allocated to this set of workstations.

The supermarket should provide all the required materials for the control section mainly

the four subassemblies. A replenishment point for the items in the supermarket should be

decided, taking into consideration the maturity (or the lack thereof) of the production cell.

Currently, the future state VSM is designed with 3 different work groups for the control

section build. Each of the work groups, as well as the subassemblies should be triggered

by the kanban signal which is provided by the next group of tasks upon their use of the

completed material. The final driver of the kanban throughout the cell is the ‘shipper

products certificate’ which requires processing of one unit every 6.5 hours.

Please refer to Appendix A for the Future State VSMs

20

7.0 Simulation Architecture and Design

As mentioned previously, the simulation model for the ESSM cell was created using

Simul8 software. The reason why this software was chosen over other simulation

packages is because both members of the team have past experience in using this

application (through prior course work) and also, because this tool is housed by the

University of Toronto and endorsed by the team’s supervisor, technical assistance from

the Simul8 team is readily available.

Secondly, the team has programmed several of the routing functions in the simulation

model using Visual Basic code. An example of the routing logic in Visual Basic code is

the ‘Kanban’ system in the ESSM cell – where work is only started at a work center after

it receives a signal from a work center further ahead in the process.

Please refer to Appendix F for Simulation Visual Basic Code Examples.

The Simul8 model was created based on “the intermediate state” of the Honeywell

production cell. The intermediate state here refers to the state where the material flow

system and production logic is based on lean manufacturing, yet the processing times in

the model is derived using the historical data provided by Honeywell.

7.1 Clock Time The model has been created with a 5 day week (Monday to Friday), i.e. the model runs

continuously without weekends, with each day starting at time 00:00 and a length of 24

hours. The reason for using a 24 hour day, as opposed to just 8 hours (1 shift) is because

21

one of the workstations, "ECU Burn-In", has a processing time of 32 hours and runs

continuously even during the non-working hours. For instance, ECU Burn-In will

continue over the weekend if its operations are not completed by the end of shift on

Friday. This is further explained in part 2) of section 7.6 – Assumptions and Model

Limitations. The model has a warm-up period of 28800 minutes (operating time for one

month: 20 days x 24 hours x 60 minutes) and a result collection period of the same time

period. The reason for the warm-up period was to allow for smoothing of the process (i.e.

to allow the system to reach its steady state) after the initial start-up of production, and

thus provide more accurate results.

7.2 Takt Time and grouping Based on the information provided by Honeywell, the demand is known as twenty eight

units without much variation for the foreseeable future. The production shift in this cell is

approximately eight hours per day with two fifteen minute breaks and a half hour lunch.

Given the following information the takt time was easily determined.

unitunithoursmonthunits

monthdaysdayhrsdTotalDeman

leTimeNetAvailabTaktTime min/300/5/28

/20/7==

×==

This is the takt time derived by using 7 available hours per day, 20 work days in 4 weeks,

and 28 units of customer demand. This signifies that the line should be designed such that

it will be able to deliver a unit every 5 hours (300 minutes). This can be interpreted as a

more conservative figure in comparison to the 6.5 hours takt time that Honeywell have

derived in its past kaizen events.

22

Based on the takt time calculated above, we were guided to design a production process

so that a unit would be worked on in a group for maximum of 300 minutes and then

passed on to the next group. Using the distributions obtained through the Stat::Fit

application in Simul8 we were able to identify the proper groupings in order to meet

the 300 minute takt time. It must be made sure that this takt time is not exceeded in each

work group. Please refer to Appendix B for Distribution Fits used in the Model.

Please refer to Appendix D for a screenshot of the lean model with a takt time of 300

minutes. Graphics used in the models are defined in Appendix C. For example, the first

group in the final assembly contains the workstations: Picking for CS and CS Assembly.

These two workstations' average processing times add up to 209 minutes. However the

next workstation which is Solder Battery Wires cannot be added to this work group

because doing so will make the this work group to have average time exceeding the takt

time of 300 minutes. All workstations are grouped using this methodology. One possible

problem this method may cause is reduced efficiency and increased queue time, in

comparison to the ideal case of having exactly 300 minutes of processing time in all work

groups. This possible problem can be dealt with pursuit of continuous improvement. The

manager of this cell must identify processes that may have room for further processing

time reduction. Once the reduction allows the first three workstations to sum up to

processing time of less than 300 minutes, the three workstations would merge.

This continuous effort is recursively carried out for all workstations in order to always

improve their efficiency.

23

Overall, this methodology follows the Toyota production system’s principle allowing

higher capacity than actual demand in order to prepare for the fluctuation in demand. The

production cell team leader is expected to coordinate the resources allocation to fine tune

the production to meet the demand.

7.3 Distribution Fits In order to ensure that our model depicted a true picture of the current system,

workstation processing times were statistically derived with actual historical data. Two

months worth of data for actual operator touch time for all operations of the ESSM

production cell was collected. Work Order cards for the ESSM cell, which lists the SSPs

(Standard Shop Procedures) were then used to group the operations together to determine

the various workstations in the model. The collected data and the respective grouping of

operations were then used with the Stat::Fit for SIMUL8 application to determine the

distribution of processing times for each workstation. This ensured that an aspect of

variance was embedded in the model, and thus reflected a more realistic system.

Once the data was run through the Stat::Fit application, several distributions were

suggested. The following methodology was used to determine the appropriate fit:

1. Take the highest ranked “Do Not Reject” distribution

2. If all distributions are “Reject”, take the one with the highest rank. (Only 3 of 10 fall in

this category).

Please refer to Appendix B for Distribution Fits used in the Model.

24

7.4 Logic

Although the Honeywell production cell does not plan to work with the physical cards

(kanbans), they will use a supermarket and the two bin system in order to achieve a pull

system. However, the simulation model was created in order to mimic the supermarket

and the bin information system through the use of conventional kanbans.

First, the supermarket (called “CS Supermarket” in all model screenshots in Appendix D)

is used at the end of all sub-assembly production. This material requirement is supplied

through the use of this supermarket which simulates the retail supermarket in the

conventional sense. The end unit or Control Section (CS) requires 1 ECU unit, 1 Harness

Assembly, 1 Actuator Assembly, and 1 Adaptor Plate. The supermarket location will

supply the CS production line with all of these required materials. When a set of

materials is taken by the CS production line, it causes the preceding lines to replenish the

materials. This is done using Visual Basic code as mentioned earlier. In this

production cell the supermarket also play another significant role which is the balancing

of the parallel sub-assembly production. The ECU assembly possesses a monument

process, ECU Burn-In, which must be processed in a batch and takes longer than the takt

time of 300 minutes. Therefore the supermarket also allows the ECU line balance its

production with the other three lines.

The CS line will only commence production if all four types of subassemblies and a

kanban are present. This is made sure through the use of ‘Collect when all materials are

available’ logic in Simul8. The production then begins and once the work group

25

completes one unit, it is placed in the queue for the first workstation of the next

workgroup. Once the unit exits that queue, a trigger is sent to replenish the kanban for the

first work group. Similar logic is used to simulate the pull system by kanban in

every work group. As a kanban is made available and if all required materials are

available, the work group is able to allow the next unit to be processed. The simple visual

basic code is included in Appendix F.

7.5 Resource Allocation and shifts

A key aspect in lean production is to produce quality products in the right quantity with

the least number of resources such as machines, space, time as well as human effort. As

part of the goals for this thesis, determining the optimal number of resources (or workers)

in the new ‘lean’ cell was required.

Three types of resources have been defined in the model based on their skill sets:

assemblers, Technicians and Inspection Shipping. A technician is required for

workstations “Zero Pots”, “Post Shroud Test”, "Functional Vibration Test" and "Final

Test". Inspection Shipping is required for workstations "II12 Inspection" and "Shipper

Production Certificates". Assemblers are used on all other workstations in the cell and are

named differently depending on the where they are assigned – for example resources

named ‘ECU Assembly’, ‘Subassembly’, ‘Resource for Group x’ are all of type

assembler. A single assembler is assigned to each grouping of workstations in a pull. For

example, ‘Resource for Group 2’ in the model with a takt time of 300 minutes is assigned

to the second group of workstations that include "Solder Battery Wires", "II49

Inspection" and "Shroud Install". Please refer to Appendix D for the model screenshots.

26

Although operating hours for the plant are from 8am to 4pm, it is required that all

workers receive scheduled breaks. This includes a half hour lunch and two fifteen minute

breaks. The model has used shift patterns and assigned them to the various resources in a

way that ensures that production is never completely stopped.

Please refer to Appendix E for Resource Patterns for Single Shift Operations.

7.6 Assumptions and Model Limitations 1) The most critical assumption which may reduce the validity of the simulation model is

the fact that we assumed material feeding into the cell is has no variation, i.e. material is

always available.

2) There will be no incomplete tasks at the end of a shift. This is controlled through the

‘Shift Behavior’ in the model which allows tasks to complete. The reasoning behind this

is that there should be no incomplete work items at the end of shift, i.e. the

worker/workstation should finish tasks once started.

3) The model is designed such that production is only carried out 5 days a week (no

weekends).

4) All workstations are assumed to have machine efficiency of a 100% and no downtime

due to breakdowns or repair time.

5) All testing stations have a 100% output yield, i.e. no defective material is produced in

the cell.

27

8.0 Analysis

This section of the thesis will take a closer look at the performance of different models.

First comparison will be between the base pull model with takt time of 300 minutes and

the push model. The second comparison will analyze the different “playbooks” which

assumes a particular scenario and the production line design based on that scenario.

8.1 Criteria for Analysis The following criteria were used to analyze performance of each model:

1) The number of completed units

This is the number of units which have been completed in the given production cycle.

The goal is to meet the monthly requirement of 28 units in all models. Although lean

principles forbid a cell to overproduce, the simulation model should be able to convey the

fact that each model is able to fulfill the requested demand. In order to collect this data,

simulation models were run for ten trials and the average of the completed units were

taken.

2) Average Production Lead time

Production lead time is the total time a unit spends in the system starting at release of the

sub-assembly (the most time consuming sub-assembly if being worked on in parallel)

until the unit is shipped to customer. Lean manufacturing aims to shorten the lead time

for its products, so that it can be more responsive to fluctuating customer demands. The

value for production lead time was calculated by taking the difference between the time a

unit enters the first workstation (ECU stack assembly) and the time it exits the last

workstation (Shipper Production Certificates). This was calculated for all completed units

28

and its average was taken to determine the Average Production Lead Time. This

value includes processing times, queue times as well as accounts for the non-operational

hours (i.e., time from 4 pm to 8 am the next day) when production has stopped. The

average production lead time is thus the total time it takes to build the product starting

from the release of the ECU subassembly (the subassembly with the greatest processing

duration) down to the completion of the final unit.

3) Total processing time

The value of total processing time in the system is the total time the unit is actually being

processed on the workstations. This metric is important to show how the variance in the

distributions affects the value of the total processing times. The average total processing

time should not vary significantly when the model is changed from a push to a pull

model, or even amongst models with different takt times. Due to the fact that the model

has a 24 hour clock, the difference between the entry and exit times for a workstation

could not simply be taken to determine its processing time. This is because the time

difference may include non-operational hours (4pm to 8am). For example, a unit may

have entered a workstation at time 900 minutes (Day1, 3pm) and left the workstation at

time 2040 minutes (Day2, 10am). The difference in entry and exit times would be 1140

minutes or 19 hours, where as the true processing time is only 3 hours. In order

to overcome these superfluous time differences, a time scaling activity was carried

out. All operating time units - time between 8am to 4pm each day- had to be rescaled for

the total operational time available in two months (warm-up and result collection

period). That is, time units were converted to a scale from 0 to 19200 minutes (2months x

20days x 8hrs x 60 min). For example, Day1 8am is simulation time 480, however, that

29

would be rescaled to time 0. Similarly Day2 8 am, which is simulation time 1920, would

be rescaled to time 480. After having rescaled all the operational time, the difference

between the entry and exit time was taken for each workstation (except for "ECU Burn-

In", which runs for 32 hours straight) and then the values were summed up to get the total

processing time for a unit. This was calculated for all completed units and its average was

taken to determine the Average Total Processing time in the system.

4) Total queue time

The total queue time refers to the accumulated waiting time in the system. The queue

time in the system is a critical measurement in terms of measuring the "leanness" of any

manufacturing cell. The lean manufacturing system is designed so that the undesired

queuing time is reduced by various tools and concepts. This metric is

obtained by accumulating the average time a unit spends in each queue - either in a queue

waiting for a workstation, or in a storage bin waiting to be picked.

5) Work In Progress (WIP) inventory at the end of two week period

The WIP inventory refers to all units in production that is either being worked on at

workstation or waiting in a queue. Ideally the least WIP inventory is desired. This WIP

inventory figure is obtained from the model by manually counting the number of units in

the system. It should be noted that one set of subassemblies (ECU, harness assembly,

actuator assembly, and the adaptor plate) were considered as only one unit when

counting. The WIP inventory levels were counted at the end of the first, second and third

quarters into production. The average was taken in order to get the final results for WIP

inventory.

30

8.2 Model Data Collection

Some of the results generated directly from SIMUL8 are inaccurate due to the following

reasons. Firstly, they include the time the system is not operating in its calculations.

The percentage of time working (or utilization) for workstations is one of these

skewed metrics. For example, workstation "Shroud Install" only has a percentage of time

working of 3.36 according to SIMUL8. This value is so low because the total time

is taken as 28800 minutes not the true operational time of 9600 minutes (20days x 8hrs x

60 min). Secondly, because there are 4 subassemblies that are put together to create the

final product the minimum and average time in system for completed work items

according to SIMUL8 is incorrect. This is because SIMUL8 takes the time in the system

for each of the subassemblies (before they are picked and combined with other

subassemblies) as time for a completed work item. So for instance, the minimum time in

system shows as 167 min and the average as 922 min, however both these values are

smaller than 1920 minutes which is the standard processing time for the single

workstation of "Burn-In".

In order to attain proper meaningful results, data had to be collected from the model,

analyzed and modified, before conducting calculations. For each workstation in the

process, data regarding the time a unit was entered and the time that unit exited each

workstation was collected. (The harness, actuator and adaptor plate assemblies were not

included as they run parallel with the ECU assembly which has a much longer processing

time). Please refer to Appendix F to see the Visual Basic code used for data collection.

31

8.3 Conventional Push System compared with the Lean Pull System

As an affirmation that lean transformation will be beneficial for this existing production

cell we have decided to create a simulation which possesses the conventional push

system. This push system will utilize the central planning system (i.e. Material

Requirements Planning (MRP) and Master Production Scheduling (MPS)) in order to

prepare a production schedule which will be used through the entire production

process. Production may release multiple units at a time, in order to benefit from the

economies of scale. In the push system simulation model 7 units will be released at the

beginning of the week, feeding material into the sub-assembly level. Seven units per

week will meet the demand 28 units a month. The chart below shows the deterministic

attributes that were used in the model design.

Table 8.1 Deterministic Attributes of Push and Pull Model Deterministic Attributes: PULL 300 PUSH Net Available Time 8400 8400 Demand 28 28 Takt 300 N/A Total # of Resources 9 11 # of Assembly Resources 6 8 Results Collection Period 28800 28800 Warm up Period 28800 28800 Burn-in Batch Size 6 6 Shift 1 1 # of CS Assembly Work Groups 5 N/A Expected WIP inventory 11 N/A

The push model has similar attributes as the pull model. The main difference on the push

model is the number of resources. This shows that the push system will have a greater

cost compared to the pull counterpart.

32

Expected WIP inventory for pull models is calculated based on two assumptions. First,

subassembly workgroups will hold six units of WIP inventory due to the batch size of

burn-in. Second, due to single-piece flow within each workgroup, there will be one unit

of WIP inventory per workgroup. For the push model, there is no clear method of

calculating the expected WIP inventory.

8.3.1 Number of units completed Results Collected: PULL 300 PUSH Average number of units completed (10 trials) 30.8 28.7

The ability to meet the customer demand has never been a problem in a conventional

push manufacturing system. The simulation confirms this theory by proving that both the

pull system and push system is able to meet the demand of 28 units. The pull system had

capacity exceeding the requirement of 28 units per month. The simulation model shows

that the cell is able to produce approximately 31 units once the cell is at steady state. This

is achieved through having a month of warm up period to get the production at a steady

state, and the subsequent month was used to obtain results. Although the pull system is

capable of producing more than 28 units, kanbans can be used as a tool to control the

number of units to be produced each month to avoid overproduction. The team has

performed an exercise to make sure that the last kanban post in the production cell pulls

at the precise pace of the takt time. This will simulate the reality where the production

manager or worker at the last workstation will ensure the cell produces at takt time by

releasing a kanban only at the specified takt time. This mechanism will ensure that no

more than 28 units will be produced in a given month.

33

The push system is also able to meet the demand of 28 units per four weeks. The central

planning only releases 7 units to each subassembly at the beginning of the week, and

prevents overproduction. However, one must note that this is the ideal push scenario.

Often in reality, many of the central planning activities can lose accuracy during the

course of product cycle. Nevertheless, the simulation model shows that the push model is

capable of producing more than 28 models.

It is quite clear by the two simulation models that both push and pull system possess

greater capacity than desired by the customer. However, the push system meets the

demand by feeding the anticipated customer demand, whereas the pull system fine tunes

the pace of the production to meet the customer demand. Therefore pull system is able

to be flexible and meet rapid changes of the customer when needed.

8.3.2 Average Production Lead TimeResults Collected: PULL 300 PUSH Production Lead Time (minutes)

8174.7 (5.7 days)

8864.3 (6.2 days)

The production lead time for the push model is approximately 10% higher than the pull

model. Having a longer lead time may have multiple effects. First, the inventory cost will

increase as the units will stay in the production line as WIP for longer duration. Also, a

longer production lead time reduces the cell’s ability to smooth production. It is therefore

clear that the pull system with a shorter production lead time, will be more

responsiveness to fluctuation in customer demand.

34

8.3.3 Average total processing time in the systemResults Collected: PULL 300 PUSH Average Total processing time in the system (minutes) 2963.1 2924.1

The total processing time in any model should not have much difference as they are all

from same set of distribution. This result confirms the hypothesis that they have very

close total processing time.

8.3.4 Average total queue time in the system Results Collected: PULL 300 PUSH Total queue time in the system (minutes) 5420.9 7752.1

The result shows that on average every unit in the pull system spends about 5400 minutes

waiting for something to be done, where as push system spends well over 7700

minutes waiting. This is a clear advantage of using the pull system over the push system.

By achieving one piece flow and coordinating schedules on each work station the queue

time is reduced. Reduced queue time in the system results in further benefits through

reduced WIP inventory cost.

8.3.5 WIP inventory (at 2 weeks into the model) WIP PULL 300 PUSH

1Q WIP Level 8 12 Halfway WIP Level 10 15

3Q WIP Level 8 12 Average WIP Level 8.7 13.0

The results show significant difference in the WIP inventory, where the push system is

higher than the pull. The pull system had 8.7 units on average in the system while the

35

inventory in the push system was comparably higher up at average of 13 units. This

correlates with the fact that push system had a longer production lead time as well as a

higher queue time. The amount of WIP inventory between the two systems is quite large,

significantly impacting the difference inventory cost.

8.3.6 Discussion Overall, both the pull and push systems complete the job by fulfilling the customer

demand. However, as observed in the queue time and WIP inventory comparison, the

cost of inventory for a push system should be significantly higher compared to its pull

counterpart. Also, the resource cost for the push is higher compared to the pull. Therefore

the lean transformation which will convert the material flow from a conventional push

system to Toyota Production System inspired pull system will be certainly able to

successfully reduce significant amount of cost. Furthermore, this analysis successfully

shows that the pull system does in fact meet the goals of TPS which is to reduce cost

through the use of fewer resources and lower inventory. Also, the pull system achieves a

lower production lead time which is a key aspect of lean manufacturing. As a result of

this analysis, it is can be seen that Honeywell will be able to reap benefits with a lean

transformation on the ESSM cell.

36

8.4 Comparison of “playbooks” based on varying takt time

The management at Honeywell was interested in knowing the lean production cell's

ability with varying takt times. As explained in previous sections, takt times may vary

based on two factors which are the net available time, and the customer demand. The

following scenario was considered as an example. Given a situation where the cell has

lost net available time through unforeseen problems such as material availability or

downtime due to machine failure, the question of how the production cell should be

redesigned and still meet customer demand is raised. This is also referred to as

"playbooks".

In this study, two alternate takt times are considered. The first playbook model assumes a

situation where the cell has lost a week of production time, and now only has three weeks

to fulfill customer demand of 28 units. The takt time is calculated as follows:



leTimeNetAvailabTaktTime min/225/75.3/28

/15/7==

×==

The second playbook, considers a relaxed situation where the production cell has 23

working days with 8 hours per day to produce instead of the 20 days with 7 hours per day

in the base model. The takt time is calculated as follows:



leTimeNetAvailabTaktTime min/390/5.6/28

/23/8=≅

×==

37

Table 8.2 Deterministic Attributes of Various Playbooks Deterministic Attributes: PULL 300 PULL 390 PULL 225 Net Available Time 8400 10920 6300 Demand 28 28 28 Takt 300 390 225 Total # of Resources 9 8 9 # of Assembly Resources 6 5 6 Results Collection Period 28800 33120 21600 Warm up Period 28800 28800 28800 Burn-in Batch Size 6 6 6 Shift 1 1 1 # of CS Assembly Work Groups 5 4 6 Expected WIP inventory 11 10 12

The attributes for the pull system with a takt time of 300 minutes remain the same. The

pull system with a takt time of 390 minutes has one less work group, resulting in one less

assembler as well as a lower expected WIP inventory. Also, its results collection period is

longer because it is run for a larger time period. The pull system with a takt time of 225

minutes has one more work group compared to the base model, resulting in a higher

expected WIP inventory. Although there are more workgroups, the numbers of

assemblers do not increase due to one grouping of workstation which requires only

resource of type technician. Also, its results collection period is shorter because it is has

lost one week of production time.

Please refer to Appendix D for model screenshots to better understand the analysis below.

8.4.1 Number of units completed Results Collected: PULL 300 PULL 390 PULL 225 Average number of units completed (10 trials) 30.8 28.5 32.3

As the numbers clearly show with the team was able to verify that the capacity of all

three designs were able to meet the production requirements of 28 units. As explained in

38

the description of the criteria, this capacity can be controlled by the use of kanban both in

the simulation model and in reality. In conclusion, at the end of each run all three play

books will able to produce to meet demand without over-producing.

8.4.2 Average Production Lead TimeResults Collected: PULL 300 PULL 390 PULL 225

Production Lead Time (minutes)

8174.6 (5.7 days)

9280.6 (6.4 days)

8489.2 (5.9 days)

The average production lead time for the 3 playbooks had small correlation with its takt

times. The base model has 5.7 days of production lead time as mentioned in the previous

comparison. The pull model with takt of 390 minutes showed production lead time of 6.4

days which was significantly higher than expected. The initial expectation of the

production lead time for the pull 390 model was to have the least amount of lead time

among the three as there are very few places where WIP can be in queue. The team has

few speculations about this result. First of all, the earliest control section work group

where the subassemblies required has a significantly long processing time (362 minutes).

This makes the completed subassemblies, mainly ECUs which are produced in batches,

wait in the queue for an extended period of time before being picked. Secondly, the work

group processing times are not as evenly balanced compared to the other two models.

These two factors coupled together may have resulted in this unexpectedly high

production lead time for the Pull model with takt time of 390 minutes.

The pull model with takt time of 225 minutes has production lead time greater than that

of the base model as expected since it has more work groups and thus more opportunities

for queue. However, the difference is not very large which means that the cell is flexible

and responsive to varying takt times without a significant increase in overall lead time.

39

8.4.3 Average total processing time in the system Results Collected: PULL 300 PULL 390 PULL 225 Average Total processing time in the system (minutes) 2963.1 2967.4 2959.8

The total processing times in the system are quite similar for all three playbooks as

expected. This result confirms the hypothesis that they have very close total processing

time.

8.4.4 Average total queue time in the system Results Collected: PULL 300 PULL 390 PULL 225 Total queue time in the system (minutes) 5420.95505 6687.70295 5675.00363

The results show a correlation with the results attained for the production lead time. It

must be noted that total lead time is comprised of total processing time and queue time.

Due to the fact that total processing times have little variation, the difference in lead time

is a result of varying queue times. Therefore, the results attained and the analysis for

queue time and production lead time are quite similar.

For the model with takt time of 390 minutes the speculation discussed previously where

subassemblies wait in queue before being picked holds true, resulting in a large queue

time. For the model with takt time of 225 minutes, the larger number of work groups

result is more opportunities for queues and thus its total queue time is slightly higher than

the base model with 300 minute takt time.

40

8.4.5 WIP inventory Results Collected: PULL 300 PULL 390 PULL 225 1Q WIP Level 8 8 10 Halfway WIP Level 10 7 9 3Q WIP Level 8 8 10 Average WIP Level 8.7 7.7 9.7

The results have shown that WIP inventory behaves in an expected manner. The model

with highest takt time has the least amount of WIP inventory and the model with the

lowest takt time has the highest WIP inventory level. This is because WIP levels are

dependent on the number of workgroups which is inversely proportional to takt time.

However, it should be noted that each model has a lower Average WIP level than the

expected number of WIP inventory. This result is due to the fact that all three production

cells have variation in their work group processing times, and as a result not every work

group is filled with a unit at a given time.

8.4.6 Discussion The primary focus in comparing the various models is the fact that varying takt times do

not hinder the cell’s ability to meet the demand. The model with the longer takt time

proves be further relaxed in that it has lower number of resources and lower average WIP

levels. However, due to the abnormal behaviour observed, speculations have been made

as to why there is a higher production lead time and queue time.

Furthermore, it is impressive that even when one full week is lost due to a problem the

production line can be rearranged to meet the customer demand. There exist some

consequences of rearranging the production this way, such as increase in production lead

time and costs due to higher inventory. However, the results show that these cost

increases are not very significant. This reiterates the effectiveness in pull material flow

41

systems in dealing with the fluctuation customer demand or strategy on recovering lost

times.

42

9.0 Optimization

Simulation is utilized in this study to serve few specific purposes, one of which is to

optimize attributes which may be critical to the performance of the production line. The

attributes that are in question in this particular production cell are the following:

1) Batch size of Burn-In process

2) Minimum human resources used in 'lean' production to meet customer demand

3) Number of shifts in order to minimize operating cost

However, one must note that these are not typical optimization problems that are solved

through linear programming or other Operations Research techniques. On the

contrary, several trials have been run on the model by varying attribute specifications and

their results have been compared to determine the optimal value.

9.1 Batch Size of Burn-In process

The second process in the ECU sub-assembly must occur at a special location some

distance away from the production cell. Burn-in process is what is known as a monument

process in lean manufacturing. Monument process is a process which cannot be relocated

to support material flow. Although monument processes are highly undesirable in

lean manufacturing processes it may not be avoidable in some cases. This particular burn-

in process is also unique in the sense that it has 32 hours of continuous processing times

which may run overnight. Due to these characteristics of the burn-in process batching is

necessary although it is a practice unappreciated in lean manufacturing. However, in

order to support production smoothing and level loading, the batch size should be

43

carefully considered and planned. It is also important that the pace of other three

parallel sub-assemblies is matched with the burn-in process. In the created simulation

model various batch sizes are tested.

The objective of this optimization exercise is to identify the best batch size of the burn in

operation. The criterion for determining the best batch size is defined as the smallest

batch size that will meet the expected demand. A smaller bath size is preferred because it

lowers WIP levels and improves smoothing of one-piece flow.

The table below shows the resulting production in the three different pull models when

batch sizes of 1 to 6 (the physical maximum) is utilized. It should be noted that for the

models used in the analysis section default batch size of 6 is used as that is the current

rule for production.

Table 9.1 Number of completed units for varying batch sizes in pull models Batch Size Pull 300 Pull 225 Pull 390

1 11.5 8.5 13.5

2 20.1 16 22.6

3 23.5 22.4 23.9

4 27.1 28.4 25.6

5 29.4 29.8 28

6 30.7 32.2 28.8

The results show that for the base pull model with takt time of 300 minutes demand can

be met with only 5 units in a batch. For the pull model with 225 minutes of takt time, a

batch size of only 4 units is sufficient to meet the production requirements. For the model

44

with takt time of 390 minutes the batch size of 5 units allows the model to meet the

demand.

Based on the results obtained by the simulation model, the suggested batch sizes are

lower than the current rule of using a batch size of 6 units. A batch size of 4 units for the

model with a takt time of 225 minutes or 5 units otherwise should be used. However, in

order to standardize operations and build mistake proofing in the process, a

consistent batch size of 5 units can be used alternatively for all models.

9.2 Resource Requirements

The grouping of the production workstations and the allocation of employees must be

adaptable to the fluctuation in customer demand. Production managers must understand

different capacity levels can be achieved by varying the resource requirements. While

being flexible, the number of assemblers required by the production line is an important

contributing factor to the overall cost of production. Thus, the goal is then to determine

the optimal (in this case, the minimum) number of resources that should be used in order

to meet the customer demand.

As mentioned previously in the Simulation Architecture and Design, there are three types

of resources in the model: assemblers, Technicians and Inspection Shipping. In all

models, there is always only one Inspection Shipping resource and it is dedicated to the

last two workstations "II12 Inspection" and "Shipper Production Certificates". Therefore,

optimization of resources has only been conducted on the assemblers and technicians.

45

The determining factor for the optimal solution is the customer demand (i.e. Average

number of units completed must be at least 28 units).

The trials run in this optimization are based on inspection of the model. A group of

workstations with a sum of processing times that is significantly lower than the takt

time has a resource (e.g. assembler) with low utilization. This means that the assembler is

capable of taking on tasks at other workstations outside of its group and thus eliminating

the need for an additional resource. Below is a summary of the trials conducted by

varying the low utilized resource levels for the pull model with a takt time of 300

minutes.

Table 9.2 Optimization Results for Resource Requirements on Pull 300 Model

PULL 300

# of Technicians

# of Assemblers

Average # of units completed Model Description / Changes

2 5 30.7 Base Model

1 5 27.6 remove 1 technician

2 4 27.3 Final Preparation uses Resource for Group 1

1 4 26.7 remove 1 technician, Final Preparation uses Resource for Group 1

2 4 30.1 Final Preparation uses ECU Assembly resource

1 4 27.6 remove 1 technician, Final Preparation uses ECU Assembly resource

Optimal Solution: two technicians and four assemblers are required in order to meet

demand. By having the ECU Assembly resource also work at Final Preparation, no

assembler needs to be dedicated to the fourth workstation group in the model.

46

A similar optimization activity was conducted on the pull model with a takt time of 225

minutes. The summary of trials is as follows:

Table 9.3 Optimization Results for Resource Requirements on Pull 225 Model

PULL 225

# of Technicians

# of Assemblers

Average # of units

completed Model Description / Changes

2 6 29.9 Base model

1 6 26 remove 1 technician

2 5 27.9 Final Preparation uses Resource for Group 3

1 5 25 remove 1 technician, Final Preparation uses Resource for Group 3

2 5 25.7 Final Preparation uses ECU Assembly resource

2 5 29.4 Final Preparation uses pool resource of either ECU Assembly or Resource for Group 3

1 5 25 remove 1 technician, Final Preparation uses pool resource of either ECU Assembly or Resource for Group 3

Optimal Solution: two technicians and five assemblers are required in order to meet

demand. Final Preparation uses a pool resource consisting of ECU Assembly and

Resource for Group 3. This means that both the ECU Assembly and Resource for Group

3 are able to work at Final Preparation, and the first resource that is idle will be assigned

to Final Preparation. This eliminates the need for a dedicated assembler at to the sixth

workstation group in the model.

9.3 Number of Shifts

The last optimization problem is to determine the optimal number of shifts to operate.

Although this is a complex problem, the approach that the team has taken is to do a

47

quantitative comparison as done in the analysis section using the same six criteria. The

team's focus remains in comparison of resource cost, and inventory cost between the two

models.

Table 9.4 Deterministic Attributes of One Shift and Two Shift Pull Models

Deterministic Attributes: PULL 300 PULL 600 - 2

SHIFT Net Available Time 8400 16800 Demand 28 28 Takt 300 600 Total # of Resources / shift 9 7 # of Assembly Resources / shift 6 4 Results Collection Period 28800 28800 Warm up Period 28800 28800 Burn-in Batch Size 6 6 Shift 1 2 # of CS Assembly Work Groups 5 3 Expected WIP inventory 11 9

The above table identifies few of the key attributes that changed in the two shift model.

First, the net available time has doubled accounting for its two 8 hour shifts per each

work day. With this increase in the net available time the two shift model operates under

a takt time of 600 minutes. The increased takt time allows for more work stations to be in

the same work group, thus the two shift model is allowed to operate with two less

resources per shift. However, as the two shift model will need two sets of these

resources, in reality operating two shifts increases the total cost of resource. Also, the

duration of operation adds to indirect cost of shared resources such as inspectors and

shippers. Facility cost such electricity and security may be increased due to extended

operation.

48

Table 9.5 Results Collected from One Shift and Two Shift Model Analysis

Results Collected: PULL 300 PULL 600 - 2

SHIFT Average number of units

completed (10 trials) 30.8 29.4 Production Lead Time

(minutes) 7513.9 6679.4 Average Total processing

time in the system (minutes) 2963.1 2961 Total queue time in the

system (minutes) 5420.9 4391.9 Average WIP Level 8.7 7.3

As the 10 trial runs show, both models are able to meet the customer demand of 28. The

total processing times in the two models do not vary by much as expected. However

both the production lead time, queue time and the average WIP level in the simulation

results convincingly show that the inventory cost of the two shift operation will be less

than the one shift operation.

The conclusion on whether the two shifts should be utilized requires further investigation

into details of the cost structure because neither model dominates another in terms of both

resource cost and inventory cost. It is expected that once the detailed costs are understood

it should be clearer which of the two modes result in further cost reduction.

49

10.0 Problems Encountered

The team had encountered number of problems throughout the course of the project.

First, there were numerous technical difficulties that the team had to face in using the

Simul8 software throughout the project. Although both Mohammad and Sunghoon had

experience with Visual Basic programming, the terms and functions specific to Simul8

program had to be familiarized again. In the process of writing visual basic code in the

model and retrieving data the team was able to get support from Wallace Law who has

experience in the Simul8 software through his work at Visual8. Secondly, although

overall time management in planning and completing the tasks were fairly well handled

by the two members of the team, some of the internal deadlines were missed as both

members were heavily engaged with other responsibilities and commitments. Lastly, the

process of collecting required data was delayed number of times as there were poor

communication between the designated Honeywell individuals and the team. This

delayed the team while attempting to fit the distributions for the models.

50

11.0 Future Considerations This thesis was aimed at creating a detailed simulation model of an intermediate state

Value Stream Map (VSM) of the ESSM production cell. The intermediate state VSM of

the ESSM cell was chosen to be modeled because it was the state the cell would

transform to shortly, and was determined to be more beneficial at the time rather than a

model of the future state VSM. However, as further lean transformations are carried out

over time, there exists opportunity to update the intermediate state model to the future

state VSM. In order to do this, several aspects of the model such as ‘supermarkets’ would

have to be introduced. Secondly, collecting data and fitting statistical distributions must

be reconsidered in order to ensure an accurate model. This is because over time

continuous improvement activities may reduce workstation processing times, or modify

the Standard Shop Procedures (SSPs).

Due to the limitations of the Simul8 Student Edition, the team was unable to make use of

the ‘OptQuest for Simul8’ plug-in in conducting the optimization of attributes in the

model. Rather, several trials were run with varying attribute specifications in order to find

the optimal value. OptQuest for SIMUL8 helps find the best answer to "what-if"

questions in a simulation. A future consideration would be to use a version of Simul8

higher than the Student Edition, where OptQuest is available, in order to carry out

accurate and possibly more versatile optimizations.

The scope of this thesis did not allow the current model to account for the costs involved

in production such as raw materials, inventory, resources, machines, downtime etc.

51

Adding a cost structure into the model would definitely be valuable enhancement for the

stakeholders at Honeywell. It would also serve as means for better optimization of

attributes or in determining the alternative (for example, single shift or double shift) that

is most cost effective.

52

12.0 Conclusion

As stated at the outset, the main purpose of this thesis was to support Honeywell in

implementing feasible lean transformations to the ESSM cell via verification of the ‘lean’

cell design. Discrete event simulation was the chosen methodology used to model lean

transformations. The objectives were defined as follows:

• Create a detailed simulation model of an intermediate state Value Stream Map

(VSM) of the ESSM production cell

• Analyze how various operations work at different takt times and determine

work-load balancing of individual work stations within the cell

• Optimize various attributes in the model

The work done in this thesis achieves all of the stated objectives in that the simulation

model designed was based on the intermediate state VSM, several other models with

varying takt time were created and a detailed analysis was performed, as well

as optimization of attributes such as the batch size, resource requirements and number of

shifts was conducted. In addition, the ‘lean’ or pull model was compared with the

conventional push model to further evaluate the effectiveness of the lean transformations.

Several future considerations for the model have been stated and these considerations

would serve to further enhance the use of the model by Honeywell.

In order for Honeywell to be able to make maximum use of the models, several personnel

must be trained on using the Simul8 software. The team is willing to conduct this

training, and thus help Honeywell achieve successful results in their new ‘lean’ ESSM

cell.

53

13.0 References

[1] Yasuhiro Monden (1998), Toyota Production System, An Integrated Approach to Just-In-Time, Third edition, Norcross, GA: Engineering & Management Press.

[2] “Lean manufacturing – Wikipedia, the free encyclopedia”, Accessed 2007 Nov 2,

Available HTTP: <http://en.wikipedia.org/wiki/Lean_manufacturing>

[3] “shmula. Batch-and-Queue or Single-Piece Flow: Business, Technology and stuff in Between”, Accessed 2007 Nov 2, Available HTTP: <http://www.shmula.com/270/batch-and-queue-or-single-piece-flow>

[4] Jeffrey Liker (2003), The Toyota Way: 14 Management Principles from the

World's Greatest Manufacturer, First edition, McGraw-Hill.

54

55

APPENDIX A – Future State VSM

Figures: Future State VSM created during the Kaizen 1

1 2

563 4

57

Figure: Future State VSM 1

58

Figure: Future State VSM 2

APPENDIX B – Distribution Fit used in the Model Table 14.1 List of Workstations with Processing Time Statistical Distributions Workstation Statistical Distribution ECU Stack Assembly Weibull(0., 9.04, 95.6) ECU Burn In Average(1920) ECU Final Inspection Beta(0., 60., 62.4, 86.6) Harness Assembly Average(60) Actuator Assemby Average(90) Adaptor Plate Assembly Average(60) Picking for CS Average(10) CS Assembly Weibull(154, 1.67, 39.7) Solder Battery Wires Beta(0., 180, 8.25, 3.3) II-49 Inspection Pearson 5(0., 5.36, 84.5) Shroud Install Weibull(0., 9.28, 108) Zero Pots Pearson 5(0., 24., 1.61e+003) Post Shroud Test Gamma(0., 19.4, 1.72) Functional Vibration Test Average(105) Final Test Pearson 5(0., 42.9, 3.98e+003) Final Preparation Pearson 5(0., 28.1, 3.85e+003) II-12 Inspection Fixed(25.2) Shipper Production Certificates Average(10)

59

ECU Stack Assembly

Auto::Fit of Distributions

distribution rank acceptance

Weibull(0., 9.04, 95.6) 100 do not rejectLognormal(0., 4.5, 0.141) 33.4 do not rejectPearson 5(0., 48.8, 4.33e+003) 29.1 do not rejectBeta(0., 113, 13.3, 3.5) 20.2 do not rejectPower Function(0., 117, 3.8) 0.103 rejectRayleigh(0., 64.6) 1.3e-003 rejectTriangular(0., 115, 110) 7.82e-004 rejectUniform(0., 113) 0. reject

ECU Final Inspection

Auto::Fit of Distributions


Beta(0., 60., 62.4, 86.6) 40.2 rejectWeibull(0., 2.43, 35.3) 19.1 rejectPearson 6(0., 2.49, 121, 10.8) 18.4 rejectPearson 5(0., 10.5, 291) 16.9 rejectLognormal(0., 3.37, 0.341) 11.9 rejectRayleigh(0., 24.) 8.67 rejectGamma(0., 7.48, 4.17) 7.26 rejectTriangular(0., 69.9, 24.3) 6.56 rejectErlang(0., 8., 3.9) 4.57 rejectUniform(0., 60.) 0.505 rejectChi Squared(0., 30.1) 0.121 rejectPower Function(0., 60., 1.38) 8.49e-002 rejectExponential(0., 31.2) 2.08e-003 reject

60

CS AssemblyAuto::Fit of Distributions


Weibull(154, 1.67, 39.7) 95.4 do not rejectRayleigh(150, 31.4) 87.6 do not rejectChi Squared(-43.1, 232) 93.6 do not rejectGamma(149, 3.25, 12.4) 78.8 do not rejectLognormal(130, 4.01, 0.367) 77.8 do not rejectErlang(149, 3., 13.4) 76.9 do not rejectPearson 5(120, 10.7, 673) 73.6 do not rejectPearson 6(156, 114, 3.26, 11.6) 72.1 do not rejectNormal(189, 21.6) 64.7 do not rejectBeta(156, 326, 2.12, 8.23) 59.9 do not rejectTriangular(152, 253, 165) 54.3 do not rejectExponential(156, 33.2) 12.4 do not rejectPower Function(155, 242, 0.827) 2.72 do not rejectUniform(156, 242) 0.191 reject

Solder Battery WiresAuto::Fit of Distributions


Weibull(0., 5.61, 145) 100 rejectGamma(0., 23.7, 5.65) 66. rejectErlang(0., 24., 5.58) 62.5 rejectPearson 6(0., 102, 52.8, 41.1) 61.7 rejectLognormal(0., 4.88, 0.208) 61.4 rejectPearson 5(0., 23., 2.94e+003) 57.7 rejectBeta(0., 180, 8.25, 3.3) 6.87 do not rejectTriangular(0., 189, 176) 1.56 rejectPower Function(0., 183, 2.97) 1.09 rejectChi Squared(0., 132) 5.35e-002 rejectRayleigh(0., 96.5) 1.06e-002 rejectExponential(0., 134) 9.7e-007 rejectUniform(0., 180) 0. reject

61

II-49Auto::Fit of Distributions


Pearson 5(0., 5.36, 84.5) 100 do not rejectPearson 6(0., 13.4, 12.5, 9.76) 74.1 do not rejectLognormal(0., 2.85, 0.438) 69.7 do not rejectErlang(0., 5., 3.82) 43.2 do not rejectGamma(0., 5.33, 3.58) 40. do not rejectRayleigh(0., 14.9) 36.4 do not rejectChi Squared(0., 18.3) 29.4 do not rejectWeibull(0., 2.26, 21.6) 23.4 do not rejectBeta(0., 2.04e+003, 4.68, 489) 20.6 do not rejectTriangular(0., 51.1, 11.1) 2.82 do not rejectExponential(0., 19.1) 2.04e-002 rejectUniform(0., 48.) 1.39e-002 rejectPower Function(0., 102, 0.564) 7.27e-006 reject

Shroud InstallAuto::Fit of Distributions


Weibull(0., 9.28, 108) 100 do not rejectChi Squared(0., 103) 36.7 do not rejectPearson 5(0., 96.1, 9.8e+003) 28.9 rejectPearson 6(0., 58.7, 258, 148) 28.4 rejectLognormal(0., 4.63, 0.104) 28.2 rejectGamma(0., 90.6, 1.14) 27.4 rejectErlang(0., 91., 1.13) 27. rejectBeta(0., 120, 3.77e+003, 955) 8.4 rejectPower Function(0., 120, 6.33) 7.28 rejectRayleigh(0., 73.3) 0.344 rejectExponential(0., 103) 6.52e-002 rejectTriangular(0., 138, 110) 4.68e-002 rejectUniform(0., 120) 7.7e-007 reject

62

Zero PotsAuto::Fit of Distributions


Pearson 5(0., 24., 1.61e+003) 100 rejectLognormal(0., 4.23, 0.217) 56.3 rejectWeibull(0., 3.57, 77.6) 43.9 rejectPearson 6(0., 273, 25.3, 99.2) 38.1 rejectErlang(0., 19., 3.71) 30.1 rejectGamma(0., 19.1, 3.69) 29.3 rejectBeta(0., 135, 9.4, 9.01) 9.91 rejectChi Squared(0., 69.7) 3.29 rejectTriangular(0., 142, 61.5) 1.42 rejectRayleigh(0., 51.5) 0.108 rejectUniform(0., 135) 5.39e-002 rejectExponential(0., 70.5) 3.06e-005 rejectPower Function(0., 139, 1.42) 0. reject

Post ShroudAuto::Fit of Distributions


Gamma(0., 19.4, 1.72) 93.4 do not rejectErlang(0., 20., 1.67) 93.2 do not rejectPearson 6(0., 259, 22., 172) 90.2 do not rejectLognormal(0., 3.48, 0.227) 74.6 do not rejectChi Squared(0., 33.4) 72.9 do not rejectPearson 5(0., 19.8, 626) 54.1 do not rejectBeta(0., 60., 13.1, 11.2) 49.2 do not rejectWeibull(0., 4.09, 36.4) 48.4 do not rejectTriangular(0., 62.9, 30.3) 7.79e-002 rejectRayleigh(0., 24.2) 1.38e-002 rejectUniform(0., 60.) 2.98e-003 rejectExponential(0., 33.3) 1.78e-005 rejectPower Function(0., 168, 0.608) 0. reject

Final TestAuto::Fit of Distributions


Pearson 5(0., 42.9, 3.98e+003) 83.5 rejectWeibull(0., 5.19, 103) 73.9 rejectLognormal(0., 4.54, 0.158) 59.7 rejectPearson 6(0., 405, 47.9, 205) 47.4 rejectGamma(0., 37.8, 2.52) 42.1 rejectErlang(0., 38., 2.51) 41. rejectChi Squared(0., 95.) 17.1 rejectBeta(0., 150, 17.4, 10.6) 15.6 rejectTriangular(0., 158, 93.5) 1.67 rejectRayleigh(0., 68.4) 0.311 rejectExponential(0., 95.2) 1.92e-004 rejectUniform(0., 150) 0. rejectPower Function(0., 200, 1.32) 0. reject

Final PrepAuto::Fit of Distributions


Pearson 5(0., 28.1, 3.85e+003) 100 do not rejectLognormal(0., 4.94, 0.189) 87.7 do not rejectPearson 6(0., 215, 46.4, 71.3) 84.9 do not rejectErlang(0., 28., 5.08) 78.6 do not rejectGamma(0., 28.1, 5.05) 77.4 do not rejectWeibull(0., 5.74, 153) 65.6 do not rejectBeta(0., 190, 4.57, 1.53) 20. do not rejectPower Function(0., 190, 3.27) 4.55 rejectChi Squared(0., 141) 0.289 rejectTriangular(0., 196, 188) 0.103 rejectRayleigh(0., 102) 7.45e-004 rejectExponential(0., 142) 4.72e-007 rejectUniform(0., 190) 0. reject

64

APPENDIX C – Icons used in Simul8

Simulation Graphic Description

Entry Point

Work Complete

Resources

Queue

Workstation

(Assemblers Required)

Test Stations

(Technicians Required)

Kanban Post

65

66

APPENDIX D – Simul8 Model Screenshots

Pull model with takt time of 300 min

Push model

68


69


70

71


APPENDIX E – Resource Patterns for Single Shift Operations TYPE A TYPE B TYPE C

8:00 8:30 9:00 9:30 BREAK BREAK 10:00 BREAK 10:30 11:00

11:30 LUNCH 12:00

LUNCH

12:30

LUNCH

13:00 13:30 BREAK 14:00 BREAK BREAK 14:30 15:00 15:30 16:00

APPENDIX F – Simul8 Visual Basic Code Examples ‘ss_result’ - a defined spreadsheet in the model that is used to collect entry and exit times for each unit on each workstation Add Work To Queue – a function that adds a work item to a specified queue [only used for pull models] 1) This code is run when the model is reset. The ss_result spreadsheet is cleared, each kanban post is assigned one kanban, and the ECU Stack Assembly is assigned work items equal to the specified batch size (this is done using a loop). VL SECTION: Reset Logic Clear Sheet Area ss_results[1,1] , 300 , 300 Add Work To Queue Main Work Item Type , KB for Group 4 Add Work To Queue Main Work Item Type , KB for Group 3 Add Work To Queue Main Work Item Type , KB for Group 5 Add Work To Queue Main Work Item Type , KB for Group 2 Add Work To Queue Main Work Item Type , Queue for Harness Assembly LOOP 1 >>> int_loop >>> Input: System Parameters[3,16] Add Work To Queue Main Work Item Type , Queue for ECU Stack Assembly 2) This code is run when a work item exits the queue for the first workstation in each work group and adds a work item to the kanban post of the previous work group. One such example is shown below: VL SECTION: Queue for Solder Battery Wires On Exit Logic 'Obeyed just after a work item exits the storage bin but before it begins travelling to the next object Add Work To Queue Main Work Item Type , KB for Group 1 3) This code adds the time a work item exits a workstation. Similar code is used on entry, and is applied to all workstations in order to collect entry/exit times VL SECTION: ECU Stack Assembly Before Exit Logic SET ss_results[2,lbl_workitem] = Simulation Time 4) This code is run at the end of a run to save the ss_results spreadsheet into a file that can be used for analysis. VL SECTION: End Run Logic Sheet to File ["H:\MIE496Y - THESIS\SIMUL8 Models\Data\output"+gbl_trial]+".csv" , ss_results[1,1] 'Obeyed when the simulation reaches end of "Results Collection Period"

73

SET gbl_trial = gbl_trial+1

74

lean-modeling of honeywell production cell · pdf filelean-modeling of honeywell . production...

Documents