Production Design by Simulation Software “Witness” – A Case Study

MD Sarder

Industrial Engineering & Technology

University of Southern Mississippi

730 East Beach Blvd, Long Beach, MS 39560

Contact email: md.sarder@usm.edu

Sumanth Yenduri

Computer Science

University of Southern Mississippi

730 East Beach Blvd, Long Beach, MS 39560

Sumanth.yenduri@usm.edu

Abstract

Today’s customers demand high quality,

customized goods and services, quickest delivery at

low prices. This has made the design and management

of systems more challenging. Companies can not

afford to design their production system in a way that

does not optimize the scarce resources. With the

advancement of computer simulation software in the

manufacturing domain, the production design is much

easier. The key success of production design depends

on the interpretation of simulation results. This paper

illustrates the use of computer simulation to design the

production of a manufacturing company that produces

snow melting modules. The analysis presented here

describes the production design process and compares

the performance of new design with the existing

system performances.

1. Introduction

One tool that is rapidly gaining popularity in

systems design and analysis is computer simulation.

Simulation is a powerful analysis tool that helps

engineers and planners make intelligent and timely

decisions in the design and operation of a system.

Simulation itself does not solve problems, but it does

clearly identify problems and quantitatively evaluate

alternative solutions [1]. They are solution evaluators

and not solution generators. By using a computer to

model a system before it is built or to test operating

policies before they are actually implemented, many

of the pitfalls that are often encountered in the startup

of a new system can be avoided. The ability of

simulation to consider a large number and wide

variety of variables in a single model makes it an

indispensable tool in designing today’s complex

business systems. There are some simulation

softwares available to analyze manufacturing systems

including Arena, Pro-Sim, and Witness. We have

selected Witness to analyze this case because it is very

easy to model different manufacturing parameters.

The company “A” in this case is a manufacturer

of roof-mounted snow melting modules, is facing a

strong increase in demand for its products. The

company wants to increase the production from 20

packages of modules per month to 260 packages per

month during a six-phase capacity expansion plan. To

analyze this situation we developed a simulation

model of the current facility using Witness. The main

objective of this study is to design the production

system of Company A in terms of minimum number

of resources with maximum utilization during each

phase of production. In the following sections, we

have discussed the detail modeling methods and

analysis of simulation outputs to design the production

systems.

2. Modeling scenarios

The Company wishes to increase its production

from 20 packages of modules per month to 260

packages per month. However there are some

constraints that limit the model. These constraints are

as follows.

• Top management is ready to buy any

resources needed to meet the production

demands but hate to pay for excess capacity

that is not utilized.

• Maximum overtime is limited to 5% of the

standard 2000 man hours/year.

• Only two shifts are possible.

• Addition of labor and equipment is allowed

only when production cannot be achieved

with 5% overtime and two shifts per day.

• The module production area is divided into

four areas. Labor cannot be shares between

these areas.

2009 World Congress on Computer Science and Information Engineering

978-0-7695-3507-4/08 $25.00 © 2008 IEEE

DOI 10.1109/CSIE.2009.836

780

2009 World Congress on Computer Science and Information Engineering

978-0-7695-3507-4/08 $25.00 © 2008 IEEE

DOI 10.1109/CSIE.2009.836

780

• Top management hates unwanted work in

process. Hence buffers capacities are to be

limited.

The manufacturing at the company A consists of

four stages.

Stage 1: Coil Preparation – This area contains the coil

preparation machines fed by an infinite supply of raw

coils.

Stage 2: Matrix Assembly – This area consists of a

semi-automated cell designed to assemble the

individual coils into a 5x8 matrix of electrically

interconnected coils. Bus interconnects are manually

applied to the matrix. The terminals are then soldered.

A plate of glass and a sheet of adhesive plastic are

placed over the matrix to form the module.

Stage 3: Final Assembly, Lamination and Test – The

operator inspects the module and repairs the assembly

before covering it with another piece of plastic

adhesive and an insulating backing. The module is

then passed into a laminator machine. The module is

then trimmed and terminal box is attached.

Stage 4: Module Framing and Final Preparation –

Four Aluminum frame sections are attached to the

module. Twenty-five modules are then packed into a

single package.

3. Preliminary experimental design

Company A produces module package through

out the whole year. The production system of

company A is considered as continuous production

where WIP is a critical factor. Top management hates

WIP but still willing to create just enough buffer

capacity to support running different shift patterns in

the various areas on the shop floor. Good

experimental design affects the effective use of

experimental resources for two reasons. It largely

determines the form of statistical analysis that can be

applied to the results and the success of the

experiment is answering the questions of the

experimenter is largely a function of choosing the

right design.

We conducted simulation strategies primarily to

learn the most about the behavior of the system for the

lowest possible cost. Thus experimental designs are

economical because they reduce the number of

experimental trials required and provide a structure for

the investigator’s learning process [3]. We tried to

increase the percentage utilization of resources

without blocking the resources. We varied all of the

above factors to get the desired production in different

phases. We tried to reach the production quota by

reducing the number of machines, number of labors,

number of operating days, number of shifts,

percentage of labor over time, percentage of

blockedness of machine, percentage of cycle wait for

labor where possible. For experimentation we have

used different forms of data to compare the results.

The company A’s plan to expand its capacity from 20

modules per month to 260 modules per month was

scaled into six phases. The output targets for those

phases are 20, 40, 80, 120, 180 and 260 modules

respectively.

4. Input data preparation

Data collection is very important and hard part in

building simulation. Building simulation on the basis

of erroneous data will fail the project. It should be

performed intelligently and systematically to ensure

that an appropriate model is built of the system [2].

Some times many data gathering efforts end up with

lots of data but very little useful information. Data is

seldom found in appropriate form to use in simulation

software. Usually, some time analysis and conversion

need to be performed for data to be useful as an input

variable to the simulation. All necessary data were

collected from the company and prepared for the

Witness to run the simulation.

5. Simulation model

The goal of conducting experiments is not just to

find out how well a particular system operates, but

hopefully to gain enough knowledge to know how to

improve the system performance [2]. Unfortunately,

simulation output rarely identifies causes of problems,

but only report the symptomatic behavior of problems.

The bottlenecks could be in the form of slow

machining time, low capacity buffers, labor shortage

resulting in wait, machine breakdown etc. In

conducting simulation experiment, the modeler must

be careful to correctly interpret the output of

simulation run. In our experiment we have considered

the following issues:

• Steady state behavior of the system

• Initial starting condition

• Appropriate length for running the simulation

• Number of replications to be run

• Number of different random streams used in

model

• Offset from random stream

Some of these issues are discussed in the

following sub sections.

5.1. Determining the warm-up period

Company A seems a continuous production

facility, where we are interested about steady state

781781

behavior of the model. Since a model starts out empty,

it usually takes some time to reach steady state. In

steady state condition, the response variables in the

system exhibit statistical consistency. The time it takes

to reach steady sate is a function of the activity times.

In this time there is a turbulence behavior in the

model, which is not the actual system behavior. To get

rid of this unusual behavior it is very necessary to set

a warm-up period. We will wait until after the warm-

up period before we start gathering any statistics. This

way we eliminate any bias due to observations taken

during the transient state of model. In our situation we

have chosen production rate as a response variable.

When the model reaches steady state, the production

rates become steady. Warm -up period varies for

different models. We have six different production

phases. In each phase different models are used. In

phases 1 & 2, the production rates become steady after

750 minutes. For safety we have taken 1000 minutes

as warm-up period for these phases. In phases 3,4,5 &

6, the production rates become steady after 1500

minutes. For safety we have taken 1750 minutes as

warm-up period for these phases. Figure 1 shows the

warm-up period for phases 3,4,5 & 6.

Figure 1. Warm-up period (Phases 3,4,5 &6)

5.2. Convergence condition

Our system is considered as non-terminating

system. Determining convergence for a non-

terminating system is difficult since the simulation

could be run indefinitely. The statistical output of a

simulation is random in nature. To get the proper

estimate of statistical data we need to run the model

sufficient times and as well as sufficient number of

times. Obviously, running extremely long simulation

is impractical, so the issue is to determine an

appropriate run length to ensure that an adequately

representative sample of the steady state response of

the system.

The recommended length of the simulation run

for a steady state simulation is dependent upon the

interval between the least frequently occurring event

and the type of sampling method (replications) used

[3]. If running independent replications, it is usually a

good idea to run the simulation enough time to let

every type of event including rare ones happen at least

10 to 20 times. In out model the least frequently

occurred event is the assembly operation of machine

“Inventory system _mc” which takes once in every

240 minutes. In phase 1 we run the simulation for

10*8*60 =4800 minutes. The number of assembly

operations = 4800/240 = 20 times. In other phases we

run the simulation for about 20*8*60 and 20*24*60

minutes. In each phases our run length is more than 20

times of frequently occurred event. Knowing the

Warm Up period and the run length, each phase is run

for the subsequent time interval.

5.3. Experimentation

In simulation experiments, there are certain

variables, called independent or input variables that

are manipulated or varied. The effects of this

manipulation on other dependent variables are the

ones that are measured and correlated. Witness

provides convenient facilities for conducting

experiments, running multiple experimental

replications are evaluating alternative scenarios.

5.4. Number of replications & Pseudo Random

Numbers (PRN)

We have run the simulation for 5 replications

with different PRN numbers with some offset. We

collected data for all replications and took the average

to interpret the results. The results of each phase are

shown in the Table 1. Statistics reveal that our model

is robust. In each phase we are 95% confident that our

output lies between the desired ranges.

5.5. Modeling phases Witness simulation model was build and ran for

each phases of production. Figure 2 shows the model

created for production phase six. Results of each

phase were analyzed to optimize the production

parameters. Figure 3 summarizes the output of

different phases, which is the expansion plan of

Company A.

782782

Table 1. Confidence Interval for Each Phase

Phase1 Phase2 Phase3 Phase4 Phase5 Phase6

True Mean 21.2 42.4 81.8 122 180.4 260.2

Std. Dev. 0.8336 0.547 1.303 0.547 1.81 0.447

Half Width(hw)

α = 95% ± 1.157 ± 0.759 ± 1.808 ± 0.759 ± 2.512 ± 0.62

Confidence

Interval

(X + hw)

20.043 < µ <

22.357

41.641 < µ <

43.159

79.992 < µ <

83.608

121.241 < µ

< 122.759

177.88 < µ <

182.91

259.58 < µ <

260.82

Figure 2. Phase Six Simulation Model

6. Verification and validation

Verification is the correct logical representation

of the model. The main objective of verification is to

show that all parts of the model work, both

independently and together [2]. The values found for

the machine statistics and parts statistics are in close

proximity to the actual output values we achieved

from the simulation run which verifies our model. For

783783

each phase verification is done separately. We verified

some of the machine, and part statistics by using

following formulas:

Figure 3. Simulation Output of Each Phase

Machine Statistics Verification:

Total Simulation Time: - No.of days * No. of hrs in a

Day*60 (mins)

% Busy: -(No. of operations * Cycle Time )/ Total

Simulation Time

% Broken down: - ((No. of operations * Cycle Time

)/Breakdown time)* Repair time)/ Simulation Time

% Set Up: - ((No. of operations / No. of operations to

perform Setup )*Setup Time)/ Total Simulation Time

Parts Statistics Verification:

No. of coils scrapped:- No.of Operations Performed

by Inspection Machine *.0075

No. of Glass /Plastic Expected:- No. of Coils

Assembled /40

No. of Package_count Expected:- No. Of Packages

passed By Module test Machine/ 25

Company A has some constraints for running the

plant. We build the model keeping these factors in our

mind. The main concern of management is to avoid

investment on under utilized resources. Hence we

build our model giving due consideration to the

maximum utilization of existing resources and

recommending additional resources only when it is in

dire need.

To start with our model represents four stages of

production areas, virtually separated from each other.

Except for parts no other elements have been shared

between these stages. This fulfills the prime constraint

of Company A. In none of the six phases, model is run

for more than two shifts per day and five days a week.

The maximum number of labor overtime hours were

limited to overtime of 5% of standard 2000 hrs. per

year. The flow of process is maintained as given in the

problem. The machining time as well as the loading

and unloading of the machines are maintained as

given in the problem representing the system. The

breakdown and repair of the machines are taken as

random justifying the stochastic random behavior of

the machines.

7. Analysis and interpretation

We analyzed our model keeping in view the

maximum utilization of resources and minimum

inventory in the buffers.

7.1. Phase 1

The initial model constructed using the given data

reveals that the present plant capacity is more than

what is needed. The plant is capable of producing

about 60 packages per month. Hence if the plant is run

with the existing resources, they will be under utilized.

In order to ensure maximum utilization of Machine

and Labor, some of them are removed. Machines

having two quantities are replaced by one quantity.

The figures below show the % Utilization of the

Machines and Labors. It can be seen from Figure 4

and Figure 5 that removing some of the resources has

resulted in better utilization of the others.

% Machine Utilisation

50.0%42.0%

47.0%42.0%

67.0%

55.0%

77.0%

55.0%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Coil_prep_mc Sol der_St_mc Laminator_small Module_test_mc

% Busy ------------>

Existing System Proposed System

Figure 4. Machine Utilization

% Labor Utilisation

40.1%

57.2%

85.1%

8.3%

55.1%

42.3%48.0%

69.3%

0%

20%

40%

60%

80%

100%

Labor1_stg3 Labor2_stg3 Labor3_stg3 Labor1_stg4 Labor2_stg4

% Busy

Existing System Proposed System

Figure 5. Labor Utilization

Production in Phases

2142

82

122

180

260

0

50

100

150

200

250

300

Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6

Pro

duction Q

uantity

784784

7.2. Phase 2

The demand in Phase2 is double that of Phase1,

i.e., 40 packages per month. But in Phase1, the model

is run just for half a month. Hence in Phase2, the

model is run for the full month and the desired output

of 40 packages is achieved.

7.3. Phase 3

In order to increase the production from 40

packages to 80 packages, it was found that single coil

preparation machine could not produce required

quantity of parts, so another machine was introduced

to remove the bottleneck. From the chart below we see

that % cycle wait labor was high in stage1 for the

desired output and labor at stage4 can be utilized for

this purpose. From the Labor utilization chart below

we see that labors are unevenly distributed. So the

labors were shared between different processes. In

order to overcome the bottleneck at solder station and

module test so as to meet the high production demand

we added one for each. Figure 6 and Figure 7 shows

the improvement over existing design.

% Cyc l e Wa i t La bor in S t age1

2.0%

27.9%

13.9% 14.2%

1% 0%

9%

0%

10%

20%

30%

40%

coi l_pr ep_mc(1) Loadcoi l_mc Unloadcoi l_mc(1) Inspection_mc

Exist ing System Proposed Syst em

Figure 6. Labor Cycle Wait Time in Stage 1

% Ut i l i sa t ion of La bor

96.0%

40.0%

92.0%

75.0%83.0%

79.0%

0%

20%

40%

60%

80%

100%

Labor 1_stg3 Labor 2_stg3 Labor 3_stg3

Exi s t i ng System Pr oposed System

Figure 7. Labor Utilization

7.4. Phase 4

Since Stage 1 is controlling the output, and it was

found from the statistics that all other stages are

comparatively idle, so stage 1 was run for two shifts

and coils are buffered in matrix buffer for processing

in next shift at next stage. Due To restriction of

overtime hours of labors, a second shift labor is

introduced in stage 1. Load is shared between labors

to evenly distribute the work. From the %-blocked

chart shown in Figure 8, we can see that one small

laminator and large laminator creating high blockage

for final layout machine and hence are bottleneck for

achieving the desired output. So laminator_small was

replaced from laminator_large. To overcome high %

cycle wait labor in stage 3 one more labor in stage 3

was added. Improvement is shown in Figure 9.

%Blocked of F inal Layout Machine

46.4%

8.5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Smal l_lam inator+ Large_lami nator T wo Large_l am inator

% Blocked------------->

Figure 8. Machine Blockedness

% Cycle Wait Labor

51. 7%

14.0%

59.2%56.1%

21.7%

9.5%

2.1%

9.9%

15.3%

2.2%

0%

10%

20%

30%

40%

50%

60%

70%

Final_layout Laminator_large(1) Trim Terminal_at tach Modul e_test (2)

% W

ait

Existing System Proposed System

Figure 9. Labor Cycle Wait Time in Stage 3

7.5. Phase 5

Since production could not be achieved in single

shift, the model was run in two shifts, still output was

not up to the desired level. Model was further run for

25mins. Overtime in each shift was maintained at the

allowable 5% standard hour’s.

7.6. Phase 6

The Output from stage 1 with two Coil

preparation machine was found to be insufficient for

785785

the desired output. Hence a third coil preparation

machine was added. From the machine statistics and

the above chart it can be seen that % blockage of

matrix machine was high with single Layout machine.

So another layout machine was added in stage 2. Same

with the inspection machine in stage 1. Since the

output from other stages increased so in order to meet

high Production demand and to overcome the % cycle

wait labor in stage 4 another labor is added in stage 4.

Figure 10 and Figure 11 shows the proposed

improvement.

% Bl ock of M at r i x_mc due t o Layout_machi ne Quant i t y

64.2%

39.1%

0%

10%

20%

30%

40%

50%

60%

70%

One M achi ne Two Machi ne

Figure 10. Machine Blockedness

% Cy c l e Wa i t Labor i n S t age4

86.1%

35.2%

4.0%0.2% 0.0% 0.0% 0.0%

79.6%

0%

20%

40%

60%

80%

100%

Scr ew_mc Inspect i on_cl ean_mc

Exist ing System Proposed Syst em

Figure 11. Labor Cycle Wait Time in Stage 4

8. Conclusion

This case study showed the modeling details of

the production system design using the simulation

software Witness. It analyzed the simulation output

and compared the performance improvement over the

existing system. The current manufacturing facility

was running inefficiently and was unable to meet the

expanded demand. Resource utilization at the current

facility reveals that the resources were underutilized

and work was not being evenly distributed among

workers. Proposed design of the production system at

the company A can meet the increased demand at

different phases. It was done by implementing various

modifications in the production systems using

Witness. This case study illustrated the methods of

modeling and designing production system so that

others can do the same.

9. References

[1] Harrell C., Tumay K., “Simulation Made Easy: A

Manager’s Guide”, Industrial and Management Press,

1995

[2] Kelton W. D., “Simulation with Areana”,

McGraw-Hill Book Company, 2006

[3] “Learning WITNESS”, Lanner Group, Inc., 1998

786786