Jai Hind Cycles, Inc.
Manufacturing Simulation
Using ProModel
DSES 6620: Simulation Modeling and Analysis
Instructor: Ernesto Gutierrez-Miravete
Rensselaer at Hartford
Fall 2000
By
Daniel Ball and Debbie Leach
TABLE OF CONTENTS
Abstract .................................................................................................................... i
1.0 Introduction .......................................................................................................1
2.0 Objective ...........................................................................................................1
3.0 Scope .................................................................................................................2
4.0 Requirements ....................................................................................................2
5.0 Locations ...........................................................................................................3
6.0 Resources ..........................................................................................................3
7.0 Entities ..............................................................................................................4
8.0 Entity Flow Diagram.........................................................................................4
9.0 Processing Sequence .........................................................................................4
10.0 Arrivals ...........................................................................................................5
11.0 Path Networks and Move Times .....................................................................6
12.0 Move Triggers .................................................................................................7
13.0 Work Schedule ................................................................................................8
14.0 Assumption List ..............................................................................................8
15.0 Model Verification ..........................................................................................9
16.0 Model Validation ..........................................................................................10
17.0 Simulation Time and Replications ................................................................10
18.0 Results ...........................................................................................................11
18.1 - Scenario 1: Current JHC Facility Layout and Production Rate.......11
18.2 - Scenario 2: Current JHC Facility Layout and Optimized
Production Rate ..............................................................................11
18.3 - Scenario 3: Department Re-Location and Optimized
Production Rate ..............................................................................11
18.4 - Scenario 4: Cellular Layout and Optimized Production Rate .........12
18.5 - Scenario Comparison .......................................................................12
19.0 Conclusions ...................................................................................................13
20.0 Recommendations .........................................................................................14
References ..............................................................................................................15
TABLES
Table 1: JHC Facility Locations ..............................................................................3
Table 2: Resources and Process Times for Scenarios 1, 2, and 3 ............................3
Table 3: Resources and Process Times for Scenario 4 ............................................4
Table 4: Processing Sequence ..................................................................................5
Table 5: Initial Entity Arrivals .................................................................................6
Table 6: Path Network Distances and Move Times for Scenarios 1 and 2 ..............6
Table 7: Path Network Distances and Move Times for Scenario 3 .........................7
Table 8: Path Network Distances and Move Times for Scenario 4 .........................7
Table 9: Comparison of Results for Each Scenario ...............................................12
TABLE OF CONTENTS (Continued)
CHARTS
Chart 1: Bicycle Production Rate Versus Scenario
Chart 2: Required Manpower Versus Scenario
Chart 3: Utilization Rates Versus Scenario
FIGURES
Figure 1: Job Shop Floor Plan (Scenarios 1 & 2) - Appendix A
Figure 2: Departmental Shift (Scenario 3) - Appendix A
Figure 3: Cellular Layout (Scenario 4) - Appendix A
APPENDICES
Appendix A: Floor Plans
Appendix B: ProModel Text File - Scenario 1
Appendix C: ProModel Text File - Scenario 2
Appendix D: ProModel Text File - Scenario 3
Appendix E: ProModel Text File - Scenario 4
Appendix F: ProModel Trace Output Sample - Scenario 1
Appendix G: ProModel Trace Output Sample - Scenario 2
Appendix H: ProModel Trace Output Sample - Scenario 3
Appendix I: ProModel Trace Output Sample - Scenario 4
Appendix J: Warm-Up Period Graphs
Appendix K: Number of Replications and Statistical Verification Calculations
Appendix L: Statistical Analysis Calculations
Appendix M: ProModel Output Results - Scenario 1
Appendix N: ProModel Output Results - Scenario 2
Appendix O: ProModel Output Results - Scenario 3
Appendix P: ProModel Output Results - Scenario 4
Appendix Q: Design Comparisons - Method of Paired Differences
Appendix R: Computer Files
ABSTRACT
A model of the Jai Hind Cycles, Inc. bicycle manufacturing facility was
developed using ProModel to simulate current operations and production logistics. This
model was then used to analyze potential modifications that would increase the overall
production rate from 200 to 308 bicycles per day in order to satisfy market demand. The
following potential scenarios were included in the analysis: utilizing the current process
layout oriented facility and increasing the assembly manpower; re-locating departments
and increasing the assembly manpower; and utilizing a cellular layout and increasing the
assembly manpower.
The results of this preliminary investigation indicate that the desired production
rate can be achieved by increasing the manpower by 52.6% at each of the four
subassembly locations (i.e. bike frame, handlebar and stem assembly, saddle post
assembly, and drive chain assembly) and by 55.0% at the final bicycle assembly location.
This option can be easily implemented due to the lack of facility layout modifications
required for the other two scenarios. Additional data would be required by the facility to
improve the performance and accuracy of the model.
i
1.0 INTRODUCTION
Jai Hind Cycles, Inc. (JHC) is a manufacturing facility that produces regular bicycles for
the domestic market. The bicycle manufacturing process currently employed by JHC
includes the manufacturing of the following four subassembly units: bike frame,
handlebar and stem assembly, saddle post assembly, and drive chain assembly. These
subassembly units are produced and then assembled together to manufacture a bicycle.
Any parts or bicycle components that are not manufactured at the JHC facility are either
purchased from the market or subcontracted to vendors.
Currently, JHC is operating with one 480-minute shift per day, a process layout oriented
facility (job shop floor plan), and is producing 52,000 bicycles per year (200 bicycles per
day). Due to the limited production capabilities and the high total market demand for the
bicycles, JHC cannot manufacture enough bicycles and must import the balance of the
requested bicycles to satisfy the customer orders. Market demand during the time period
from 1994 through 1998 has indicated a total market demand of approximately 80,000
bicycles per year (308 bicycles per day).
Details regarding the JHC facility are presented in “Simulation Using ProModel” (Harrell
et al., pp. 367-369).
2.0 OBJECTIVE
Develop a model that will accurately simulate JHC’s current manufacturing facility and
aid in the design of a manufacturing system that will allow JHC to satisfy the domestic
demand for regular bicycles, thus eliminating production shortages and the need to
import bicycles.
Due to the multiple subassembly units manufactured at the JHC facility, modeling the
overall bicycle production system was attempted by focusing only on the production of
the handlebar and stem assembly. The handlebar and stem assembly was assumed to be
the limiting factor in the production of the entire bicycle due to this assembly’s extensive
resource requirements. For this reason, optimizing the production of the handlebar and
stem assembly unit would also optimize the manufacturing of the entire bicycle.
Proposed system modifications to be evaluated include altering the required manpower,
re-locating manufacturing departments, and shifting to a cellular facility layout. These
scenarios are presented in detail in Section 3.0.
1
3.0 SCOPE
A model of the production of the handlebar and stem assembly unit was developed using
ProModel. Once the current manufacturing system model was setup, verified, validated,
and calibrated, the model was optimized to sufficiently increase overall bicycle
production and subsequently eliminate the need to import shortages.
System
optimization included an analysis regarding the required manpower and appropriate
facility layout. Specifically, the following scenarios were explored:
1. The current JHC manufacturing facility layout (see Figure 1, Appendix A)
with a production rate of 200 bicycles per day was setup and verified. Then
the model was validated and calibrated by adjusting the manpower required
during the assemblies of the handlebar and stem assembly unit and the entire
bicycle. The ProModel text file for this scenario is included in Appendix B.
2. The amount of manpower, required during the manufacturing of the handlebar
and stem assembly unit and the entire bicycle, was increased until the
production goal of 308 bicycles per day was achieved. The ProModel text file
for this scenario is included in Appendix C.
3. The effects on bicycle production after re-locating two manufacturing areas
(molding and casting) was determined (see Figure 2, Appendix A). The
amount of manpower, required during the manufacturing of the handlebar and
stem assembly unit and the entire bicycle, was adjusted until the production
goal of 308 bicycles per day was achieved. This result was then compared to
the results from Scenario 2 to determine the relative effect of this
departmental shift. The ProModel text file for this scenario is included in
Appendix D.
4. The feasibility of modifying the production facility to accommodate a cellular
layout was explored (see Figure 3, Appendix A). The amount of manpower,
required during the manufacturing of the handlebar and stem assembly unit
and the entire bicycle, and the costs of incorporating a cellular layout was
evaluated and compared to the results from Scenarios 2 and 3. The ProModel
text file for this scenario is included in Appendix E.
4.0 REQUIREMENTS
The following elements were required to accomplish the stated objectives:



ProModel software package;
IBM PC, or 100% compatible, with Microsoft Windows 95 or higher, 16 MB
of memory, VGA or higher-resolution video adapter, and Microsoft mouse or
compatible pointing device;
Information provided for the JHC manufacturing facility (Harrell et al, pp. 367369); and,
2

Establishment of assumptions as presented in Section 14.0.
5.0 LOCATIONS
The locations included in the model of the JHC manufacturing facility are presented in
Table 1.
Table 1: JHC Facility Locations
Location
Raw Material Storage
Cutting Queue
Cutting
Molding Queue
Molding
Bending Queue
Bending
Casting Queue
Casting
Final Assembly I Queue
Final Assembly I
Final Assembly II Queue
Final Assembly II
Description
the location where all entities arrive into the system
waiting area for Cutting location
the location where entities are cut using electric saws
waiting area for Molding location
the location where entities are molded using a molder
waiting area for Bending location
the location where entities are bent using tube benders
waiting area for Casting location
the location where entities are cast using a die caster
waiting area for Final Assembly I location
the location where the handlebar and stem assembly
units are assembled
waiting area for Final Assembly II location
the location where all four subassembly units and
purchased or vendor-supplied components are
integrated to assemble the bicycle
6.0 RESOURCES
The resources and process times included in Scenarios 1, 2, and 3 of the model of the
JHC manufacturing facility are presented in Table 2.
Table 2: Resources and Process Times for Scenarios 1, 2, and 3
Equipment
Type
Molding
Tube Bender
Die Casting
Electric Saw
Process Time
45 seconds per part (Log-Normal distribution)
1 bend per 30 seconds (Log-Normal distribution)
1 part per minute (Log-Normal distribution)
1 cut per 15 seconds (Log-Normal distribution)
Quantity
1
2
1
2
The resources and process times included in Scenario 4 of the model of the JHC
manufacturing facility are presented in Table 3.
3
Table 3: Resources and Process Times for Scenario 4
Equipment
Type
Molding
Tube Bender
Die Casting
Electric Saw
Process Time
Quantity
45 seconds per part (Log-Normal distribution)
1 bend per 30 seconds (Log-Normal distribution)
1 part per minute (Log-Normal distribution)
1 cut per 15 seconds (Log-Normal distribution)
1
1
1
1
A Log-Normal distribution was applied to each resource-related process time to account
for potential variability (Law and Kelton, p. 307).
7.0 ENTITIES
The following entities are included in the model of the JHC manufacturing facility:




Handlebars
Handlebar Plugs
Handlebar Stems
Handlebar and Stem Assembly
8.0 ENTITY FLOW DIAGRAM
An entity flow diagram was constructed to indicate the flow path of each entity as it
moves through the system. The entity flow diagram is applicable for all four scenarios
and is presented as follows:
Handlebars
Raw
Material
Storage
Handlebar Plugs
Handlebar Stem
Cutting
Bending
Final
Assembly
I
Handlebar
and Stem
Assembly
Molding
Casting
Final
Assembly II
Bicycle
9.0 PROCESSING SEQUENCE
Entities are processed with the logic presented in Table 4.
4
Table 4: Processing Sequence
Entity
Location
Handlebars
Cutting
Bending
Final Assembly I
Handlebar Plugs
Molding
Final Assembly I
Handlebar Stems
Casting
Cutting
Final Assembly I
Handlebar and Stem Assemblies Final Assembly II
Process Time
(mean, standard deviation or
half-range)
Log-Normal (15, 1) seconds
Log-Normal (30, 1) seconds
Uniform (45, 15) minutes
Log-Normal (45, 1) seconds
Uniform (45, 15) minutes
Log-Normal (1, 0.1) minutes
Log-Normal (15, 1) seconds
Uniform (45, 15) minutes
Uniform (45, 15) minutes
The handlebars, handlebar plugs, and handlebar stems are joined together at the Final
Assembly I location and form the handlebar and stem assembly unit. The handlebar and
stem assembly unit is then further processed and integrated with the bike frame, saddle
post assembly, and drive chain assembly units at the Final Assembly II location to form
the entire bicycle.
Deviations to the process times provided with the case description (Harrell et. al, p. 367)
were incorporated to accommodate actual process time variability. A Log-Normal
distribution was applied to each resource-related process time to account for potential
variability (Law and Kelton, p. 307). The uniform distribution was applied as indicated
with the case description (Harrell et al, p. 367; Law and Kelton, p. 299).
10.0 ARRIVALS
Entity arrivals to the system were designed to provide a continuous supply from the Raw
Material Storage location. The number of each entity initially arriving at the Raw
Material Storage location was determined based on the specific requirements for each
scenario. Due to the extended travel length of the handlebar stem (Scenarios 1 and 2) and
the handlebar plug (Scenario 3) to their initial process location, multiple arrivals were
necessary to ensure continual entity flow and minimize the effect of entity availability on
simulation output. The arrivals were adjusted to achieve the desired bicycle production
rate and maximize utilization at the limiting locations (i.e. the initial process location that
required the furthest travel distance from the Raw Material Storage location). This setup
conforms with the assumption presented in Section 14.0 that there is no shortage of
entities flowing through the system. As a result, initial entity arrivals into the system
necessary to satisfy this assumption are presented in Table 5.
5
Table 5: Initial Entity Arrivals
Entity
Handlebar
Handlebar Plug
Handlebar Stem
Scenario 1
1
1
2
Scenario 2
1
1
2
Scenario 3
1
3
2
Scenario 4
1
1
1
Once each of these entities reaches the appropriate initial process destination as indicated
in the Entity Flow Diagram (Section 8.0), an order command triggers the instantaneous
arrival of the respective entity to the Raw Material Storage location. This arrival setup
eliminates raw material supply factors and allows for a realistic “just-in-time” ordering
and delivery system that governs the flow of entities throughout the system.
11.0 PATH NETWORKS AND MOVE TIMES
Path networks were designed to accurately simulate a conveyor-like entity transportation
process. A constant transportation rate of 150 feet per minute (fpm) was used and
applied to the scaled factory layout incorporated into the model. A separate path network
was constructed for each entity to follow as it traveled through the path indicated on the
Entity Flow Diagram (Section 8.0).
The distances and approximate move times pertaining to the primary locations for
Scenarios 1 and 2 are displayed in Table 6.
Table 6: Path Network Distances and Move Times for Scenarios 1 and 2
From
Raw Material Storage
Cutting
Bending
Raw Material Storage
Molding
Raw Material Storage
Molding
Casting
Cutting
To
Cutting Queue
Bending Queue
Final Assembly I Queue
Molding Queue
Final Assembly I Queue
Casting Queue
Final Assembly I Queue
Cutting Queue
Final Assembly I Queue
Distance (feet)
205.06
155.71
325.07
196.01
320.92
356.31
320.92
340.94
419.45
Time (minutes)
1.367
1.038
2.167
1.307
2.139
2.375
2.139
2.273
2.796
The distances and approximate move times pertaining to the primary locations for
Scenario 3 are displayed in Table 7.
6
Table 7: Path Network Distances and Move Times for Scenario 3
From
Raw Material Storage
Cutting
Bending
Raw Material Storage
Molding
Raw Material Storage
Casting
Cutting
To
Cutting Queue
Bending Queue
Final Assembly I Queue
Molding Queue
Final Assembly I Queue
Casting Queue
Cutting Queue
Final Assembly I Queue
Distance (feet)
205.06
155.71
325.07
360.00
135.56
175.12
272.38
405.60
Time (minutes)
1.367
1.038
2.167
2.400
0.904
1.167
1.816
2.704
It should be noted that, due to low move times, only significant travel distances (greater
than 35 feet) were included in these tables. Travel distances between queues and
respective process locations and between the Final Assembly I and the Final Assembly II
Queue locations were included in the model, but not in the tables.
The distances and approximate move times pertaining to the primary locations for
Scenario 4 are displayed in Table 8.
Table 8: Path Networks Distances and Move Times for Scenario 4
From
Raw Material Storage
Cutting
Bending
Raw Material Storage
Molding
Raw Material Storage
Casting
Cutting
Final Assembly I
To
Cutting Queue
Bending Queue
Final Assembly I Queue
Molding Queue
Final Assembly I Queue
Casting Queue
Cutting Queue
Final Assembly I Queue
Final Assembly II Queue
Distance (feet)
147.81
28.71
149.69
100.00
111.65
82.43
97.72
190.43
293.00
Time (minutes)
0.985
0.191
0.998
0.667
0.744
0.550
0.651
1.270
1.95
It should be noted that only travel distances between primary process locations were
included in this table. Travel distances between queues and respective process locations
and between the Final Assembly I and the Final Assembly II Queue locations were
included in the model, but not in the table.
12.0 MOVE TRIGGERS
Entity move triggers depend on the specific point location in the Entity Flow Diagram
(Section 8.0). An entity that is located at a process location (i.e. cutting, molding,
casting, bending) will move to the next location once the appropriate processing has been
completed. An entity that is located in a queue (with the exception of the Final Assembly
I Queue) will proceed to the respective process location in accordance with a First-In7
First-Out (FIFO) move logic. A handlebar stem that arrives at the Final Assembly I
Queue location will instantly pass to the Final Assembly I location, provided that the
destination capacity has not been achieved. Handlebars and handlebar plugs, located in
the Final Assembly I Queue, will remain in queue until a handlebar stem has arrived at
the Final Assembly I location and initiated a “join” command to represent the assemblage
of the three entities and form the handlebar and stem assembly unit.
13.0 WORK SCHEDULES
Stations are scheduled to operate 480 minutes (8 hours) per day, five days per week
(Monday through Friday).
14.0 ASSUMPTION LIST
The model of the JHC facility for Scenarios 1, 2, 3, and 4 is subject to the following
assumptions:

The total market demand for regular bicycles is 80,000 bikes per year (308
bicycles per day). Because the current production of bicycles is only 52,000 per
year (200 bicycles per day), daily production must increase by approximately 108
bicycles per day in order to eliminate production shortages.

The manufacturing of the handlebar and stem assembly unit is assumed to be the
limiting factor in the production of the entire bicycle. All equipment types, with
the exception of the welding and forging equipment, are utilized in the production
of the handlebar and stem assembly unit. For this reason, optimizing the
production of the handlebar and stem assembly unit will also optimize the
manufacturing of the entire bicycle.

The flow of entities operates on a “just-in-time” ordering and delivery system that
would order the appropriate entity to the Raw Material Storage location (with
instant delivery and arrival) once the given entity successfully arrived at its first
designated location.

There is no shortage of entities available for continuous flow through the system.

There are no machine or process location downtimes.

The capacity of the Final Assembly I and Final Assembly II locations represents
the number of workers in each location.

Changes in the manufacturing system that change the production of the handlebar
and stem assemblies will subsequently change the production of the other
subassemblies by a relatively equal amount. Thus, a percentage change in
manpower at the Final Assembly I location would require an equal percentage
manpower change in the locations where the bike frame, saddle post assembly,
8
and drive chain assembly are assembled in order to achieve the same amount of
bicycle production change.

The manufacturing of the handlebar and stem assemblies do not cause delays in
the manufacturing of the other subassemblies, and vice versa.

Each of the three subassembly units that are not manufactured in the model (i.e.
bike frame, saddle post assembly, and drive chain assembly) are readily available
at the Final Assembly II location.
15.0 MODEL VERIFICATION
The model of the JHC manufacturing facility was verified by conducting the following
tasks:

“Dummy variables” were included during model development to track the
movement and behavior of entities at various locations. This technique was
particularly useful during the coding of the “join” statement at the Final Assembly
I location. “Dummy variables” were developed to track the number of
handlebars, handlebar plugs, and handlebar stems that arrived at the Final
Assembly I Queue and if/when a handlebar stem successfully passed on to the
Final Assembly I location to provide the appropriate move trigger (Section 12.0)
and join with a handlebar and handlebar plug. This technique aided in the
identification of an error in the move trigger process that prevented handlebar
stems from entering the Final Assembly I location and triggering the “join”
command. “Dummy variables” were also developed to track the movement
behavior of handlebar and stem assembly units from the Final Assembly I
location to the Final Assembly II Queue. These variables were removed from the
code once the process was verified.

Animation options included with the ProModel software package were used to aid
in the visualization of entity flow paths. This technique made it possible to ensure
that entities were traveling to the proper location in accordance with the entity
flow diagram (Section 8.0). Using color to designate specific entity and path
networks allowed for easier visual tracking of entity flow.

The trace command included with the ProModel software package was used to
verify that the entity flow logic, resource operations, and designed path networks
simulated the system processes as intended. This technique was particularly
useful during the development of the “just-in-time” ordering and delivery system.
Review of the trace output provided evidence that batch arrivals to the Raw
Material Storage location would not initiate an accurate process flow;
subsequently, the “just-in-time” ordering and deliver system was developed to
eliminate upstream flow variations and ensure adequate entity flow. The step
trace command allowed for proper tracking of the entity arrivals to the Raw
Material Storage location, the movement of entities along designated path
networks with proper move times (Section 11.0), the ordering and automatic
9
delivery of entities, and the appropriate process events. Sample versions of the
step trace command for Scenarios 1, 2, 3, and 4 are included in Appendices F, G,
H, and I, respectively.
Discrepancies in model logic discovered during the verification process were
subsequently rectified.
16.0 MODEL VALIDATION
The model of the JHC manufacturing was validated by conducting the following tasks:

Statistical variation was incorporated in the process times as discussed in Section
9.0. These variations were included to provide flexibility in the model to better
represent variability in the manufacturing process.

The animation and trace techniques were applied as discussed with the model
verification process to ensure proper model execution. These techniques are
discussed in detail in Section 15.0.

A sensitivity analysis was performed to determine the effects of entity arrivals and
manpower on the model output. This analysis was included in the calibration
process to represent current model conditions by adjusting the capacity
(manpower) of the Final Assembly I and Final Assembly II locations so that the
bicycle production rate was 200 bicycles per day, and in subsequent analyses of
Scenarios 2, 3, and 4.
17.0 SIMULATION TIME AND REPLICATIONS
The model of the JHC manufacturing facility was setup as a nonterminating simulation
due to the steady-state (long-term average) behavior of the system.
A warm-up period was determined to establish appropriate model operating parameters.
The warm-up period was determined by running the model for a 40-hour week and
graphically (output versus time) determining the warm-up period. This analysis was
conducted for the current JHC operating condition (Scenario 1) and for the three
optimized cases (Scenarios 2, 3, and 4). The warm-up period for each of these scenarios
was determined to be 3 hours. Due to the time required to run the simulation for
extended time periods, the model could be run for one day with a 3-hour warm-up period.
The graphs used to determine the warm-up period for each scenario are included in
Appendix J.
The required number of replications was determined by applying a 95% confidence
interval to the desired daily bicycle production rate. These calculations were determined
for both Scenarios 1 and 2 with a desired deviation of 2 bikes per day from the sample
mean. These calculations indicated that 24 replications would be necessary to ensure
that, with a 95% confidence level, the sample mean bicycle production rate is within 2 of
the true mean bicycle production rate. Scenarios 3 and 4 were then conducted using 24
10
replications; the results from the simulation of these scenarios were analyzed and
confirmed that 24 replications would be statistically significant using a 95% confidence
level. The calculations used to determine the number of replications and verify the
results are included in Appendix K.
In summary, the model was implemented to simulate one 8-hour work shift with a 3-hour
warm-up period. The results would represent steady-state production and could then be
applied to determine the annual bicycle production rate. Detailed information pertaining
to the determination of the warm-up period and the required number of replications is
included as Appendices J and K.
18.0 RESULTS
18.1 - Scenario 1: Current JHC Facility Layout and Production Rate
The model representing the current JHC manufacturing facility was developed for the
purpose of establishing a baseline model for optimization purposes. The baseline
manpower requirements were 19 for the handlebar and stem assembly process (Final
Assembly I) and 20 for the entire bicycle assembly process (Final Assembly II). Using
the statistical analysis package provided with ProModel and a confidence level of 95%,
this manpower configuration produces bicycles with an average production rate of
199.917 bicycles per day (95% confidence interval: 198.553 to 201.281 bicycles per day).
As a result, this configuration reasonably represents JHC’s current production rate of 200
bicycles per day. Calculations verifying the statistical analysis are included in Appendix
L. Process location utilization is currently 19.39% (Cutting), 57.38% (Molding), 18.28%
(Bending), 81.98% (Casting), 99.64% (Final Assembly I), and 93.96% (Final Assembly
II). ProModel output results are included in Appendix M.
18.2 - Scenario 2: Current JHC Facility Layout and Optimized Production Rate
The optimized manpower requirements, using the current JHC facility layout, were 29 for
the handlebar and stem assembly process (Final Assembly I) and 31 for the entire bicycle
assembly process (Final Assembly II). Using the statistical analysis package provided
with ProModel and a confidence level of 95%, this manpower configuration produces
bicycles with an average production rate of 307 bicycles per day (95% confidence
interval: 305.067 to 308.933 bicycles per day). As a result, this configuration reasonably
represents JHC’s goal production rate of 308 bicycles per day. Calculations verifying the
statistical analysis are included in Appendix L. Process location utilization with this
scenario is 19.38% (Cutting), 57.41% (Molding), 18.27% (Bending), 81.97% (Casting),
99.64% (Final Assembly I), and 92.46% (Final Assembly II). ProModel output results
are included in Appendix N.
18.3 - Scenario 3: Department Re-Location and Optimized Production Rate
The optimized manpower requirements for the JHC facility when switching the locations
of the casting and molding locations were 29 for the handlebar and stem assembly
process (Final Assembly I) and 31 for the entire bicycle assembly process (Final
11
Assembly II). Using the statistical analysis package provided with ProModel and a
confidence level of 95%, this manpower configuration produces bicycles with an average
production rate of 306.667 bicycles per day (95% confidence interval: 305.254 to
308.079 bicycles per day). As a result, this configuration reasonably represents JHC’s
goal production rate of 308 bicycles per day. Calculations verifying the statistical
analysis are included in Appendix L. Process location utilization with this scenario is
19.49% (Cutting), 80.64% (Molding), 18.28% (Bending), 82.73% (Casting), 99.60%
(Final Assembly I), and 92.58% (Final Assembly II). ProModel output results are
included in Appendix O.
18.4 - Scenario 4: Cellular Layout and Optimized Production Rate
The optimized manpower requirements for the cellular layout scenario were 29 for the
handlebar and stem assembly process (Final Assembly I) and 37 for the entire bicycle
assembly process (Final Assembly II). Using the statistical analysis package provided
with ProModel and a confidence level of 95%, this manpower configuration produces
bicycles with an average production rate of 306.667 bicycles per day (95% confidence
interval: 304.942 to 308.391 bicycles per day). As a result, this configuration reasonably
represents JHC’s goal production rate of 308 bicycles per day. Calculations verifying the
statistical analysis are included in Appendix L. Process location utilization with this
scenario is 46.91% (Cutting), 69.90% (Molding), 50.74% (Bending), 86.06% (Casting),
99.60% (Final Assembly I), and 77.27% (Final Assembly II). ProModel output results
are included in Appendix P.
18.5 - Scenario Comparison
A comparison of manpower requirements, bicycle production rates, and resource
utilization is included in Table 9.
Table 9: Comparison of Results for Each Scenario
Scenario
1
199.917
19
20
19.39
57.38
18.28
81.98
99.64
93.96
Parameter
Average Production Rate (bicycles per day)
Final Assembly I Manpower
Final Assembly II Manpower
Cutting Utilization (%)
Molding Utilization (%)
Bending Utilization (%)
Casting Utilization (%)
Final Assembly I Utilization (%)
Final Assembly II Utilization (%)
Scenario
2
307
29
31
19.38
57.41
18.27
81.97
99.64
92.46
Scenario
3
306.667
29
31
19.49
80.64
18.28
82.73
99.60
92.58
Scenario
4
306.667
29
37
46.91
69.90
50.74
86.06
99.60
77.27
Using a 95% confidence level, the method of paired differences (Harrell et al, pp. 228230, 489-492) was used to compare bicycle production rates for each of the scenarios.
This analysis concluded that the average production rates exceeded that of Scenario 1 for
12
Scenario 2 (104.93 to 109.24 bicycles per day), Scenario 3 (104.55 to 108.95 bicycles per
day), and Scenario 4 (104.55 to 108.95 bicycles per day). In addition, 95% confidence
intervals all contained zero when comparing Scenarios 2-3 (-2.21 to 2.87 bicycles per
day), Scenarios 2-4 (-1.89 to 2.56 bicycles per day), and Scenarios 3-4 (-2.48 to 2.48
bicycles per day); thus, the production rates of Scenarios 2, 3, and 4 do not differ with a
95% level of confidence. The method of paired differences calculations are included in
Appendix Q.
The bicycle production rate, required manpower, and location utilization are graphically
displayed in Charts 1, 2, and 3, respectively.
19.0 CONCLUSIONS
The desired production rate of 308 bicycles per day can be achieved by increasing the
manpower at each of the four Final Assembly I locations (i.e. bike frame, handlebar and
stem assembly, saddle post assembly, and drive chain assembly) by 52.6% and increasing
the manpower at the Final Assembly II location by 55.0%.
The newly designed departmental layout model (Scenario 3) gives similar results to the
optimized model (Scenario 2). The main difference is the increase in utilization at the
molding location. This increase is due to the shorter distance the entity must travel to get
to the molding station. Although this increase appears desirable, the limiting factors have
been the handlebar stems and the capacity (manpower) in the Final Assembly I and II
locations. In addition, this model assumed that the production of the handlebar and stem
assembly unit is the limiting subassembly unit in the manufacturing of the bicycle. It
should be noted that the saddle post assembly unit also requires use of the Molding
location and would need it to be available. The high utilization rate at the molding
location may create problems during the actual production of all of the subassembly
units. In addition, departmental re-location costs would be incurred to implement
Scenario 3 with little benefit to the overall outcome since manpower at the Final
Assembly I location is the limiting factor.
The cellular layout (Scenario 4) maintains significantly higher resource utilization than
Scenarios 2 and 3 for the Cutting, Bending, and Casting locations and higher Molding
utilization than Scenario 2. This result is due to the lower travel time needed for each
entity to reach its desired location. However, the overall output of bicycles remains
unchanged due to the fact that the amount of workers at the Final Assembly I location is
the limiting factor. The cellular layout would require increased costs with respect to
manpower, additional resource purchase, and layout re-organization. Since the cellular
layout requires an additional 6 workers in the Final Assembly II location, the cost of
employees salaries would increase by an average of approximately $210,000 per year
(assuming an average of $35,000 per year per worker). Also, a minimum of one electric
saw and one die caster would have to be purchased to satisfy the demands of all four
cellular production units (i.e. bike frame, handlebar and stem assembly, saddle post
assembly, and drive chain assembly). A cost-benefit analysis would have to be
performed to determine whether the increase in worker utilization is worth the cost of the
new employees as well as the new layout.
13
20.0 RECOMMENDATIONS
The following recommendations to JHC have been developed using the results of the
model analysis:

Compare the results of Scenario 1 (baseline case) to current facility manpower
and resource utilization to determine the accuracy of the governing assumptions
presented in Section 14.0. The accumulation of more facility data and subsequent
adjustments to the model would be made if necessary.

Perform a cost-benefit analysis of the cost of workers needed to reach the desired
goal of 308 bicycles per day compared to the cost of importing the necessary
shortage.

Implement the increase in workers (Scenario 2) if the baseline model results prove
to be accurate and the cost-benefit analysis proves Scenario 2 to be more
beneficial than importing the bicycles. Scenario 2 can be easily implemented due
to the minimal manpower requirements when compared to Scenario 4 and the lack
of facility layout modifications necessary for both Scenarios 3 and 4.

A re-evaluation of all scenarios could be conducted, and corresponding model
improvement, if the actual results of Scenario 2 do not produce the predicted and
desirable results or an increase in worker utilization is required.
14
REFERENCES
Harrell, Charles, Ghosh, Biman K., and Royce Bowden, “Simulation Using ProModel”,
McGraw-Hill Companies, 2000, pp. 367-369.
Law, Averill M., and W. David Kelton, “Simulation Modeling and Analysis”, McGrawHill Companies, 2000, pp. 299, 307.
15
APPENDIX A
FLOOR PLANS
FIGURE 1: Job Shop Floor Plan (Scenarios 1 & 2)
Area = 500,000 ft2
Raw Material Storage
Cutting
Molding
Bending
Casting
Welding
Offices
Final Assembly
Warehouse & Shipping
FIGURE 2: Departmental Shift (Scenario 3)
Area = 500,000 ft2
Raw Material Storage
Cutting
Casting
Bending
Molding
Welding
Offices
Final Assembly
Warehouse & Shipping
FIGURE 3: Cellular Layout (Scenario 4)
Area = 900 ft2
Raw Material Storage
Casting
Cutting
Bending
Molding
Final
Assembly
I
NOTE: Final Assembly II location is not included in the cellular layout
APPENDIX B
PROMODEL TEXT FILE – SCENARIO 1
APPENDIX C
PROMODEL TEXT FILE – SCENARIO 2
APPENDIX D
PROMODEL TEXT FILE – SCENARIO 3
APPENDIX E
PROMODEL TEXT FILE – SCENARIO 4
APPENDIX F
PROMODEL TRACE OUTPUT SAMPLE – SCENARIO 1
APPENDIX G
PROMODEL TRACE OUTPUT SAMPLE – SCENARIO 2
APPENDIX H
PROMODEL TRACE OUTPUT SAMPLE – SCENARIO 3
APPENDIX I
PROMODEL TRACE OUTPUT SAMPLE – SCENARIO 4
APPENDIX J
WARM-UP PERIOD GRAPHS
APPENDIX K
NUMBER OF REPLICATIONS AND STATISTICAL
VERIFICATION CALCULATIONS
APPENDIX L
STATISTICAL ANALYSIS CALCULATIONS
APPENDIX M
PROMODEL OUTPUT RESULTS – SCENARIO 1
APPENDIX N
PROMODEL OUTPUT RESULTS – SCENARIO 2
APPENDIX O
PROMODEL OUTPUT RESULTS – SCENARIO 3
APPENDIX P
PROMODEL OUTPUT RESULTS – SCENARIO 4
APPENDIX Q
DESIGN COMPARISON – METHOD OF PAIRED DIFFERENCES
APPENDIX R
COMPUTER FILES