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
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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.
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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.
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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
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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
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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
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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
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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
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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.
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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.
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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.
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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.
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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
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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.
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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.
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• 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.
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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
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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]
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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
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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.
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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.
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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
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Figure 6.2 Overview of Current State ESSM Cell Processes
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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
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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.
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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.
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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
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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
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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:
unitunithoursmonthunits
monthdaysdayhrsdTotalDeman
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:
unitunithoursmonthunits
monthdaysdayhrsdTotalDeman
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.
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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APPENDIX A – Future State VSM
Figures: Future State VSM created during the Kaizen 1
1 2
563 4
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Figure: Future State VSM 1
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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)
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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
distribution rank acceptance
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
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CS AssemblyAuto::Fit of Distributions
distribution rank acceptance
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
distribution rank acceptance
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
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II-49Auto::Fit of Distributions
distribution rank acceptance
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
distribution rank acceptance
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
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Zero PotsAuto::Fit of Distributions
distribution rank acceptance
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
distribution rank acceptance
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
distribution rank acceptance
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
distribution rank acceptance
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
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APPENDIX C – Icons used in Simul8
Simulation Graphic Description
Entry Point
Work Complete
Resources
Queue
Workstation
(Assemblers Required)
Test Stations
(Technicians Required)
Kanban Post
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APPENDIX D – Simul8 Model Screenshots
Pull model with takt time of 300 min
Push model
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Pull model with takt time of 225 min
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Pull model with takt time of 390 min
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Pull model with takt time of 600 min
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"
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SET gbl_trial = gbl_trial+1
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