IE484_F23 Sample Design Case 4
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Industrial Engineering
Date
Dec 6, 2023
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44
Uploaded by SargentRiver6906
IE 484 Fall 20
XX
Design Case Study 5
Cytel
Author
Contribution %
Contribution Aspect(s)
A1
25
1,3,4
A2
25
1,2,3,4,5
A3
25
1, 3, 4, 5
A4
25
6
1
Sample solution for IE484 Design Case 5
; NOTE: The sample is
incomplete, and does not match your assignment in certain aspects.
Also
note additional comments in this color throughout the sample solution.
Team XX
Design Recommendation Letter
Date
....
To whom it may concern,
After extensive research and analysis, our team has arrived on a new facility design for Cytel’s
new Integrated Circuit plant along with a material handling process with a corresponding WIP policy. Our
proposed solution will help Cytel be competitive in the marketplace by helping them produce enough
product to meet their projected demand as well as handling their material in an effective manner that will
help them minimize accidents. With our WIP policy Cytel will be able to effectively minimize the amount
of time that a lot spends in their facility while ensuring product quality. In the summary table below the
reader will find a comparison for the two proposed designs and an ROI analysis for our proposed material
handling systems.
Figure 1: Summary Table
For the design of our proposed facility, we included the number of necessary machines to meet
demand. Our recommended material handling system involving an overhead joist system working in
conjunction with AGVs. The layout design can be seen below in Figure 2.
2
Figure 2
:
Layout Design
U
NITS OF MEASURE???? BASED ON
WHICH DESIGN ALTERNATIVE? LAYOUT DESIGN OF WHICH PART OF THE fAB?
We are confident that this design will help Cytel in both the present and the future because it helps them meet
demand in an efficient manner Overall, we are confident that our proposed solution will serve Cytell well both now
and in the future and will be able to successfully penetrate its desired market in the global market.
Sincerely,
Team
XX
3
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Abstract
In this case study the team was tasked with helping Cytel create an optimal facility layout for the
production of Integrated Circuits (IC) and recommend two appropriate material handling system designs.
The team created an optimal facility layout based on the movement and production schedule of the
product that is being produced.
The group gave a relationship rating between each machine in order to
ensure the process is optimal.
The team created two different design layouts and compared them using
the CORELAP score and the distance between departments that have high ratings.
With these ratings, an
optimal layout was decided upon.
The next task was to decide on appropriate material handling systems
that could be added into our system.
The two that were chosen were an Overhead Hoist Transport (OHT)
and an AGV system.
These were chosen because they reduce the risk to yield and limit the gap time for
the machines.
The team then calculated the ROI for each system.
The recommended designs will help
Cytel be competitive in this market and remain in business for a long time to come.
4
Report Body
Question 1
Problem Statement: Determine how many machines are needed to be installed and when they are needed
based on demand.
Use a simulation to determine this.
Assumptions:
1. Data given for 2022 is summed up to equal the first quarter of 2022
Question 1a
Integrated Circuit (IC) production is a highly competitive industry. At any time during Cytels
semiconductor production schedule a competitor could introduce a new product that makes Cytels
obsolete, thus eliminating or greatly reducing future demand. If this occurred, and additional machines
were already purchased to accommodate future demand, those machines would never be needed or used
making them a waste of capital. Therefore the financial consultants
chase method
, purchasing new
machines only when they are immediately needed to meet demand, is logical for the IC industry because
it protects the company from substantial unforeseen changes in demand.
Question 1b
The team is given demand that is expected for each quarter of the next two years.
The goal is to
use linear interpolation to get the demand for each week.
The demand will be increasing, or remaining
constant, in a linear fashion within each quarter.
The results can be shown in the graphs below.
5
Figure 1.1: 2020 linear interpolated demand by week
UNITS of
MEASURE?
Figure 1.2: 2021 linear interpolated demand by week
6
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Question 1c
Assumptions:
1. To account for the varying availabilities per machine using the simulation software available to
us (Arena) we are evaluating all machines at the lowest Availability percentage of 65%
2. Since Arena crashes when more than 150 entities are present in a system, we are testing our
simulation model for the working utilization time in one day and expanding that to production
capabilities for a week.
To determine the number of processing machines necessary over time to meet demand for
product A, an arena simulation was created. It contained more than 29 modules, with the number of
machines was represented by the capacity of each specific machine resource labeled “cvd_mach”,
“dry_mach”, etc. Its size is large therefore to view it clearly see Appendix B. Initially one of each
machine type was simulated with the system running for one day worth of uptime, in this case 11.1 hours
per day (full calculations in Appendix E). In the results page, if the amount of wafer entities produced did
not meet or exceed demand, an additional machine was added based on which machine had the highest
utilization reported. For example, Figure 1.3 shows an iteration of this process in which demand was not
met and another lithography machine, represented by the green bar, needed to be added and the
simulation run again.
7
Figure 1.3: Utilization on Arena Results Page Indicating Which Machine Type Should Be Added
This Process was repeated until the necessary number of machines for each level of demand was
met. The results, showing the number of required machines demand levels as a function of time are shown
in Figure 1.4 below.
Figure 1.4, Total Number of Machines Needed To Produce Product A
8
I
s there no variability? uncertainty?
_
Would a range not give
you
more confidence?
Arena also produced helpful queue information such as average waiting time per process, the
average time a WIP wafer spent in a queue, the average number of WIP parts in the system and the
average waiting time of each machine. Appendix H shows some show some parts of the result pages with
these metrics produced during our simulation testing.
Question 1d
The team was tasked to create a capacity outlook graph.
Since the team already had the
simulation, the next step was to perform sensitivity analysis on the demand.
The results of the sensitivity
analysis can be shown below.
Figure 1.5: Sensitivity Analysis on demand
The next step is to create the capacity outlook graph. This is a graph that has the demand (in blue)
overlaid with the capacity (in red) to show when the company will need to order more machines (black
vertical lines).
The graph can be seen below.
9
What are your assumptions about the sensitivity? What is the sensitivity
analysis based on?
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Figure 1.6: Capacity Outlook Graph
Question 1e
The time at which the Nth machine should be added can be seen above in the capacity outlook
graph.
The more detailed table with exact times and numbers can be seen below.
Figure 1.7: Summary table for time machines need to be added
10
"time" of "when"?
Question 2
Problem Statement: Determine and recommend an optimal layout of the factory to support production of
Cytel’s
products.
Assumptions:
1.
Due to restriction in the capabilities of our simulation software (Arena), our model is assuming a
different type of process machine must be used for each product. Therefore an oxidation machine
must be assigned to work on either Product A, B,
or
C but not a combination of them.
2.
Each department contains only machines that perform one of the eight types of processes. For
example, the lithography department will only contain lithography machines.
3.
All manufacturing processes take place inside the bay and all fumes are pumped into the chase
4.
Each machine requires a 10’ x 10’ area.
In order to design our layout we used the CORELAP algorithm to come up with 2 different
design layouts. In order to make a relationship chart for each production line the team decided to first
make a from to chart to count how often a product was transferred between two different departments.
The results can be seen below in Figures 2.1 through 2.3.
Figure 2.1: From to Chart for Product A
11
Figure 2.2:
From to Chart for Product B
Figure 2.3:
From to Chart for Product C
To determine the order of placing the departments on the coordinate grid the group
used a
Muther grid to give relationships between all 8 of the departments. For the grid the team gave the
relationship a score of either A,E,I,O,U, and X. The significance of the letters was to denote the closeness
desirability of the two departments with A being of the highest importance, E being moderately important,
I being important, O be ordinarily important, U being unimportant, and X being undesirable.
The results
for each of the charts are below in Figures 2.4 to 2.6.
12
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Figure 2.4: Muther Chart for Product A
Figure 2.5:
Muther Chart for Product B
13
Figure 2.6:
Muther Chart for Product C
Following this the team added these relationship values for each department to get a Total
Closeness Rating (TCR), which helps determine the order in which departments are prioritized during the
design process. The excel sheet where TCRs were calculated can be found in Appendix A. From this the
group determined the order in which the dimensions would be for each department. After allocating space
for every department the group
was left with the following design, which will be called design A which
can be seen in Figure 2.7. The proposed material flow for Product A can be seen in the green line in the
Figure. In order to determine the amount of space needed for the gown room the team determined that the
square footage required was 22% of the total facility space (IRSHA). From this the team decided that the
gown room should be a 174’ x 174’ square area.
14
Figure 2.7:
Facility Design A
Further below in Figure 2.8 is a more in depth look at the three production lines.
15
Units of measure????
Which facility?
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Figure 2.8:
Production Line Layout for Design A
To create an alternative design the team redistributed the machines with a stricter adherence to the
CORELAP algorithm and the new design along with the material flow (signified in green) can be found
below in Figure 2.9. This layout will be called Design B. The team opted not to change any of the
dimensions for the support rooms, gown rooms, or the storage space since we determined that the space
was adequate enough.
16
Figure 2.9: Facility Layout for Design B
Further below in Figure 2.10 is a more in depth look at the three production lines.
17
Figure 2.10: Overview of Production Area for Facility Design B
Design Evaluation:
In order to determine the best facility the team decided to use two performance metrics, the CORELAP
score for the design and the average
number of departments in between departments with high (A or E
ratings) relationship ratings. The summary table comparing these two metrics can be found below in
Figure 2.11.
18
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Figure 2.11: Summary Table for Design Evaluation
Based off of this table it is clear to see that layout design b is the better design choice.
Question 3
Problem Description: Recommend a selection for a more suitable MHS to help minimize risk to the yield.
Assumptions:
1.
Human employees that would cary WIP wafers from one machine to another known as runners
are able to handle the output of two machines in a single department.
2.
In each department there is one operator each shift inspecting all wafers before they leave the
department area. These workers are not replaced in each MHS design iteration and therefore are
left out of ROI calculations.
Question 3a
One of the main problems with the process of manufacturing Integrated Circuits is that they must
be handled with gentle care.
This is a major factor for the yield of the system.
One of the things the
group wanted to do was to minimize the amount that the lot is handled by a human.
The team decided to
use an Overhead Hoist Transport (Wang).
This will allow for the product to move to other machines
efficiently and limit the vibrations that a human would create while carrying the product.
Based on Table 17.5 from class notes 6a, the cost of this overhead hoist is about $300/ft. The
team’s design in Question 2 requires 1969
amount of feet around the bays, so the total cost for this hoist is
$590,766
.
19
What about other assessment criteria? MHS Performance Ratios?
In order to calculate ROI, the team needs to understand how much money this lift will be saving
the company.
From assumption 3.1 the company would have a max amount of 172 runners in the first
year that are needed for carrying the product to and from other machines.
With this hoist system, Cytel
no longer needs these runners.
This will save the company $4,586,400.
The summary of the data and
calculation for ROI can be seen below.
Cost of Labor without Overhead Hoist 2020
$13,025,376
Cost of Labor with Overhead Hoist
$8,438,976
Dollars Saved 2020
$4,586,400
Cost of Overhead Hoist Transport
$590,766
ROI
.1288 years
Figure 3.1: Return on Investment Results During Cytels First Year of Operation For MHS Design A
Question 3b
The MHS Design A can be seen in the figure below.
The OHT is around the perimeter of the
machine stations and takes or drops product off from the stocker area.
The OHT will reduce the need for
humans to touch and ruin the product, which will increase the yield of the system.
This design was
influenced by the design from Gaurav K. Agrawal (Agrawal, 112-120) and can be seen below.
20
Figure 3.2: MHS Design A
Question 4
Problem Statement: Determine another MHS design (“MHS Design B”) that interacts with your Design A
in (3) to further improve output from the process tools. (Hint: Think about sources for Gap
inefficiency.)
Assumptions:
1.
An AGV cost $100,000 according to Barbara Soto, a marketing analyst at Murata Machinery
USA, INC.
21
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2.
The AGV’s are advanced enough to load wafers into machines and remove wafers from machines
without the aid of human operators.
3.
In terms of space requirements to allow each AGV to move freely and for storage/charging, we
assume each takes up a five foot by five foot area.
5.
Although AGV’s will replace human machine operators, one human worker will still be required
to oversee their task execution and maintenance in each department.
6.
Whereas one human operator was required for each machine in MHS Design A, one AGV is
required for every four machines, based on internship experience and speed of AGV’s in relation
to machine cycle time.
7.
The ROI for MHS Design B is based on its implementation during the Cytels second year of
operator starting the first quarter of 2021. At this time the Overhead Hoist system in MHS Design
A is already in place.
8.
In each department there is one operator each shift inspecting all wafers before they leave the
department area. These workers are not replaced in each MHS design iteration and therefore are
left out of ROI calculations (Duplicate, see Question 3 Assumption 2) .
9.
To calculate charging cost, the AGV’s each contain a 75 kWh battery pack with 85% charging
efficiency, which is comparable to the rechargeable battery pack used in a 2018 Tesla Model 3
(Sendy, 2018).
10.
The human employees working as runners are between the ages of 20 and 39 years moving WIP
wafers at speeds 1.34 to 1.43 meters/second (Abdel-Malik & Arora, 2013).
Question 4a)
Problem Statement: Present your “MHS Design B” and draw it on the layout
22
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Figure 4.1: MHS Design B
Question 4b)
Problem Statement: How would you justify a return on investment for this MHS Design B?
Design B incorporates AVG’s which will be replacing
human operators loading and unloading
machines. However humans workers, one per shift per department, will need to be hired per assumption
5. Their battery life lasts for 12 hours, so each day one AVG works for 1.5 shifts while its alternate
charges then halfway through the day they are swapped. Therefore two AVG’s will be needed per day to
fulfill the role of loading and unloading the machines. The AVG’s work fast enough, with an unload and
23
For which part of the facility?
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load time of 10 seconds, to cover four machines at a time. The only exception to this are machines that
have a cycle time under a minute, Wet Etch and Ion Implant for Products B and C
,
which will require 2
AVG’s per machine.
Using a simple example to clarify, if a department had four machines needing to be operated for
three back to back eight hour shifts a day, MHS Design A requires paying wages of 12 human operators
whereas MHS Design B requires paying for the electricity to charge 2 AGV’s and the wages of 3 humans
to oversee the AGV’s. The price of electricity to fully charge an AGV is $11.47 ped day, based on the
battery pack, the 13 cents per kWh price of electricity, and charging efficiency stated in assumption 9
(Sendy, 2018). These factors were accounted for on a quarterly basis for the year of 2021 to calculate the
ROI shown in Figure 4.2
below.
Cost of Labor with Overhead Hoist 2021
$20,409,480
Cost of Reduced Labor with Overhead Hoist &
Charging AVG’s
$1,363,766
Dollars Saved 2021
$19,045,713
Cost of Purchasing AGV’s
$7,200,000
ROI
0.378 years
Figure 4.2, Return on Investment Results Cytels Second Year of OperationFor MHS Design B
Question 4c)
Problem Statement: Create an example of MHS Design B and apply it to (1) to defend your proposed
design.
Using the simulation program Arena we modeled MHS Design B. As it would be implemented
during 2021, the number of machines needed was increased to meet the demand for each product starting
in Q1 of 2021. The main element that was changed from previous simulation parameters whas the gap
24
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time. While our base case, which transported, unloaded and loaded WIP wafers using only human
workers moving at speeds 1.34 - 1.43 meters/second established in assumption 10,
the MHS of Design B
encorparted AVG’s and the Overhead Hoist system which transports wafers at twice that speed
(Abdel-Malik & Arora, 2013). Therefore the gap decreases from 15% to 7.5% making our daily
productive time per day in Arena 12.1 hours up from 11.1 hours used in question 1. The resulting number
of machines required to meet demand for product A in both scenarios can be seen below (Fig 4.3 & Fig
4.4).
Figure 4.3: MHS Design B; Total Machines Needed Each Quarter For Product A Beginning 2021
Figure 4.4: Only Human Workers; Total Machines Needed Each Quarter For Product A Beginning 2021
25
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Not only were less machines required each quarter, but as shown below in Figure 4.5, during the
first week of Q3 2021
with demand of
811 units per week
the total time a single wafer spent waiting in a
machine queue and the total time to fabricate a single wafer decreased on average.
Figure 4.5: MHS Design B vs Only Human Workers;Queue Waiting Times Product A
Question 5
Problem Statement:
Recommend a WIP policy that would be most suitable for our factory and identify
which indicators should be tracked.
Question 5a
For our WIP strategy we are aiming to reduce the amount of time that a lot will spend in our
production facility by implementing a Just in Time (JIT) production schedule. By implementing a JIT
schedule we will be manufacturing our products at the rate that Cytel’s customers request them, thus
ensuring that we will be minimizing the total time that a lot spends on our production floor. In addition to
suggesting a JIT production strategy we will also have the operators assist each other if they encounter
problems in the manufacturing process with the goal of assisting in their peers problems before starting a
new work order on their machine.
There are six important performance metrics with their effects seen in
the figure below.
26
Note: JIT is only one WIP policy, and simplistic. There are better
policies that we prefer, related to JIT but refining it smartly.
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Figure 5.1: Performance metrics
Question 5b
A facility that has a poor WIP policy generally has long cycle times, many bottlenecks, and a
lower than expected production rate. The six metrics that we chose to analyze our WIP policy in the
previous question can all be used to effectively analyze any sort of WIP policy and ensure that the factory
is operating in an effective manner. In the example of a poor WIP policy it will have a low throughput
rate, a high cycle time, high starve downtime, high slack downtime, and a longer than expected time that a
product spends in inventory. If we were to apply our six metrics to a poor WIP policy with the intent of
improving all of the metrics we would find that the factory would function better because it would have
fewer bottlenecks, less unproductive downtime, and more overall profit for the company.
Question 6
Problem Statement: Design a production line for a sedan with a price tag of “expensive” or higher.
Assumptions:
1.
Factory size is small (26x27 or 702 sq ft)
2.
Expensive car range: $45K-$100K
3.
Standard car means has no additional features added
27
Note: PLS as assigned here is not relevant to Cybell
Nice, but:
How are they related
to this design case? Why not
illustrate a few with numerical
examples
that can help clients
make decisions
?
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Question 6a
In designing the production line for an expensive sedan, one of the most important things to take
into consideration is the features available. Several upgraded features were added to justify for putting a
higher price tag on the car. All of the features selected are listed as “very rare” on PLS, but a few of them
are more commonplace in reality (such as airbags or electric windows). The rest of the upgrades,
however, were selected based on features found on similarly priced models from different brands. A full
list of these added features can be found below in Figure 6.1.
Figure 6.1
:
List of Upgraded Features
The final production system consists of 22 types of stations, as opposed to the standard 8 stations.
These additional stations are essentially “sub-stations” of the standard ones and are responsible for adding
the bonus features. For example, “fit door panels” is responsible for adding the car alarm, central locking,
and lane departure warning function. The layout of the production line is as follows:
28
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Figure 6.2
:
Basic Flow of Assembly Line
Several key performance measures are listed in Figure 6.3 below to demonstrate how well the
production line is performing. Vehicle production per hour lets the company know if the assembly line is
producing enough cars to meet customer demand and whether or not they should have more lines to keep
up. Knowing the percentage of vehicles running and slots running provides information on possible
bottlenecks and if any of the machines are idle for significant portions of time. This is helpful in
determining how many stations should be placed. Production costs and market value provide financial
statistics on profits and how money is being spent.
29
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Figure 6.3: Performance Metrics
Question 6b
It takes 2 hours 54 minutes for the designed assembly line to produce a car with additional
features, as opposed to 2 hours and 8 minutes for a standard line with 7 stations producing a car with no
upgrades (see Appendix C for time calculations). Significant bottlenecks occur at three stations: fit body,
paint, and fit engine. These three stations take up a significant amount of time to run, leaving the stations
that follow immediately after idle for most of the time. Thus, more stations were added to alleviate the
bottleneck situation.
Figure 6.4: Number of Each Station in One Assembly Line
With this production line design, one assembly line can produce 4 cars in an hour. Given that
there are 66 customers that visit each hour, the demand is estimated to be around 33 to 50 cars. To keep
up with this demand, more than one assembly line is needed. However, as more cars are produced, the
30
How are these related to the required
performance ratios?
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in-game COO sends a message warning that there are too many cars being produced. Thus, the team made
the decision to stick with producing 4 cars, despite having a high number of customers visit the
showrooms. The current production system only consists of one assembly line, which has a factory space
utilization of 30%. However, if customer demand increases in the future, there is plenty of space to build
additional assembly lines to increase total car production.
With the addition of 15 stations, there are increases in the number of employees (from 35 to 83),
space used, and number of items imported. This results in a significant rise in costs, nearly four times the
expenses for a standard car, shown in the figure below. However, the profits make up for the costs. The
market value for the designed expensive car is $57,538, while the standard car is only $16,360. On
average, there are 5 cars sold every hour. This results in a profit per hour of $177,672 for the expensive
car, while the standard one has a profit of $54,083. As long as there are more than two cars sold each
hour, the profit from the expensive car will outweigh that from the standard car. Appendix I compares the
profit differences depending on the number of cars sold.
Figure 6.5: Comparison of Hourly Expenses
Question 6c
Assumptions:
1.
Doesn’t consider the purchase cost of production machines and additional supply stockpiles
2.
Taking into consideration the cost of raw materials, number of parts consumed, power costs, and
additional workers cost
31
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Instead of importing all of the components, the production line could manufacture every part in
house to save on delivery costs. In case 3, the team already came up with an equation to determine in
house production costs.
Figure 6.6: Equation for Hour In-House Production Costs
Using this equation, savings for the five most expensive items can be found below. Not all of the
costs were calculated, but it can be seen that producing components in house is generally cheaper than
importing them. However, it should be noted that producing components in-house will take up factory
space. This is potential space that could be used for an additional assembly, provided that the customer
demand calls for an additional assembly line.
Figure 6.7: Import Costs vs. In-House Costs
32
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Question 6d
With different teams working in separate areas, this allows for individuals with specific skill sets
to work with their strongest areas. In the PLS simulation, the body of the car is built first. The specific
details and upgrades are then later installed by another team of specialists. By separating these processes
and having multiple teams work on the car, this lowers the possibility of errors and increases the quality.
Teams working with specialized skills in a specific area will have higher success and quality assurance
rates than non-specialized teams completing multiple tasks at once. Furthermore, having all the stations
working at once allows for the production of multiple cars at once. As mentioned earlier, one car takes
almost 3 hours to assemble. At this rate, if the assembly line only worked on one car until completion, the
company would lose money since the production costs would outweigh the value of the car. Fortunately
though, the designed assembly line produces four cars in one hour, which increases the gross profit, since
the machines are constantly running.
Companies need to be aware of updating their products and services on a regular basis. Without
innovating products, companies run the risk of losing potential customers to competitors. Technology is
constantly improving, so non-updated products quickly become less desirable. The components used in
this PLS design run this risk. Some of the standard features of this car are very out of date (such as roll
down windows). If customers saw cars nowadays with no electric windows, there is a very small chance
they would be interested in purchasing one. Some of the upgraded features included in the design like
cruise control may seem innova tive now, but within a couple of years, they could become unnecessary as
self driving technology improves. In addition to improving products, companies should consider spending
time to update/develop better production systems and setup of their facilities. This could help companies
produce more goods, while maintaining or even improving the quality of their products.
When designing this production line, the team took used several engineering concepts to optimize
the assembly flow. Modular design played an important role in this assembly line. Bigger stations like the
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“fit body” station were broken down into smaller sub-stations, such as “fit bodyshell”, “fit trunk”, “fit rear
bumper”, and “fit door panels” so that additional features could be added. Modular design improves
efficiency and allows for parts to be worked on independently. Another design consideration was keeping
related stations together and the whole assembly line close to the resource importers. This makes the flow
easy for anyone to understand and reduces the amount of time that components need to travel. Finally, the
presence of bottlenecks were monitored and additional stations were added to reduce the idle time of
machines, essentially maximizing the “value” of each machine.
Summary and Conclusion
The team was tasked by Cytel to provide a recommendation on the initial setup of their Integrated
Circuit facility.
The group was given an overview of the IC creation process, the machines that must be
included, a general layout plan, production routes, and quarterly demand.
The goal was to determine the
amount of machines that are needed at any given time, deciding appropriate material handling systems
that have a low ROI, and design a facility layout that includes these.
The first step was to create a simulation to show how many machines are necessary for each
quarter.
The team used linear interpolation to smooth out the factory demand for Product A to obtain a
weekly demand and show exactly when a new machine would be required.
The amount of machines,
from the team’s simulation, can be shown in the figure below.
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Figure 3: Summary of amount of machines needed in each quarter
The next step was to design a baseline optimal layout of the IC facility to support production of
the products.
The team considered the fact that the layout needs to have a cleanroom, the maximum
amount of machines for the next two years, and the relationship between the machines.
The team created
two different layouts to compare and decided on one optimal layout.
The results and comparison can be
seen in the table below.
Figure 4: Summary of different layout designs
The final step was to recommend two new material handling designs that would reduce the risk to
yield and limit the gap or “standby time”.
The first design the team recommends is that of an Overhead
Hoist Transport.
This will limit the amount of vibrations occurring in the movement of products.
The
second design is the use of AGVs within each bay.
This will be quicker than human movements and will
save labor costs in the long run.
The total ROI can be seen below.
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Figure 5: ROI summary table for Design A and Design B
In conclusion, the team believes that there is a lot of potential for Cytel in the IC market in the
coming years.
The team’s solution will benefit the production of the product for this company and allow
for a large amount of future growth in this market.
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References
Abdel-Malik, K. A., & Arora, J. S. (2013). Human motion simulation: predictive dynamics. Amsterdam:
Elsevier.
Agrawal, G. K., & Heragu, S. S. (2006). A survey of automated material handling systems in 300-mm
SemiconductorFabs.
IEEE transactions on semiconductor manufacturing
,
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(1), 112-120.
C N Wang et al 2016 J. Phys.: Conf. Ser. 710 012033
Everything You Need to Know About Health Club Locker Room Size. (n.d.). Retrieved November 10,
2019, from
https://www.ihrsa.org/improve-your-club/everything-you-need-to-know-about-health-club-locker
-room-size/.
Sendy, A. (2018, December 18). The cost of charging a Tesla-and how it compares to gas vehicles.
Retrieved November 12, 2019, from
https://www.solarreviews.com/blog/how-much-does-it-cost-to-charge-a-tesla-is-it-the-same-as-th
e-cost-to-charge-other-electric-vehicles.
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Appendices
Appendix A
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Ourpose of this Appendix?
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Appendix B
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Purpose? Assumptions needed?
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Appendix C
Appendix D
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PLS not relevant to our DC4
PLS not relevant to out DC4
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Appendix E
Appendix F
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Appendix G
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Based on what analysis? Assumptions needed for this analysis?
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Appendix H
\
43
Comparisons to queuing models analyzed?
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Appendix I
44
PLS not relevant in our DC4
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