The Purdue Industrial Engineering senior design team was tasked by Fiat Chrysler Automobiles to identify issues for the 9-speed transmission machining process using the Theory of Constraints methodology. Through data analysis, the team determined the bell housing line's OP 30 machines were the bottleneck. To address this, the team conducted an availability project to eliminate part seat detection faults and two process projects to reduce cycle times. Video analysis was used to evaluate cycle times and identify inefficiencies. The goal was to optimize the OP 30s and improve throughput for the 9-speed transmission line.

1. Purdue Senior Design Team
Fall 2015
Final Report
Fiat Chrysler
9 Speed Transmission Project
(CHR948TE)
Aditya Das
Akshit Singh
Arnaud Marquet
Omar Modak
Zach Taylor
December 11, 2015

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Table of Contents
-Executive Summary 2
-Introduction 2
-Statement of Problem and System Definition 3
-Team Composition and Roles 5
-Tasks and Methodologies 6
-Outcomes, Evaluation, Benefits, and Constraints 22
-Suggestion for Implementation, Additional Analysis, or Supplemental Project Work 32
-Appendices 36

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Executive Summary
The Purdue Industrial Engineering Senior Design team has been tasked by Fiat Chrysler
Automobiles (FCA) to use the World Class Manufacturing methodology and Theory of
Constraints to identify issues for the 9 Speed Transmission machining process. Theory of
Constraints means that a chain is only as strong as its weakest link. Therefore, the first task that
was given by FCA was to find the bottleneck by analyzing the available data. The team analyzed
top issues related to efficiency, availability, and quality to determine ways to improve the
process bottleneck. The team used macro and micro analysis through industrial engineering tools
such as throughput and overall equipment effectiveness to aid in our project analysis. The project
objective was to identify process bottlenecks on the 9-speed transmission machining line by
using data gathered in pivot tables. Once a bottleneck was identified, the process was observed
by taking a video of one of the machines affected over a whole cycle and potential solutions
determined to improve the bottleneck.
Introduction
The Fiat Chrysler Automobiles (FCA) transmission plant in Kokomo began production in
1956. The floor space for the plant is 3.1 million square feet, with acreage of 110 acres including
the casting plant. The site has 3,326 employees operating on a 3-2-120 work pattern, which
means three crews, two shifts, and 120 total hours per week. The machining of engine block
castings and transmission components (aluminum and steel) and the following assemblies take
place at the plant:
1. 62TE: FWD - Chrysler Town & Country, Dodge Journey and Dodge Grand Caravan
2. 40TE/41TE - 40TES/41TES: FWD - Dodge Journey

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3. 845RE: RWD - Jeep Grand Cherokee, Chrysler 300, Dodge Charger, Dodge Challenger,
Dodge Durango and Ram 1500
Since 1974, the Kokomo transmission plant (KTP) has built more than 67 million
transmissions. The 17-million four-speed milestone was achieved over a 25-year period from
1988 to December 2013. The plant began building the six-speed in 2006 and reached the three
million mark in April 2014. In December 2014, KTP achieved the Bronze award level in World
Class Manufacturing after demonstrating clear know-how and competence in the manufacturing
methodology. World Class Manufacturing (WCM) is a methodology that focuses on reducing
waste, increasing productivity, and improving quality and safety in a systematic and organized
way. WCM engages the workforce to provide and implement suggestions on how to improve
their jobs and their plants.
Statement of Problem and System Definition
The task handed to the Purdue Industrial Engineering Senior Design team was to identify
issues for the 9-Speed Transmission machining process related to efficiency, availability and
quality. The focus of this task, as requested by the customer (FCA group), was to determine the
bottlenecks in the process lines by exploring a wide range of data pertaining to each individual
machine in all the manufacturing lines from bellhousing to casing. The problem was described
using the 5W+1H method, which is FCA’s problem identification system, which consists of the
what, when, where, who, which, and how of the problem. Through an initial evaluation, it was
determined that the 9 speed transmissions bellhousing line had the lowest throughput and was
considered a potential bottleneck. This problem occurs during continuous running and operating
and every cycle is affected. Through a microanalysis, the bottleneck is apparent on the op 30’s
and it affects all of the operations in the bellhousing department. The problem is confined to the

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machines in bellhousing and do not account for the operator’s efficiency issues. Therefore, the
objective as perceived by both the customer and the Purdue Industrial Engineering Senior Design
team is to optimize the op 30’s in the bellhousing department. In summary, the 9 speed
transmissions, bellhousing, op 30 is not meeting the daily production target which affects the
output of the department and the customer.
The system is defined as the bell housing process line of the 9-Speed Transmission
manufacturing department. There are two identical phases of the bellhousing lines that consist of
the op machines. A representation of the two phases is given in Figure 1. The material is picked
up from the bins and flowed into the different departments consisting of the op machines. Once
the part has been processed, it flows into the leak testers where they are tested.
Figure 1: 3-D layout of bell housing process line
If they fail the test, they are scrapped and if they pass they are placed in containers for the next
assembly process. Figure 2 presents a 2-D layout of the bellhousing production line. The
screenshot was taken of the FIS software that FCA group uses to monitor the machine states. The

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objective was to optimize this system by finding potential bottlenecks through the Theory of
Constraints method as requested by the customer.
Figure 2: 2-D layout of bell housing process line (FIS)
Team Composition and Roles
1. Aditya Das: Logistics Management
As logistics manager, he was responsible for collecting data, inputting it into the pivot
tables and obtaining results. He assisted with other tasks in the project such as analyzing the
production lines to determine bottlenecks in the system.
2. Akshit Singh: Creative Innovator
As the technical leader, he was in charge of data collection, organizing the results and
making them presentable to both the customer and instructional team. Also, he had the
responsibility of analyzing the data and directing the team to best approach the next step in terms
of data collection and project progress/implementation with regards to the theory of constraints.

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3. Arnaud Marquet: Technical Leader
As the creative innovator was in charge of project write ups and presentations and
coming up with ideas on how to best present the data/result in a functional and feasible ways in
which all parties are able to comprehend the project. In addition, the creative innovator was in
charge of methods of data collection and results gathering on site.
4. Omar Modak: General Support
As general support, he was helping with the collection of data and the communication to
team members. He was also in charge of updating the Gantt chart, monitoring the time tracking
sheets and contributing to both internal and external deliverables where the most help was
needed.
5. Zach Taylor: Project Manager and Communications Coordinator
As project leader and communications coordinator, he was the primary point of contact.
He was in charge of allocating team responsibilities, monitoring project progress and setting up
team meetings amongst team members. Moreover, he was responsible for communicating with
FCA and the instructional team on behalf of the team. In addition, he was in charge of
assignment submission on behalf of team and helping out with project completion across the
board.
Tasks and Methodologies
For the team to come to an understanding and conduct analysis as well as complete the
project for FCA, multiple trips were made to the transmission plant in Kokomo. The first trip
was on 9/25/2015 where the team toured the plant and got an idea of how the entire 9-speed
machining line functioned. Moreover, during the first site visit, past FCA Theory of Constraints
projects were discussed and the team got introduced to tools the FCA industrial engineering

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department uses such as power pivot tables. These pivot tables are available on Microsoft Excel
2013. The data provided by FCA contained 10 million data points at a time therefore, utilizing
pivot tables the team would be able to specify which data they are looking for over which
specific range of time (see appendices A on the excel file that was used for data analysis). For
example one can see throughput for the 9-speed machining casing line for the month of August
and then dig further into which operation within the casing line was causing overall throughput
to lag. Therefore, seeing how to use power pivot tables allowed the team to begin conducting
data analysis to figure out what the bottleneck of the 9-speed machining line was. Over the
course of the next couple of weeks the team analyzed the data source file and conducted data
analysis with regards to 9-speed machining line. With the initial analysis the team tried to find
the bottlenecks with regards to each machine make and with regards to each machining line. The
team decided upon a date range from 7/6/2015 to 10/12/2015, since the data was up to that date
and three months seemed adequate to find a lot of data on significant 9 speed bottlenecks. Figure
3 and 4 show the data found over this range with regards to machine make and line type.

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Figure 3: 9 speed machining bottleneck data with regards to machine make.
Figure 4: 9 speed machining bottleneck data with regards to line type (largest bottleneck for each
line highlighted in red)

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As the team grew more comfortable and familiar with power pivot tables over the course
of the month, and with help from the FCA point of contact, upon the next site visit the team was
able to figure out the bottleneck of the 9 speed machining line. By this time the range of data was
just reduced to the month of October, since there were machine as well as line shutdowns over
the previous months that affected the initial data. Therefore, for the month of October all FCA 9
speed machining lines were functioning, thus allowing for comprehensive analysis throughout
the month. On 10/30/2015 the team made another site visit where the results of our bottleneck
findings were shown to our FCA point of contact.
Figure 5: Throughput of total good pieces over the month of October
The graph above shows that the bell housing department has the lowest throughput.
Therefore, the team further analyzed which department within bellhousing has the least

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standalone jobs per hour and is causing the bottleneck. The bellhousing department consist of
five departments from the op 10’s to the op 50’s.
Figure 6: Stand-alone jobs per hour with respect to bell housing departments
This showed the team and FCA that the op 30’s was the bottleneck of the department.
Moreover, initial micro analysis graphs were made with the use of power pivot tables by the
team to show the bell housing department, specifically the bottleneck being the op 30’s, in terms
of availability performance and quality. By visualizing these graphs the team could work on
projects to help improve bellhousing stand-alone jobs per hour thus improving the bottleneck.
Figures five through nine discuss the micro analysis of the op30 department that lead to project
selections.

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The point of micro analysis according to theory of constraints is to see the system
bottleneck in terms of availability, process and quality and then assign projects to improve the
bottleneck in terms of these projects. Eliminating the worst performing factors of availability,
process, and quality will improve the bottom line, thus increase stand alone jobs per hour. In
Theory of Constraints a department is only as good as its worst performing job. Therefore, to
improve the op 30’s micro analysis was conducted with regards to availability, process and
quality and projects were assigned based off of this. Availability will be discussed for the bell
housing department with respect to op 30’s over the month of October.
Figure 7: Availability of op30 machines over the month of October
As can be seen the availability is around eighty-two percent. Therefore, further analyzing
issues with regards to availability a top ten issues graph was made of the op 30’s bell housing
department.

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Figure 8: Top 10 part defect faults for op 30’s
As can be seen from the graph the top down time fault issue was coolant related, however
a project was already in place beforehand, therefore the team was tasked to solve part seat defect
fault the second biggest issues on the graph. This was our first availability project to help
improve stand alone jobs per hour. Next the performance of op30’s over the month of October
was analyzed, since it part of the theory of constraints project methodology. Improving the op30
process will help with reducing the bottleneck.

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Figure 9: Overall performance of op 30’s
The performance was further analyzed in terms of cycle time for the op 30’s to see which
machines were not performing up to par. The cycle time target for each machine is supposed to
be 419 seconds. The calculation for the target is shown below.

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Figure 10: Target time calculation and op 30 machine cycle times with respect to the target
As can be seen a lot of the machines in the op 30’s bell housing department need to lower
their cycle time to hit the target, marked with the horizontal orange line, of 419 seconds.
Therefore, the process project for the team was to find two ways to reduce cycle time, thus
increase stand alone jobs per hour. The last aspect of micro analysis was seeing op 30’s quality
over the month of October. Figure 9 shows the quality graph.

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Figure 11: Quality of op 30’s over the month of October
Based off of the graph in figure 11 it can be seen that the quality of op30’s are all over
99.9 percent. This was due to the fact that all quality checks are done at the op 50’s department
for bell housing and rejected there, since op 30’s is our bottleneck our team did not have to
conduct a quality project. That is why we had to process projects instead with regards to cycle
time reduction.
To solve the problems described earlier, with regards to improving the bell housing
Op30’s, which is the current bottleneck on the 9 speed machining line, the team conducted three
projects with regards to Theory of Constraints; the first was an availability project, the second
and third were both process projects. These projects were solved over the next site visits over the
course of November.

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For the availability project, as mentioned earlier part seat detect fault was the issue,
therefore the team needed to see the machine and how it worked on the floor to try and come up
with a solution. Therefore, on 11/13/2015 the team went on another site visit and analyzed the
current machines to try and eliminate the part seat detect fault on the op30’s. The team found out
that this issue was from the sensor not being able to detect whether the part was placed on the
machine or not. This could have been due to a variety of reasons the first being that there is too
much coolant that is blocking the sensor or that there are a lot of chips in the machine due to the
machining which is not allowing the sensor to see whether the part is seated or not. This
availability project was a carryover project from FCA as mentioned earlier since they are
working on the top issue, which is coolant fault related. Therefore, the team took it a step further
by analyzing the next issue with regards to part seat defect. Figure 12 shows the problem with
regards to the fault upon completion of our observation and analysis.
Figure 12: Part seat defect fault problem (sketched image)
As can be seen the switch does not detect the part. Therefore, the team thought that an
easy fix for this problem this problem is to have the robot communicate the status of the part

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being present directly to the NTC machine. This disables the switch detect part, which in turn
will eliminate the part seat detect fault, thus increasing stand alone jobs per hour for the op 30’s
bell housing department.
Figure 13: Part detect seat fault solution (sketched image)
The disabled switch is irrelevant once the robot directly communicates part presence to
the NTC machine. The tool engineers will work on these changes to improve production and
disable the part detect switch. The financial benefits and minutes saved with regards to
implementation of the availability project will be discussed in the outcomes, evaluations, benefits
and constraints part of the report. Now, on to the first process project.
The process project was with regards to reducing cycle time to 419 seconds. Therefore, to
reduce cycle time the team had to analyze op 30’s machining cycles. This was done by recording

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a video that was a little over seven minutes and using that to analyze the process and to find
inefficiencies with regards to the machining movement. The video was recorded by the team
with a gopro camera that was placed inside of the op 30 in an angle where all the machining
movements for the op 30 process could be seen. The video was recorded on the site visit
11/13/2015. The goal for the process project for the team was that any time saved in this process
will result in more parts being processed per shift.
Upon recording the video and collecting data on the op 30’s machine cycle time the team
began performing video analysis (see appendices B for data tables with regards to cycle times).
The figures and charts were made and further analyzed during and after the team's final plant
visit on 11/20/2015. The following figures are of the entire machining process time in seconds
for the op 30’s and the cutting process time in seconds as well for the op 30’s.
Figure 14: Op 30 overall machining process
Based off of the overall machining cycle time chart, the team saw that the cutting process
takes up the bulk of the time, 416 seconds, and has room for the most improvement in terms of
motion time studies and improving any inefficient machining movement for the op 30’s. This led
to the team analyzing the cutting process and collecting data off of the op 30 machines in terms
of the actual cutting processes and the seconds break down of each cut from the beginning of the
process to completion at 416s. The cutting process is broken down in figure 13.

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Figure 15: Op 30 cutting process
As can be seen from the cutting process chart the cycle time of all 28 steps totaled is 416
seconds. Which is in line with the overall machining chart that shows the same time for the
cutting process. Moreover, with the team performing video analysis there was an inefficient
movement that was spotted. The movement happened after a tool change at the 286th second
which is step 20 of the cutting process, where one second could be saved. This one second saved
time is shown in the next figure.

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Figure 16: Op 30 cutting process with proposed change (in red is the 1 second saved)
As can be seen one second is proposed to be taken out at step twenty. Based off of that
the next process project to reduce cycle time takes place. The next movement inefficiency,
according to the team's video analysis, is 292nd second where there are 2.5 seconds that can be
saved. This is shown in the next figure where the previous projects one second is already taken
into consideration.

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Figure 17: Op 30 cutting process with more proposed change (in red is the 2.5 second saved)
As can be seen the cycle time is 415s based off of the 1 second from the first process
project. Moreover, the 2.5 seconds in red is going to be taken out from step 26 to reduce the
overall time of the cutting process, which in turn reduces the op 30’s machining time.
This results in a total saved time of 3.5 seconds over both process improvement, cycle
time projects. The financial benefits and seconds saved with regards to implementation of the
both of these process project will be discussed in the outcomes, evaluations, benefits and
constraints part of the report.
The most important item that the team used to stay on track with their deliverables, while
going through their respective academic semesters, and complete FCA’s project on time, with
regards to FCA’s schedule and the instructional team’s schedule, was to keep an updated Gantt
chart which was used to get everything done in a timely manner. The Gantt chart was made to

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guide the team in terms of plant visits and project deliverable completions for FCA. The figure
below shows the completed Gantt chart.
Figure 16: Purdue Senior Design Team Gantt Chart
The Gantt chart was used by the team to stay on track and meet FCA deliverables and
complete everything in a timely manner, with the dark blue tasks being main tasks and the light
blue tasks being subtasks of that main task. This tool allowed for all the analysis to be done by
thanksgiving, thus allowing for presentation preparation over week 15 and report writing over
weeks fifteen and sixteen.
Outcomes, Evaluation, Benefits and Constraints
The team successfully finished three, Theory of Constraints, microanalysis projects to
improve the bottleneck of the bell housing department. The first project was an availability
project that was a carryover from FCA’s previous project. For this project, the customer will
make software changes to allow the robot arm that places the part in the machine, to
communicate the status of the part present to the NTC machine and allow the machining to
begin. This allows the customer to remove the part detect switch and eliminate any fault time

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related to it. The graph in figure 18 shows all the part detect faults and the team was able to
completely eliminate the second highest fault time by presenting this project. Although the
highest fault time was related to coolant pressure, the team was asked to ignore it. This was done
in anticipation that the customer are already in the process of eliminating this problem.
Figure 17: Part detect seat fault solution (sketched image)
Based on the changes suggested, the plant expects to save 900 minutes per month. This
number is shown in the figure below and it is expected that the customer will have this step
implemented by next year.

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Figure 18: Part detect faults with Seat detect fault highlighted
In order to get a monetary valuation on the time saved, a Kaizen Benefit/Cost Worksheet
was provided to the team. This is a FCA specific tool to convert time into dollar amount. It takes
the number of parts lost per day to calculate the overtime required to produce those parts. This
number is then multiplied by the number of workers in each category and their rates. The
following displays the result of the first project completed by the Purdue team.

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Figure 19: Kaizen Benefit/Cost Worksheet for availability Project
This worksheet was completed with sensitive data provided to the team by the customer
and the analysis data filled in by the team. The team calculated the daily production rate based on
the customer’s target of producing 800,000 parts in 2015 with 9.5 hours per shift and two shifts
per day production schedule. This gave a target of 2800 parts required per day and, using the 900
minutes down time, it was calculated that 21 units were lost each day the targeted fault existed.
In order to make up for the lost units, the company would have to consider 42 hours per year of
overtime. This overtime would go to four set of employees: non-skilled operators, skilled
operators, floor manager and a salaried engineer. Their rates and number of each category of
employee needed were given to the team by the customer. Therefore, the overtime amount was
multiplied with the rate of each employee and then multiplied by the number of each category.
This was the final dollar amount, saved by the company, of $37,646 annually. Since this was a
carryover project, the team and customer agreed that no Stand Alone Jobs Per Hour (SAJPH)
calculation would be needed. To improve the SAJPH, the team completed two performance
projects related to cycle time analysis.

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The first performance project presents an opportunity of saving 1 second on the cutting
process. This can be achieved by making software changes and programming the cutting arm to
skip the pause time. This will allow a swift and continuous motion that would help cut down the
cycle time. The 1 second saved in step 20 of the OP30 machine cutting process reduces the
process cycle time from 416 seconds to 415 seconds.
Figure 20: Opportunity of improved cycle time (first performance project)
In order to find the significance of this opportunity, a Kaizen Benefit/Cost Worksheet
was used. Since the targeted machine was on the same line as the one analysed for the
availability project, the dollar rate and categories of employees remained the same. Also,
Production schedule and target were also kept the same. Using these values and the targeted
excess time of 1 second, it was calculated that 8 units were lost per day. In order to make up for
these lost units, the company would have to implement 16 hours of overtime. Therefore, similar

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to the method used for the availability project, it was calculated that the customer would have the
opportunity to save $14,341 annually.
Figure 21: Kaizen Benefit/Cost Worksheet for first performance project
The third project was a performance based microanalysis which was similar to the second
project. For this project, the team used the cycle time that includes the saved time from the
earlier project. However, the same cycle video was used for the analysis and, based on the
observation, an opportunity of saving 2.5 seconds was presented. This would reduce the cutting
cycle time from 415 seconds to 412.5 seconds.

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Figure 22: Opportunity of improved cycle time (second performance project)
In order to find the significance of this opportunity, a Kaizen Benefit/Cost Worksheet
was used. The values in the worksheet were kept the same from the previous performance
project, except the saved time was 2.5 seconds instead of 1 second. This resulted in 10 units lost
per day. In order to make up for these 10 units lost per day, the plant will have to allocate 20
overtime hours per year. This, using the same method as in the earlier projects, would cost the
customer an annual cost of $17,927. If the proposed project is considered and the changes are
implement, this cost will become savings for the company.

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Figure 23: Kaizen Benefit/Cost Worksheet for second performance project
Based on the three projects completed by the Purdue Senior Design team, an opportunity
of total amount saved was calculated. This number was simply the sum of all the annual benefits
calculated in each of the three Kaizen Benefit/Cost Worksheet. Therefore, the total amount that
the customer had the opportunity of saving was $69,914.
To calculate the increase in Stand Alone Jobs Per Hour (SAJPH), the team created an
excel spreadsheet with a sample cycle time sheet. Following figure displays the worksheet:

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Figure 24: Worksheet for calculating opportunity of SAJPH increase
The worksheet consists of the cutting process breakdown based on the recording. The
timing from each step was taken on site from the machine itself for better accuracy. It was
critical to understand that there are ten machines in each phase and are identical to each other.
This allowed the team to assume that any changes made to one machine could be made to other
machines as well. Based on this assumption and the data from the time cycle analysis from
before, the team calculated the total increase in production by saving the 3.5 seconds on cycle
time. This number was found to be 345.74 parts per year which, in turn, presents an opportunity
of increasing the Stand Alone Jobs Per Hour by 2.05. Following figure displays the improvement
made by the two performance projects put together.

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Figure 25: Total opportunity of SAJPH increase
An important consideration made for calculating the increase in SAJPH was to exclude
the time saved from the availability project. This was due to the fact that the availability project
was a carryover from a previous project done at FCA and the problem was already being
processed. However, a dollar amount saved by that project was considered in the final
calculation and the figure below shows the two main results achieved by the Purdue senior
design team.
Figure 26: Final results of the Purdue senior design team

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Suggestions for Implementation, Additional Analysis, or Supplemental Project Work
At the beginning of this project, the team was introduced to the process of using Theory
of Constraints and World Class Manufacturing methodologies to optimize a manufacturing line.
This is the same process that FCA has worked hard to establish and standardize for their
industrial engineers to utilize regularly. Typically, a team of industrial engineers working on a
project of this caliber would consist of six to seven members in a span of around six months. Due
to the fact that our team consisted of five members and the project span was only one semester, it
was known from the start that the process would not be fully completed come the semester's end.
The team set the goal of at least accomplishing step four in the process, the analysis portion. If
possible, the team would then proceed to step five and step six, countermeasures and results,
respectively. Step seven, standardization, was excluded from the start of the project due to time
constraints. In the end, due to staying up to date with our Gantt chart, the team was able to fulfill
every step of the process, up to and including step six, for three different rounds of optimization
through microanalysis.
The first round of microanalysis on the op 30’s in bell housing focused on availability.
The team was able to complete up to and including step six, with some room for additions in the
latter two sections. In summary, the team discovered that a malfunctioning part sensor could be
fixed by opening direct communication between the robot and the NTC machine on the exact
location of the part. It was then found that this solution could potentially save the company
$37,646 annually. Transitioning the remainder of this analysis to the FCA industrial engineers,
this process should be picked up by revisiting step five and step six. Before continuing, it will
need to be confirmed that the aforementioned countermeasures and their results are in fact the
best route for the company. Once this solution or another has been chosen and results verified,

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the team should proceed to implement and standardize the countermeasure. For example, if the
suggested malfunctioning sensor was chosen, the team would need to bring the issue to the
tooling engineers to remove the sensor and the computer or electrical engineers to set up the new
communication. Then, once the solution is implemented, the team should run further analysis to
confirm their theoretical results followed by closing this analysis and proceeding on to the next
bottleneck discovered by the Purdue senior design team.
The second and third rounds of microanalysis conducted by the Purdue team focused on
performance and were very closely related. Again, the team was able to complete up to and
including step 6, with room for additions in the last two sections. Overall, through video
analysis, the team’s second round of process streamlining found that the cutting time could be
reduced by 1 second in step 20 of the cutting process, resulting in $14,341 saved annually. The
third and final round of analysis revealed an inefficiency on the 292 second mark where 2.5
seconds can be saved, resulting in $17,927 saved annually. In order to transition the remainder of
these project to the FCA industrial engineers, they will need to start by revisiting steps five and
six for both rounds of analysis. Once the best solution and results have been confirmed, the FCA
team can proceed with step seven of implementing and standardizing the recommendations. For
example, if the aforementioned solutions were chosen, the team would bring the results to the
computer engineering team to discuss how reprogram the machines in order to fix the inefficient
movements. Then, once the team has confirmed the theoretical results with further tests, both
rounds of analysis can be finalized and the next bottleneck project started.
Up to this point, it has been mentioned, but never fully recognized that this new data
collection system and optimization process being implemented at the FCA Kokomo plant is is
very new process and has inspired Fiat to start implement projects of a similar fashion. In fact,

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FCA is using the Kokomo plant as a solo trial for these new tools and processes before
implementing them on a full scale throughout the company. Similar to the Kokomo plant, this
senior design project and team has been a trial to determine if this bi-annual program of students
utilizing these new tools and processes is beneficial to FCA and the development of its
optimization process. Past Purdue seniors design teams have conducted certain steps of the
process or utilized some of the tools. However, this is the first team and project to use every tool
and follow the process exactly as the full time industrial engineers do at FCA. Clearly, the trial
was a success, setting the foundation for future Purdue senior design projects and teams to work
successfully with FCA Kokomo. Semester by semester, Purdue teams will be able to pick up the
new bottlenecks following the results of the previous semesters team. As each semester passes,
not only will FCA Kokomo’s manufacturing processes be streamlined but the actual optimization
process and tools will be perfected over and over again. The result will be vastly increased
profits along with higher potential for FCA Kokomo to achieve not only WCM silver but WCM
Gold due to key contributions from the program. Not only will this continuously beneficial
program result in these great gains for FCA, it will provide invaluable experience to round after
round of Purdue engineers to prepare them for graduation and work in the industry.
Looking back at the project and semester as a whole, the entire experience was filled with
positive aspects and ideas with only a few areas that could use some improvement. To start, the
team was able to work with a majority of the industrial engineering department from the younger
engineers to management. This allowed the team to gain insight on how to work with different
types of coworkers in a real industry setting. Another great aspect of this program is that the
team was treated like full time industrial engineers instead of interns. All team members were
allowed to use any resource typically available to an industrial engineer at FCA Kokomo, and

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each team member felt as if they were fully trusted to conduct themselves appropriately around
the factory. Though a vast majority of the aspects in this project were positive, a few negatives
managed to appear. The first of these negatives appeared when the team was choosing a data set
to analyze. There were large portions of data that were unusable due to machines being down
and therefore data being skewed. This made the team use a smaller data set than initially
intended, however, the team was lucky enough that it sufficed for the required analysis. The
improvement that can be made here is simply to ensure there is a good chunk of data for the
Purdue team to use in the months prior to starting the project. The only other area of potential
improvement that existed pertained to the six month project scope being packed into a single
semester. This aspect was handled extremely well this past semester, but, there is definitely room
for improvement. For example, the following Purdue design team could complete the previous
teams step seven while initiating their own optimization project for the next team to wrap up.
Each team gets to complete the main body of their project and all projects get finalized through
every step. Overall, this project consisted mainly of positive ideas and aspects that created an
invaluable experience for the FCA Purdue Senior Design Team of Fall 2015.

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Appendices
A- FCA DataSource file with all up to date data on 9-speed machining

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B- Original Cutting Process and Machining Process data collected from Op 30 machines