Simulation of medical defibilator facility

THE UNIVERSITY OF TEXAS AT ARLINGTON
Simulation and Optimization
Project
Under the Guidance of
Dr. Brain L Huff
Submitted By:-
RAHUL RAMAKRISHNAN RAMESH
Student ID: - 1001266617
08/15/2016

Problem Definition
Our firm SimSolutions has been hired by MedAssist Corp on the Warranty and Medical Equipment
Lease Department to develop a detailed resource requirements plan that will be used to help them
bolster their rapid growth with incurring minimal additional cost. The main functions of the department
are:-
1. To recondition and repackage devices returned by hospitals in the leasing program
2. To repair faulty or damaged equipment’s received from users.
MedAssist Corp is consolidating the presently operated 4 separate line into a single facility.
Our aim is to simulate a model of the facility which accommodates 8000 units or more per day and
streamlining the process in such a way that the company has to spend very little for their expansion.
The streamlining of the process includes the following functions:-
1. Reduce the cost per unit conversion
2. Preparing a processing plan for the incoming lots contains of mixed items
3. Separate the incoming boxes according to the number of units contained in it
4. Predict the qualified personals and the equipment’s required
5. Assign right share for each product
6. Cycle time of the product less than its desired service level
7. To find where the bottleneck is and addresses it
Project planning
PHASE 1:- GANTT CHART
First a Gantt chart is prepared for scheduling the things to do and the time associated with it. As
deadline of the project is 11th
of August, 2016 things are split on the day to day basis except for the
week ends which is combined. The Actions to be done is shown with respect to the corresponding dates
on a Gantt chart as shown in figure (1.1 & 1.2). The color coding for the Gantt chart is as shown
below:-
Fully Fulfilled
Partilally Fulfilled
Did not Fulfilled

Jul 12 Jul 13 Jul 14 Jul 15- Jul 17 Jul 18-19 Jul 20-22 Jul23-24 Jul 25- Aug 1 Aug 2 Aug3- Aug 7 Aug 8-10 Aug 11-12
Planned
Actual
Planned
Actual
Planned
Actual
Planned
Actual
Planned
Actual
Planned
Actual
Planned
Actual
Planned
Actual
Planned
Actual
Planned
Actual
Planned
Actual
Planned
Actual
Implementation
Input Data Preparation
Model Translation
Final Experimental Design
Experimentation
Analysis and Interpretation
Date
Project Description, Class Notes
Project Planning
Project Definition
System Definition
Preliminary Expermental Design
Conceptual Model Definition
Figure (1)
The Gantt chart plans the order in which the project has to be completed. It also helps us track
the progress of the progress. This tool also helps us to decide whether we are on time or running late
according to the timeline set. With this we can roughly estimate the amount of resources needed during
the course of project. They also project us the decencies of the sequel operations on the once stated
before.
PHASE 2:- REQUIREMENTS PLANNING
With the Project description we can come to an estimate of following essentials:-
• Computer - 1no
• Witness Horizon Software - 1no

PHASE 3:- OVERALL LAYOUT (30000 FOOT VIEW PROCESS MAPPING)
The layout or the overall picture is important to for the project to take off. 30000 foot view is
the best tool to project the process involved at a high level. This gives us the idea before descending to
the detailed view. The communication channels are shown in between the sectors of the project.
Figure (2)

System Definition
Once we have an Idea of the process flow we try to investigate the process flow in detail of the whole
facility. We try to decompose the facility into separate entities with each of them interconnected to
each other. Let’s call the separate entities as areas. We divide our facility into 7 areas:-
• Receiving area
• Log in area
• Product repair area
• Lease reconditioning area
• 3rd Party Shipping Area
• 3rd party receiving area
• Shipping area
Now the detailed processes involved in the separate sections are as follows:-
RECEIVING AREA
On the receiving area as the input we receive the products from the federal express at the 4 times
and with the percentages split of the products as follows:-
• 5:00 am with 10% of the products, 7:00 am with 25% of the products, 8:00 am with 55% of the
products and 10:00am with 10% of the products
There are other shipments also arriving from the 3rd party repair facilities 4 times in a day with the
split up as follows:-
• 12:00 pm with 40% of the products, 3:30 pm with 25% of the products, 7:30 pm with 20% of
the products and 10:30 pm with 15% of the products
Input Machine People Output
FedEx Shipments Dummy machine Level 1 Operator Unit Login
Figure (3)
The incoming boxes may contain either 1 or 2 units. An algorithm should be written for the
same. The average incoming units per package is 1.8 and the total average of units incoming per day
is 8000 units per day. We need to separate the incoming boxes according to the number of units in

contains. The cycle time for handling the product is 7sec per package. This operation also requires
level 1 employee. The output from the receiving area goes to Log in area where devices are further
processed.
LOG IN AREA
Login area has two operations under it. The operations are:-
1. Box packaging
This unit helps in separating whether the package contains 1 unit or 2 units in them.
2. Unit Log in Work stations
The information relevant to the units is updated to the computer tracking system. The
information provided by the customer or the leasing partner is analyzed to determine what has to be
done with the units.
- This station separates lease equipment from the In House repair (Warranty) equipment’s
- This Separated equipment follows different routes
The Input items and percentage split for the In House repairs and the lease equipment are
- 678- J4 Units as 60%, 3000-J11 Unit as 25% and 4000-J11 as 15%.
Figure (4)
Box Unpacking Level 1 Operator Lease Reconditioning Area
In House repairs
3rd party Shipping
Level 2 OperatorUnit Login
Receiving Area
In addition to the above 13% of leasing equipment’s must undergo in house repairs.
The 3rd Party units are sent to 3rd party shipping area. The desired service level of the
equipment’s are of four levels and the percentage split of its arrival is as follows:-
• Level 1 - 12 hrs. 25% of units

• Level 2 - 18 Hrs. - 25% of Units
• Level 3- 24 Hrs.- 40% of units
• Level 4- 48 Hrs.- 10% of units
If a unit cannot be processed and shipped to the customer on the stated time period,
MedAssist ships a new product which costs for the company $300 extra.
REPAIR AREA
This area consists of a series of a parallel and independent cells. This line can process only
products of one technology. 2 hours of downtime is required for the reconfiguration of another
product.
Here we have 2 flow path for the operations.
1. The equipment’s are quick tested and 45% of the units go to Quick fix and sent to the Base Line
Functional Test & Calibration and follows the sequence of operations.
2. The other 55% of the units go through repair prep and repair station and follows the sequence of
succeeding operations.
At the base line functional test and calibration 85% of the units pass the test to follow the
sequence and the rejected ones go through the same procedures again joining the Repair Prep
station. After the sequence the unit is either sent to the Lease Equipment Re-Conditioning Area or the
Shipping Area depending on the unit’s history.
Quick Test
Repair Prep
Repair Station Technician
Loading and
Unloading
Tech Asst
Operator
Testers Automatic
Unit Adjustment Technician
Label Level 2 Operator
Final Check
Tech Asst
Operator
Unit History
Update
Logout
Unit Packaging Level 1 Operator
Quick Fix
Unit Login
Tech Asst
Operator
Level 2 Operator
Lease Reconditioning Area
and Shipping Area
Figure (5)

Areas to Focus:-
• Since the automated equipment of the Base Line Functional Test & Calibration is prone to
breakdowns we should have enough machines to keep the process running.
• The Mean time to Failure (MTTF) is based on the 8 operational hours. The mean time to Repair
(MTTR) follows a log Normal Distribution with a mean time of 40 min and a standard deviation
of 5 min.
• We have to make sure we have enough Test Technicians to avoid increase in breakdown time
of the machine and reduce the cycle time of the units.
LEASE EQUIPMENT RE-CONDITIONING AREA
This area reconditions the unit for the use by another customer. This area requires linear flow
path. The arrangement of the cells are product specific and any change in the cells requires a
downtime of 30 minutes.
Of all the lease equipment 13% of the units will require repair work. The units which has repair
work is again sent to the repair prep station and follows the same sequence as shown in the figure.
Unit Login and
code change
Level 1 Operator
Cosmetic Repair
Tech Asst Operator
Re-Programming
Station
Tech Asst Operator
Product Labeling Level 2 Operator
Product Literature
& Accessories
Assembly Level 2 Operator
Inspection Level 2 Operator
Unit Packaging Level 1 Operator
Shipping Area
Unit test
Unit Login
Tech Asst Operator
Figure (6)
AREAS TO FOCUS:-
• These testers have a mean time to failure (MTTF) of 7 hours following Negative exponential
distribution and Mean Time to Repair (MTTR) of 30 minutes and a standard deviation of 2.5
min following Log normal distribution.

• Analysis have to be done to have enough Test Technicians to repair the machines.
• Enough testers should be put avoid stoppage of the facility
SHIPPING AREA
The four major operations performed in this area are:-
• Late Unit processing: - This process is used by Med Assist to ensure the Repair Processing Time
Obligations are met. By the help of system tracking
• Lease Shipment Consolidation: - Designed to assemble 25 units of three products to three
different customers which gives us 9 combinations of products and customers. There are three
process involved namely consolidation, packaging and addressing.
• Label Printing:- this process is used to process individual customers
• Packaging:- this process is also used to process individual customers
Handling Level 1 Operator
labeling
labeling
boxing Machine
Lease
Reconditioing
Units
In house Repair
Units
Shipping Dock
Level 2 Operator
Level 2 Operator
Assembly machine
for packing 25 boxes
FIGURE (7)
THIRD PARTY SHIPPING AREA
The third party units after log in are sent to this area to be prepared for the shipment to
external repair facilities. After working on the products, they are dispatched to the shipping dock.
Figure (8)

Unit Login Preparation Level 2 Operator
Shipping
Dock
THIRD PARTY RECEIVING DOCK
After the repair at the outside third party facilities the units are logged in, printed shipping
labels and packaged to be sent to the individual customers. After the process the units are sent to the
Shipping Dock.
Figure (9)
3rd party repair
facilities
Preparation Level 2 Operator
Shipping
Dock
SHIPPING DOCK
This area hold all the outgoing units until FedEx arrive to pick them up. There are 3 pick up
each day for the customers or leasing companies and the 3rd party repair facilities have 4 pickups in a
day.
We have to maintain enough Level 1 Employee
3rd Party Receiving Area
Handling Time Level 1 `Operator FedEx Pickups
Shipping area
Figure (10)

Conceptual Model Design
As we have decomposed the whole project into 7 Phases in the system definition, we try to model
them on the witness tool.
PHASE 1:- Receiving Area
We model in such a way that the parts are stored on a buffer space before sending it to the receiving
area. A layout is shown for example in the Figure (1)
Figure (1)
The incoming units are either from leasing companies or customers in the morning and the 3rd party
returns in the evening. A dummy machine can be used to process the components with a level 1
operator.
The notable points in the area are as follows:-
• Arrival of part with different time as mentioned in the system definition phase is to be stored,
separated and processed
• A logic to be created for the incoming units are to be either 1 or 2 units
• The processing time for each unit is 7 seconds
• This process requires Level 1 employee for operation
PHASE 2:- LOG IN AREA
There are 2 operations done
• Box Unpacking
• Unit Log in Workstation
Box Unpacking
In this area the parts are separated according to the number of units contained i.e. whether 1 or
2 units. After separation the units are sent to unit log in station for further processing as shown in
Figure (2) below. An attribute can be used to enable the split of the components. The machine type
can be changed to production type with the attribute as the output quantity.

Notable Points:-
• Box unpacking takes 15 seconds
• This operation requires level 1 Employee
Figure (2)
Unit Log in Workstation
In this area an employee analyses the units obtained from the customer, reads the feedback given by
customer and enters the details on the computerized tracking system. The units are sent to the three
different categories.
• Lease Units
• In House Repairs
• 3rd Party Shipping Units
After separation the units sent to lease units and in house repairs are split according to the parts
contained in it. The 3 different parts are:-
• 678- J4 Units
• 3000-J11 Unit
• 4000-J11
The different units are sent to the repair area for further processing. The 3rd party shipping units are
sent to the 3rd party shipping area as shown in Figure (2). A single machine can be used to split the
different type of components according to the percentages by using an if statements or percentage
on the output rule.
PHASE 3:- PRODUCT REPAIR AREA
This area can process only one unit as it has series of parallel and independent cells. The units are
sent to the quick test area to check its operational condition. Based on the results, it is sent either to
quick fix area or to the Repair prep area. The reject units from the quick test area are sent to the

repair prep station which prepares the unit for repairs and repair station corrects the faulty things.
After the repair it is sent to the base function test and calibration block for tests.
The passed units from the quick test area is test to quick fix were the minor faults are attended and
sent to base functional test and calibration area for further tests.
Figure (3)
From the base functional test and calibration a series of operations are followed till the log out area.
From the logout area the lease equipment’s are sent to the lease equipment re-conditioning area. The
rest are sent to the shipping area for further processing. Only 85% of units pass the base functional
test and calibration. The rest 15% go through the same process again starting from the repair prep
station. All the machines used are the single type of machine
The main areas to focus are:-
• MTTF is based on 8 operational hours
• MTTR is based on Log Normal distribution with a mean of 40 mins and a standard deviation of
5 Mins.
• Enough manpower are to be provided for smooth of operations
PHASE 4:- LEASE EQUIPMENT RE-CONDITIONING AREA
The units are re-conditioned in this area for the use of another lease customer. This area is arranged
by a series of cells. Multiple products cannot flow through the same line simultaneously.
Reconfiguration requires downtime of 30 minutes.
The area to focus are:-
• 13% of units fail the testing and is sent back to the repair station for processing again
• MTTF and MTTR occur based on the operational time with their respective distribution of time
• Each and every machine should have enough and able resources for smooth operations

PHASE 5:- SHIPPING AREA
There are 4 performances performed in the area:-
1. Late Unit Processing
2. Lease Shipment Consolidation
3. Label Printing
4. Packaging
Late Unit Processing
This function is used to meet the customer service level. Unit tacking systems generated reports of all
the units that are about to expire. These units which are not going to finish their operations within
their estimated window are substituted with a new product.
Real and integer attributes and variable can be used to calculate the elapsed time and
comparisons can be done time on the obtained time at the final stages of production
Lease Shipment Consolidation
This unit is used to assemble 25 unit shipments of three products for 3 leasing companies. This
accounts to the 9 unique combinations of the product and customers. In this area the consolidated
units are also packed and addressed for shipping to the companies and sent to the Shipping Dock.
• Handling takes 5 Seconds for each unit
• Batch consolidation takes 45 seconds to pack
• Labeling takes 17 seconds for each batch.
The lease equipment’s can be boxed to 25 shipments using assembly machines. The handling
machines controls the input to the packing machines. A stringent clause can be applied on the output
of the handling machine to enable proper split of the incoming units so that the units are evenly
packed in numbers to customers.
Label Printing and Packaging
This area is used to process the individual customer repair units. The boxed units are sent to the
shipping dock.

• Label printing takes 17 seconds each
• Boxing the unit takes 30 seconds
The types of machines that can be used are multi cycle machine for the units that come from
the in house repair areas.
PHASE 6:- THIRD PARTY SHIPPING AREA
After the Log in all the 3rd party repair units are sent
to be prepared for shipment to external facility in this
area. After packing the units are sent to the shipping
dock. Flag mechanisms can be used to control the
shipping of the stored units.
PHASE 7:- THIRD PARTY RECEIVING
AREA
After the repair at the outside third party facilities
the units are logged in, printed shipping labels and
packaged to be sent to the individual customers.
After the process the units are sent to the Shipping
Dock. Flag mechanisms can be used to control the
receiving of the stored units.
PHASE 8:- SHIPPING DOCK
This area hold all the outgoing units until the FedEx pickups arrive and collect the different type of
products. Flag mechanisms can be used to control the shipping of the stored units.

Preliminary Experimental Design
Once the measured values are obtained, Theory of Constraints approach can be used to find the
bottlenecks and changes can be done accordingly to overcome it. So, at this point I just looking at the
flowcharts and with intuitions say on the following phases the area to look out wile modelling are:-
PHASE 1:- RECEIVING AREA
In this area we require a level 1 employee for processing the received package. This operations take 7
seconds per package. The system parameters that we are can alter or change here would be:-
• The shared labor
• We decided the shift patterns according to the available package as run after the modelling
• Our schedule should be in such a way that the work is equally split between the employees
and divided between the allotted shifts
PHASE 2:- LOG IN AREA
The incoming boxes are received either from consumer or leasing partners. The process undergoes
through 3 stations as shown in the figure on the log in area. Production type of machine can be used
to enable the split of the components with the help of attributes and variables
The factors that are considered for analysis are:-
• Box unpacking is done by level 1 employee, Enough employees should be assigned and
proper shifting pattern is to be done to maintain a constant flow of process
• Enough stations should be built to equal the incoming units and to maintain a steady
flow
• Level 2 employee must be well staffed so that login of units are is done properly
• Enough Login machines are to be provided to control the incoming units and keeping
the buffer low.
• Three different units are to be separated. do that processing can be done in batches
• Assigning the units to lease and in house repair units correctly according to the split i.e.
35% and 30% respectively
• Third party repair units to be separated from the lot and sent to the third party
shipping area
• Prioritize the units according to the service levels
• Split the 3 different types of units
PHASE 3:- PRODUCT REPAIR AREA
The incoming products are tested and decided to flow through 2 flow paths according to the test
results.
The units that fails the test are routed through the repair station.

The factors to be considered for analysis in this route are:-
• Enough test stations to accommodate the units ready to be tested
• Only 55% passes the test and moves to the quick fix station
• The rest 45% goes through the repair prep and repair station
• Of all the incoming units to the testers 15% gets rejected and sent back again to the repair
prep station and follows the sequence of operations
• The number of Base Line Functional Test & Calibration machine to be installed are to be
checked as the machine is prone to breakdown
• System technicians to be properly staffed as breakdown of the machine base functional test &
calibration is crucial and make the production come to halt
• Enough machines are to be installed to make sure that the process is continuous till the logout
units as shown in figure
• The lease units to be sent to the lease equipment reconditioning area
• The In house repairs units to be sent to the shipping dock
PHASE 4:- LEASE EQUIPMENT RE-CONDITIONING AREA
The units is used to re condition the unit which has undergone repairs and have been used by the
customer. The units undergoing the process have to undergo a series of process to be sent to another
lease customer.
The factors to be considered for analysis in the area are:-
• Minimum product change schedule to be established as change of products require a
downtime of 30 minutes
• 13% of units are to be rejected from the unit test machine and sent to the repair prep station
to follow the sequence of operations to eliminate the fault in it.
• Number of series of the machines can be increased to observe the production and reduce the
loss of production due to breakdown time
• The automatic test equipment are to be attended by the system technicians so that the line
can be brought to production at the earliest
PHASE 5:- SHIPPING AREA
The lease units are consolidated in this area. All units are labeled and packed to be shipped. This area
also keeps track of the units running late of their service levels. Assembly machines are used to enable
packing of boxes with 25 reconditioned units together. A multi cycle machine is planned to be used
for the units coming out of the repair stations. The flag mechanism are used here. Changing the part
used as a substitute to truck is changed from passive to active with profiles to enable arrival of parts
at a certain time.
The factors to be considered for analysis are:-
• The lease units are to be consolidated and designed to assemble as 25 unit shipment
• The products are to be handled manually so enough resource are to be available

• The units to be sent to individual customers are to be labeled and packed
PHASE 6:- THIRD PARTY SHIPPING AREA
After Log in the units are prepared to be sent to the external repair facilities. After the operations the
units are sent to the shipping dock. The flag mechanism are used here. Changing the part used as a
substitute to truck is changed from passive to active with profiles to enable arrival of parts at a certain
time. The experimentation of the said process are either done on the final experimentation.
The factors to be considered for analysis in the area are:-
• Number of machines to be increased to check any changes in the throughput
• The number of level 2 operators should be adequate to perform the operations involved in the
process
PHASE 7:- THIRD PARTY RECEIVING AREA
The units received from the 3rd party repairing units are sent from the receiving are to this area. The
repaired units are labeled and packed to be sent to the customers. The flag mechanism are used here.
Changing the part used as a substitute to truck is changed from passive to active with profiles to
enable arrival of parts at a certain time as mentioned in the shipping dock and third party shipping
area.
• Enough machines are to be installed as the working time is 90 seconds
• Enough operators are to staffed
• Proper shifting to be done for continuous operations
PHASE 8:- SHIPPING DOCK
All the units to be shipped to the leasing partners or the individual customers are stored here. The
FedEx pickups arrive at fixed times on a day
• Timely dispatch of units
• Enough staffs (level 1 Employees) to be available for the loading process
• Number of loading facilities to be altered to find a change in the dispatch time

INPUT DATA PREPARATION
The input data for the project can be obtained from the project description given to us by Dr. Huff.
With the problem statements and the explanations for every area involved in the project, the following
are the input data required for modeling the facility:-
The cost associated with each level of shared resource are:-
The Cost associated with the each machine, the cycle time involved with the process and the required
level of employees associated with the process according to different area are:-

Step 7&8 - Model Translation, Verification and Validation
In this step we translate the concepts already formulated in step 5 on the basis of the systems
designed. With the help of the inputs collected on step 6 we get to the modelling process. There are
several critical factors to be considered while modelling, which affects the productivity and hampers
the overall profit of the system.
The various factors which we have considered are –
1. Cost: In our study, our aim is to reduce the various costs by reengineering, Man Power planning,
cost per unit etc.
2. Speed: If bottlenecks in the system are removed, that will result in decrease the production time per
unit and increase in overall production. Thus production capacity increases.
We use the theory of constraints to model the process. This enables us to find the bottleneck at
each step of the process and eliminate it.
The methodology that is going to be used is as follows:-
Step A: - System’s constraint identification: -
We use the statistics and charts of the built model to identify the critical constraints
Step B: - Decision making about the problem: - We use tools such as re-design, Manpower planning
etc. to attack the problem.
Step C: - Aligning the system to the new constraint: - we use the line balancing and simulation tools to
check the decisions are correct
Step D: - Elevation of Constraints: - we monitor the change implemented for a long duration of time
and the checked its impacts
Step E: - If any shift in the constraints, start again from Step 1:- Once the constraints influence is
obsolete, new constraints are identified and the methodology is repeated again.
LEASE EQUIPMENT RE-CONDITIONING AREA
Our aim is to have a free flow in the model without any bottlenecks (Step A). We arrange the
machine as according to the flow paths shown in the conceptual model design (Step B). The Built base
model (Step C) is as follows:-
Figure (1)
For the base model we use 1 unit of all machine and only 1 shared resource of each type
required for the model. The units to be sent to the repair Station is scrapped in this model. The parts are

pulled out of the world with the following input rule on the login machine.
Figure (2)
The same pull rule is used on other machines to pull the unit to be processed from the downstream
station. The arrows on Figure (1) shows the direction of the flow of materials.
The tester is made as a multi-cycle machine to enable different activities as follows:-
• Loading
• Unloading
• Testing Machine
Figure (3)
Percent rule is used to split and push the components from the testers to the other machines as shown
in figure (4)
Figure (4)
The output obtained from this model is as follows
Name No. Entered No. Shipped No. Scrapped No. Assembled No. Rejected W.I.P. Avg W.I.P. Avg Time Sigma Rating
A 10923 9484 1435 0 0 4 3.16 30.36 2.62
Part Statistics

Name % Idle % Busy % Filling % Emptying % Blocked % Cycle Wait Labor % Setup % Setup Wait Labor % Broken Down % Repair Wait Labor No. Of Operations
login 0 20.81 0 0 79.17 0.03 0 0 0 0 10923
test 0 59.03 0 0 0 36.19 0 0 4.78 0 10921
cosmetic 63.81 36.19 0 0 0 0 0 0 0 0 9486
reprogram 77.42 18.07 0 0 0 4.51 0 0 0 0 9485
pdt_label 72.89 27.1 0 0 0 0.01 0 0 0 0 9485
pdt_lit 84.19 15.81 0 0 0 0 0 0 0 0 9485
inspection 95.47 4.52 0 0 0 0.02 0 0 0 0 9485
package 90.43 6.77 0 0 0 2.79 0 0 0 0 9484
Machine Stats
Name % Busy % Idle Quantity No. Of Jobs Started No. Of Jobs Ended No. Of Jobs Now No. Of Jobs Pre-empted Avg Job Time
level1 27.58 72.42 1 20408 20407 1 0 1.42
tech_asst 64.66 35.34 1 40815 40814 1 0 1.66
test_tech 4.78 95.22 1 166 166 0 0 30.21
level2 47.42 52.58 1 28455 28455 0 0 1.75
Labor Stats
Name % On-Shift % Off-Shift Completed Shifts
night 29.2 70.8 72
day 29.2 70.8 73
evening 29.14 70.86 73
Shift Stats
The Built model was able to ship 9484 units a year when we run the machine for a single shift, 5 days a
week and 50 weeks a year (Step D). The cost per unit for the model built is 46.84
VERIFICATION
The verification of whether the built model is correct is done by checking the statistics. The
number of operations that should have been done on the busy time is calculated for the Login machine.
Number of Operations = (busy time)/ Cycle Time
= (0.2081*360000)/ 2
= (21850.5)/2
= 10925.25
The number of operations on the obtained statistics is 10923. The calculated number of operations is a
little more because of rounding the busy time.
VALIDATION
The built model is validated with the statistics of Dr. Huff. In comparisons the following are noted.
• The number of parts shipped is 9484 units which is 99.52 % of what is being shipped
• The cost per unit is near the numbers and 0.006% higher in comparisons.
• On the machine statistics all the values seems to be the same with a difference of 0.5 %
difference
• On labor statistics comparisons level 1 , Tech assist and test technician have same results with a
deviation of maximum 1%
Now after validation our constraint that is to be addressed is the daily Production and cost per unit
we will have to follow the steps of methodology on theory of constraints to improve the production and
reduce cost.

IN HOUSE REPAIR AREA
As mentioned in the lease reconditioning Area theory of constraints is used to improve the model to
match the given statistics. On translation of given flow to the model there is a potential dead lock
situation on the flow path quick test -> repair Prep -> repair station -> base functional test and
calibration-> rejected units to repair prep.
By theory of constraints (Step A) we see the potential deadlock situation as the bottleneck.
On analysis (Step B) a choice buffer had to solve the problem, so, a buffer is put for the units that are
rejected from the base functional test and calibration machine. This solved the deadlock situation and
helped the model run smoothly. The built model is shown in figure (5).
Figf
Figure (5)
We use the same input command as the lease reconditioning area to pull the parts out of the world. The
incoming parts are processed through 2 different flow paths as required. The output rule used to split is
showed in the figure (6)
Figure (6)
The input rule and output rule on the base functional test and calibration is shown in the figure (7).
Figure (7)
The rejected15p shown in the model is the buffer placed to entertain the rejected parts from the base
functional test and calibration machine. This enables the model to run smoothly.

The main machine that caused the bottle neck was the repair prep which could not take products from
testers when engaged with a part. This behavior had to stop the base functional calibration and test
machine turning the production to come to halt.
The built model is run for 105000 minutes (Step D) i.e. one shift a day, 5 days a week, and 50 weeks a
year. The output obtained is as shown below:-
Name No. Entered No. Shipped No. ScrappedNo. Assembl No. RejectedW.I.P. Avg W.I.P. Avg Time Sigma Rating
A 5179 5174 0 0 0 5 5.05 102.33 6
part statistics
Name %Idle %Busy %Filling %Emptying %Blocked %CycleWaitLabor %Setup %SetupWaitLabor %BrokenDown %RepairWaitLabor No.OfOperations
quick_test 0 7.23 0 0 36.38 56.39 0 0 0 0 1519
quick_fix 69.35 1.3 0 0 15.56 13.79 0 0 0 0 682
repair_prep 8.09 7.84 0 0 52.78 31.29 0 0 0 0 1097
repair_station 5.15 20.88 0 0 10.73 63.24 0 0 0 0 1096
base_fn_test_calibration 37.43 16.37 0 0 14.66 27.34 0 0 1.28 2.91 1778
unit_adjustment 28.05 4.33 0 0 0 67.62 0 0 0 0 1515
unit_label 90.3 2.89 0 0 0.99 5.83 0 0 0 0 1515
final_check 69.42 2.89 0 0 0 27.69 0 0 0 0 1515
unit_history 95.33 0.72 0 0 0 3.95 0 0 0 0 1515
logout 98.66 0.36 0 0 0 0.98 0 0 0 0 1515
Machinestatistics
test_tech 4.38 95.62 1 36 36 0 3 37.32
tesh_asst 68.84 31.16 1 6643 6643 0 52 3.18
tech 86.33 13.67 1 2670 2670 0 59 9.91
level2 13.59 86.41 1 4556 4556 0 11 0.91
Labour Stats
Name TotalIn TotalOut NowIn Max Min AvgSize AvgTime AvgDelayCount AvgDelayTime MinTime MaxTime
rejected15p 261 261 0 3 0 0.13 53.12 0 1043
Buffer
day 29.2 70.8 73
evening 29.14 70.86 73
night 29.2 70.8 72
Shift Stats
The number of parts shipped is 5174 units and the cost per unit comes to be $ 107.47
VERIFICATION
To verify the model built we calculate the busy time percentage
Busy Time % = ((Number of Operations * Cycle Time)/ Total Time)*100
= ((1519*5)/ 105000)*100
= (7595/105000)*100
= 7.23%
This verifies that my model is correct
VALIDATION

On validating the results with Dr. Huff’s statistics we come to know that
• The number of parts shipped are 18% less
• $14.85 is spent more on each unit
• Idle time for the machines are high in comparison
With the results on the validation we can say our model is not correct and is not efficient. We have to
redesign the model (Step 5).
Now our constraint is there is more idle time on the testing machine (Step 1) which hinders the
production.
On analysis (Step 2) we come to a decision to control the bottleneck area by having a control
over the incoming units. The buffer is relocated and the units moving to the repair prep is now planned
to be re-routed through the buffer in addition to the rejected units from the base functional calibration
and test machine.
A random distribution is created as shown in figure (8) to enable the split of the components (step 3)
i.e. 45% to the quick fix and 55% to the repair prep.
Figure (8)
The output rule for the quick test machine is changed to take the control of the incoming units to the
repair area as shown in figure (9). The model built is shown in the figure (10).
Figure (9)

Figure (10)
After the implemented rule, the quick test machine wait only when the buffer has a rejected unit and it
has an already processed unit to be sent to repair prep in it.
The built model is run for 105000 minutes (Step D). The obtained statistics of the model are shown
below.
Name No. Entered No. Shipped No. Scrapped No. Assembled No. Rejected W.I.P. Avg W.I.P. Avg Time SigmaRating
A 6230 6223 0 0 0 7 6.94 117.01 6
PartStatistics
Name %Idle %Busy %Filling %Emptying %Blocked %CycleWaitLabor %Setup %SetupWaitLabor %BrokenDown %RepairWaitLabor No.OfOperations
package 94.07 5.93 0 0 0 0 0 0 0 0 6223
quick_test 0 29.66 0 0 33.69 36.65 0 0 0 0 6229
quick_fix 7.89 6.86 0 0 82.33 2.93 0 0 0 0 3601
repair_prep 0.01 26.13 0 0 69.18 4.68 0 0 0 0 3658
repair_station 0.01 69.66 0 0 21.07 9.26 0 0 0 0 3657
base_fn_test_calibration 0.22 66.82 0 0 3.19 23.64 0 0 6.12 0 7257
unit_adjustment 61.96 17.78 0 0 0 20.25 0 0 0 0 6224
unit_label 80.49 11.85 0 0 7.66 0 0 0 0 0 6223
final_check 72.27 11.85 0 0 0 15.87 0 0 0 0 6223
unit_history 96.98 2.96 0 0 0 0.05 0 0 0 0 6223
logout 98.31 1.48 0 0 0 0.2 0 0 0 0 6223
MachineStats
test_tech 6.12 93.88 1 158 158 0 0 40.64
level1 5.93 94.07 1 6223 6223 0 0 1
tesh_asst 81.42 18.58 1 34227 34226 1 0 2.5
tech 87.44 12.56 1 9882 9881 1 0 9.29
level2 16.3 83.7 1 18670 18669 1 0 0.92
Labour Stats
Name Total In Total Out NowIn Max Min AvgSize AvgTime AvgDelayCount AvgDelayTime MinTime MaxTime
rejected15p 3660 3659 1 5 0 1.06 30.54 0 121.83
buffer

day 29.2 70.8 73
evening 29.14 70.86 73
night 29.2 70.8 72
Shift Stats
The number of units that are shipped are 6223 and the cost per unit is $ 92.62
VERIFICATION
To verify the model built we calculate the busy time percentage
Busy Time % = ((Number of Operations * Cycle Time)/ Total Time)*100
= ((6223*5)/ 105000)*100
= (31115/105000)*100
= 29.6%
This verifies that my model is correct
VALIDATION
In Comparison with Dr. Huff’s statistics we see that
• The number of units shipped is 6223 i.e. 98.54% of the given statistics
• The cost per unit $ 92.62 i.e. $ 1.09 extra per unit.
• My custom random distribution is giving a split % more close to the required than Dr. Huff i.e.
my split is 57.8% to repair prep compared to 59.1% on Dr. Huff’s statistics
• The machine stats and the labor stats are also almost same with 2% variations.
With this we can validate that the built base model is correct. And further improve the existing model
with the help of theory of constraints to maximize the production and reduce the cost per unit.
Step 9:- Final Experimental Design
To simplify the Experimentation stage we optimize the critical areas. The optimized critical areas can
be turned into a designer element and inserted on the final model according to the requirement. The
most critical areas in our model are
• Lease Reconditioning Cell
• In House Repair Area
The Steps involved in optimizing both the cells are shown below. As mentioned above on the
Preliminary Experimental Design about the theory of constraints, we use the same methodology to
improve the performance of the in house repair area and reduce the cost per unit. The output of the base
model gave us 6223 units a year with a cost $92.62 per unit. On improvising the methodology
The cells showing red color are good for us and shows a decreasing trend in the cost.
The cells showing green color are bad for us and shows an increasing trend in the cost.

LEASE RECONDITIONING CELL
The iterations on a tabular format is shown on the appendix section
Iteration 1
This step of the optimization is the base cell as shown on the Step 7&8
Iteration 2
Step A: - The number of units produced were less leading to higher cost
Step B: - The production was restricted to only a shift on one day and planning was done to extend to 3 shifts a
day
Step C: - We created a three shift pattern and added labor accordingly to enable higher production
Step D: - the built model was run for 7hrs a day, 5 working days, 50 weeks a year i.e. 360000 minutes
Step E: - The production of the facility increased from 9484 to 28653 units a year. The cost per unit decreased
by 18.1%.
Test
Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %
1 Base 9484 $46.84 37.936 359785 0
2 base with 3 shifts 28653 $38.36 114.612 3283978 8.48 18.10%
Even though the production increased there were more cycle wait labor which was hindering
production. This change of constraint is now taken and analyzed as the next iteration.
Iteration 3
Step A: - The production is blocked 79% on the login machine due to 43% of the cycle wait labor on the unit test
machine
Step B: - The number of tech assist labor was less and they had to operate 3 machine single handedly
Step C: - We increased the number of tech assist from 1 to 2 per shift
Step D: - the built model was run for a year i.e. 360000 minutes
Step E: - The production of the facility increased from 28653to 40628 units a year. The cost per unit decreased
by $3.63
Test
2 base with 3 shifts 28653 $38.36 114.612 3283978 8.48 18.10%
3 add 3 tech asst 40628 $34.73 162.512 6602538 3.63 9.46%
Even though the production increased there were more there are still blocks observed on the unit test
machine. This change of constraint is now taken and analyzed as the next iteration.
Iteration 4
Step A: - The production is still blocked 69%. And no idle time is observed on the login machine
Step B: - The unit test machines are also working without idle time and the remaining was cycle wait time and
breakdown times.
Step C: - We increased the number of unit test to from 1 to 2
Step E: - The production of the facility increased from 40628 to 57797 units a year. The cost per unit decreased
by $6.02

Test
3 add 3 tech asst 40628 $34.73 162.512 6602538 3.63 9.46%
4 add 1 test Unit 57797 $28.71 231.188 13361973 6.02 17.33%
With the increase in production by 17.33% we also observed on the statistics that production is blocked
on all the machines before product label. This is taken as the new constraint and analyzed at the next iteration.
Iteration 5
Step A: - The block of production was observed. The machines below product label was waiting for labor
Step B: - More resources are required on the product label and inspection station.
Step C: - We increased the number of level 2 employees from 1 a shift to 2
Step E: - The production of the facility increased from 57797 to 61654 units a year. The cost per unit increased
by 4.77%
Test
4 add 1 test Unit 57797 $28.71 231.188 13361973 6.02 17.33%
5
add level 2 operators 61654 $30.08 246.616 15204863 -1.37 -4.77%
With the increase in production of 3857 units we also notices that the cost of the production per unit
went up by $1.37 a unit. Even though the cost of the unit is increasing, an intuition that on the later stages we
can reduce the cost is felt. Blockages were observed and taken as the constant again.
Iteration 6
Step A: - The block of production was observed. The machines before product label was blocked.
Step B: - A thought of increasing the reprogramming station can reduce the blockage was felt
Step C: - We increased the number of reprogramming station from 1 to 2
Step E: - The production of the facility increased by 57 units a year and in turn the cost per unit increased by
12cents.
Test
4 add 1 test Unit 57797 $28.71 231.188 13361973 6.02 17.33%
5
add level 2 operators 61654 $30.08 246.616 15204863 -1.37 -4.77%
6
add reprogramming station 61711 $30.20 246.844 15232990 -0.12 -0.40%
This step does not yield any significant results. Reprogramming station is removed and the constraints
to increase the production is again checked on the iteration 5.
Iteration 7
Step A: - The block of production was observed. The machines before product label was blocked.
Step B: - A thought of increasing the cosmetic station can reduce the blockage was felt
Step C: - We increased the number of cosmetic station from 1 to 2
Step E: - The production of the facility increased by 9960 units a year and in turn the cost per unit reduced by
$2.68
Test
6
7
remove reprogramming add
cosmetic unit 71614 $27.52 286.456 20514260 2.68 8.87%

This step yielded a significant change results to the production and cost. Cycle wait labor percentages
are comparatively high. We take this as the new constraints for the next iteration.
Iteration 8
Step A: - Cycle wait labor reducing the production was observed.
Step B: - increasing the tech asst should increase the production was observed
Step C: - We increased the number of tech assist from 6 to a total of 9 people i.e. 3 per shift
Step E: - The production of the facility increased by 10535 units a year and in turn the cost per unit also
increased 8 cents
Test
7
remove reprogramming add
cosmetic unit 71614 $27.52 286.456 20514260 2.68 8.87%
8 add 3 tech asst 82149 $27.60 328.596 26993833 -0.08 -0.29%
Even though the cost of the unit is increasing, an intuition that on the later stages we can reduce the
cost is felt. Blockages were observed and taken as the constant again.
Iteration 8
Step A: - Cycle wait labor was observed in the unit packaging station.
Step B: - increasing the level 1 operators should increase the production was felt
Step C: - We increased the number of level 1 operators from 3 to a total of 6 people i.e. 2 per shift
Step E: - The production of the facility increased by 212 units a year and in turn the cost per unit also increased
$1.48
Test
8 add 3 tech asst 82149 $27.60 328.596 26993833 -0.08 -0.29%
9
add 3 level 1 operator 82361 $29.08 329.444 27133337 -1.48 -5.36%
This step does not yield any significant results. Level 1 operators is removed and the constraints to
increase the production is again checked on the iteration 7.
Iteration 9
Step A: - Cycle wait labor was observed in the unit packaging station.
Step B: - increasing the level 1 operators should increase the production was felt
Step C: - We increased the number of level 1 operators from 3 to a total of 6 people i.e. 2 per shift
Step E: - The production of the facility increased by 212 units a year and in turn the cost per unit also increased
$1.48
Test
8 add 3 tech asst 82149 $27.60 328.596 26993833 -0.08 -0.29%
9
This step does not yield any significant results. Level 1 operators is removed and the constraints to
increase the production is again checked on the iteration 7.
Iteration 10
Step A: - product blockage is observed before in the sequence before product label
Step B: - increasing the product labeling station should increase the production was felt
Step C: - We increased the number of stations from 1 to 2 per shift

Step E: - The production of the facility increased by 57 units a year and in turn the cost per unit also decreased
by $1.42 compared to the iteration 9
Test
8 add 3 tech asst 82149 $27.60 328.596 26993833 -0.08 -0.29%
9
10
remove 3 level 1 operator add
pdt label 82428 $27.62 329.712 27177501 1.46 5.02%
This step yielded us significant results. But on comparison with iteration 8 we are still 2 cents high on
costs. Blocks were observed on stations before reprogramming station and take this as the new constraint.
Iteration 11
Step A: - product blockage is observed before in the sequence before reprogramming station
Step B: - increasing the reprogramming station should increase the production was felt
Step C: - We increased the number of stations from 1 to 2 per shift
Step E: - The production of the facility decreased by 246 units in turn increasing the cost by 16 cents per unit
Test
8 add 3 tech asst 82149 $27.60 328.596 26993833 -0.08 -0.29%
9
10
remove 3 level 1 operator add
pdt label 82428 $27.62 329.712 27177501 1.46 5.02%
11
This step did not yield us significant result but I realize that this is going to help in increasing the
production as the blockages and the idle time of the testers are reduced to less than 3.2%. Blockage is observed
at the unit login station and taken as the next constraint.
Iteration 12
Step A: - product blockage is on the unit login station
Step B: - Increasing the tester was analyzed to facilitate more production
Step C: - We increased the number of stations from 2 to 3
Step E: - The production of the facility increased by 21.25% and in turn decreasing the cost by $2.35 per unit
Test
11
12 add 1 unit test 99649 $25.43 398.596 39719693 2.35 8.46%
This step yielded us significant result. Blockage is observed at all station expect the packaging station
and this is taken as the next constraint.
Iteration 13
Step A: - blockage identified on stations in the sequence before packaging station.
Step B: - Increasing the packaging station was analyzed to facilitate more production
Step C: - We increased the number of stations from 1 to 2
Step E: - The production of the facility reduced by 85 units a year and in turn the addition of machine increased
the cost by 5 cents per unit

Test
12 add 1 unit test 99649 $25.43 398.596 39719693 2.35 8.46%
13 add unit packaging 99565 $25.48 398.26 39652757 -0.05 -0.20%
This step did not yield us significant result. Blockage is observed at all station expect the packaging
station and this is taken as the next constraint.
Iteration 14
Step A: - blockage identified on stations in the sequence before packaging station. Cycle wait labor is also
observed on the packaging station
Step B: - removing the packaging station and increasing the level 1 operator was analyzed to facilitate more
production
Step C: - We increased the number of level 1 operator from 1 to 2 per shift totaling to 6 persons.
Step E: - The production of the facility increased by 7745 units a year and in turn the addition of resource
increased the cost by 10 cents per unit
Test
13 add unit packaging 99565 $25.48 398.26 39652757 -0.05 -0.20%
14
remove unit packiging and add
level 1 operator 107310 $25.58 429.24 46061744 -0.10 -0.39%
Continuing with the results considering that we can reduce the cost further we try to get the most out
of the system. Now on analysis cycle wait labor is observed.
Iteration 15
Step A: - Cycle wait labor is also observed on the labelling and literature station.
Step B: - Increasing the level 2 operator seemed to facilitate more production
Step C: - We increased the number of level 2 operator from 2 to 3 per shift totaling to 9 persons.
Step E: - The production of the facility decreased by 21 units a year and in turn the addition of resource
increased the cost by $1.43 per unit
Test
14
level 1 operator 107310 $25.58 429.24 46061744 -0.10 -0.39%
15 add 3 level 2 operators 107289 $27.01 429.156 46043718 -1.43 -5.59%
This addition does not yield us anything and increase the operating cost. Continuing with the results
considering that we can reduce the cost further we try to get the most out of the system. Now on analysis cycle
wait labor is observed.
Iteration 16
Step A: - We remove the added level 2 operators and see that the repair wait time is high
Step B: - Increasing the test tech operator seemed to facilitate more production
Step C: - We increased the number of test tech operator from 1 to 2 per shift totaling to 6 persons.
Step E: - The production of the facility decreased by 20 units a day and in turn the addition of resource
decreased the cost by $0.52 per unit on comparisons with iteration 15
Test
14
level 1 operator 107310 $25.58 429.24 46061744 -0.10 -0.39%
16
remove 3 level 2 operator add 3
test tech 112634 $26.49 450.536 50745672 0.52 1.93%
This addition reduces the cost in comparison to the iteration 15 but it’s vice versa when compared with

iteration 14. Continuing with the results considering that we can reduce the cost further we try to get the most
out of the system. Now on analysis idle time is observed and taken as the next constraint.
Iteration 17 & 18
Step A: - Idle times are observed at the testers which is the critical component of the model
Step B: - Increasing the unit login should help in more production to cover up the idle time. Then add cosmetic
station
Step C: - unit test machine and cosmetic station is increased to 2
Step E: - The production of the facility decreased by 20 units a day and in turn the addition of resource
decreased the cost by $0.52 per unit on comparisons with iteration 15
Test
16
test tech 112634 $26.49 450.536 50745672 0.52 1.93%
17
add unit log in station 115143 $27.53 460.572 53031642 -1.04 -3.93%
Adding cosmetic station after unit login we increase the production per day to 470 units from 450 units
with increasing the cost by $0.74 compared to iteration 16
Test
16
test tech 112634 $26.49 450.536 50745672 0.52 1.93%
17
add unit log in station 115143 $27.53 460.572 53031642 -1.04 -3.93%
18 add cosmetic station 117690 $27.23 470.76 55403744 0.30 1.09%
of the system. Now on analysis units were blocked on the sequence behind product label machine is observed
and taken as the next constraint.
Iteration 19
Step A: - Units blocked were because cycle wait labor percentage on both label machine was high (45%)
Step B: - Increasing the Level 2 operator seemed to facilitate more production
Step C: - We increased the number of Level 2 operator from 2 to 4 per shift totaling to 12 persons.
Step E: - The production of the facility decreased by36 units a day and in turn the addition of resource increased
the cost by $0.26 per unit on comparisons with iteration 18
Test
18 add cosmetic station 117690 $27.23 470.76 55403744 0.30 1.09%
of the system. Now on analysis cycle wait labor is observed and taken as the next constraint.
Iteration 20
Step A: - Cycle wait labor for machines testers, cosmetic repair and label station is observed
Step B: - Increasing the tech assist operator can facilitate more production
Step C: - We increased the number of tech assist operator from 4 to 5 per shift totaling to 15 persons.
Step E: - The production of the facility decreased by 18 units a day and in turn the addition of resource again
increased the cost by $0.82 per unit on comparisons with iteration 19

Test
20 add 3 tech asst 131159 $28.31 524.636 68810733 -0.82 -2.99%
of the system. Now on analysis cycle wait labor is observed and taken as the next constraint.
Iteration 21
Step A: - Cycle wait labor is observed over all the machines this is because the shared resources work only 7 hrs.
A day and they leave the unit as such after their working time.
Step B: - Changing the shift patterns could work out and reduce the cycle wait labor
Step C: - Change the shift patterns of all the shift. An example of 2 shifting patterns for the night is shown below
The first shift pattern Second shift pattern
Step E: - The production of the facility increased drastically by 37 units a day and in turn increase in the
resources increased the cost by $0.14 per unit on comparisons with iteration 20
Test
20 add 3 tech asst 131159 $28.31 524.636 68810733 -0.82 -2.99%
21 change the shift patterns 140357 $28.45 561.428 78800350 -0.14 -0.48%
of the system. Now on analysis the tester was fully loaded and the idle time was less than 0.5% on all three
machines. Since the cost is high our next action is to reduce it.
Iteration 22
Step A: - Now that the production is at a considerable level and the cost is soaring high compared to the lowest
cloaked i.e. $25.43 we find the ways to reduce it
Step B: - Reducing the labor can get the cost low but the thing to check is that the production doesn’t fall down

the reached level
Step C: - We reduce the number of level 2 operator from 15 to 12 persons i.e. 4 per shift
Step E: - The change in the labor didn’t affect the production much and reduced the cost by 3.69%
Test
21 change the shift patterns 140357 $28.45 561.428 78800350 -0.14 -0.48%
22
reducing the 3 level 2 operators 140064 $27.40 560.256 78471696 1.05 3.69%
Blockage was observed and considered as the next constraint
Iteration 23 & 24
Step A: - Units blocked are to be addressed and two trails are taken
• Increase level 1 operators
• Add buffer to remove block
Step B: - both of the methods can reduce the block and facilitate production
Step C: - We increase the number of level 1 operator or try adding bufffer
Step D: - the model built was run for a year i.e. 360000 minutes
Step E: - The change in the labor didn’t affect the production much and increased the cost by 3.21%
Test
22
23 increase 3 level 1 operators 140236 $28.28 560.944 78664543 -0.88 -3.21%
So we remove the level 1 operator added and add buffer. The output of the model run for a year
Test
22
24
remove 3 level 1 operators and
add 2 buffer after reprogram
station 140444 $27.22 561.776 78898069 1.06 3.75%
Adding a buffer got the cost down by 3.75% compared to iteration 23. The obtained cost per
unit is less than iteration 22. On analysis after the experiment more idle time is observed
Iteration 25
Step A: - Cosmetic machine is observed to have more idle time.
Step B: - of the three machine the 3rd
machine is only used 35% and the rest 2 is used only up to 75%
Step C: - We reduce the number of cosmetic station to 2
Step E: - The removal of the machine affected the production and increased the cost by 4.92%
Test
24
remove 3 level 1 operators and
add 2 buffer after reprogram
station 140444 $27.22 561.776 78898069 1.06 3.75%
25 remove cosmectiv station 130679 $28.56 522.716 68308004 -1.34 -4.92%
Observations that units are blocked are noted and taken as the next constraint.
Iteration 26, 27 &28
Step A: - The blocked units are observed at unit login and testers
Step B: - buffers can be used to increase the busy time and reduce the blocked %
Step C: - we do the following steps
• Add buffer near unit test
• And remove buffer B1 inserted after reprograming station

Step E: - The obtained results are as follows for each iteration
Adding 1000 units of buffer B2 after unit login
Test
25 remove cosmectiv station 130679 $28.56 522.716 68308004 -1.34 -4.92%
26 add buffer after unit test 141755 $34.22 567.02 80377920 -5.66 -19.82%
Since the buffer space is high we incur a lot of money to it. We take it as the constraint follow the steps to
reduce the cost by reducing the buffer size. Once we reduced the size to 5 units the stats we obtain are as
follows
Test
26 add buffer after unit test 141755 $34.22 567.02 80377920 -5.66 -19.82%
27
reduce the buffer B2 to 5 units 141228 $27.26 564.912 79781392 6.96 20.34%
The cost per unit reduced to $27.26 and further to reduce the cost the Buffer B1 is removed. The stats after
removal is as follows
Test
27 reduce the buffer B2 to 5 units 141228 $27.26 564.912 79781392 6.96 20.34%
28 remove buffer b1 141309 $27.24 565.236 79872934 0.02 0.07%
Further removal of the buffer reduced the cost by 2 cents per unit. We see that the cycle wait labor is
still prominent on all the machines. This is taken as the new constraint for analysis.
Iteration 29
Step A: - Cycle wait labor% is prominent on all the machine due to the break time of the shift patterns
Step B: - Change in the working pattern can increase the production
Step C: - We change the working pattern on all the shift. An example for the Night shift is shown below
Shift pattern (1) Shift Pattern(2)

Step E: - The change in the shift pattern reduced the cost by 2 cents
Test
28 remove buffer b1 141309 $27.24 565.236 79872934 0.02 0.07%
29 change the shift patterns 141442 $27.22 565.768 80023357 0.02 0.07%
The number of units shipped remains the same and the cost per unit reduces by 2 cents per unit. Which is a
good sign. On further analysis still the cycle wait labor persisted. We take this as the next constraint and follow
the same steps.
Iteration 30
Step A: - Cycle wait labor is still existing due to the breaks on the shifting patterns
Step B: - preemption and priority can be used to steel labor so that the testers are utilized to the fullest
Step C: - we insert 1st
priority to the testers and preemption of labors to have the machine loaded and reduce
the cycle wait time
Step E: - This rule increased the production by 22 units per day.
Test
29 change the shift patterns 141442 $27.22 565.768 80023357 0.02 0.07%
30
add priority and prempt to
tester 146992 $26.61 587.968 86426592 0.61 2.24%
The cost per unit also reduced by 2.24%. On further analysis all testers were blocked 3% of the times. We take it
as the constraint for analysis.
Iteration 31 & 32
Step A: - Blocks on the testers were analyzed
Step B: - Two ways to reduce the block are to add more machines or buffers
Step C: - we test the following approaches for results
• Add buffer space at B2
• Add a cosmetic repair machine
Step E: - on adding 5 buffer space to B2
Test
30
tester 146992 $26.61 587.968 86426592 0.61 2.24%
31 add 5 buffers b2 149057 $26.43 596.228 88871957 0.18 0.68%
On reducing the buffer space of B2 to 5 units and adding a cosmetic repair machine the obtained stats are as
follows
Test
30
tester 146992 $26.61 587.968 86426592 0.61 2.24%
32
reduce the buffer B2 to 5 units
and add cosmetic station
150611
$26.28 602.444 90734693 0.15 0.57%
Adding a cosmetic machine produced higher results and the cost is reduced by 33 cents. On further
analysis less than 1% of time is idle and 1.4% of time the testers are blocked. Reducing this level is
taken for further analysis

Iteration 33 & 34
Step A: - Idle time and blocked percentage are targeted on the testers to utilize it to the fullest
Step B: - adding a machine and adding buffers should solve the targets
Step C: - we add the on the following steps to see the changes
• Add login station to cater product
• Increase the buffer size to 10
Step E: - This process increased the production and reduced the cost as follows:-
Adding a unit login gave the following results
Test
32
reduce the buffer B2 to 5 units
and add cosmetic station
150611
$26.28 602.444 90734693 0.15 0.57%
33 add one login station 152639 $26.12 610.556 93194657 0.16 0.61%
The cost per unit also reduced by 61 cents. On further steps we increase the buffer size to 10 units. This change
yielded us the following result.
Test
33 add one login station 152639 $26.12 610.556 93194657 0.16 0.61%
35 increase the buffer size to 10 153148 $26.10 612.592 93817240 0.02 0.08%
The cost per unit is reduced further by 2 cents and the production is increased by 2 units per day. On
further analysis we saw that the cost is high compared to the previous low of $25.43. So we see that we
have a lot of labor resource
Iteration 35
Step A: - As cost incurred on the labor is high a study is done to analyze what can be done to rece the cost
without hampering the production
Step B: - Test technicians has paid more and stay idle for a long time
Step C: - check is done by removing 3 test technicians
Step E: - This production decreased by 15 units per day and the cost of the unit reduced drastically to the ever
lowest of $25.08
Test
35 increase the buffer size to 10 153148 $26.10 612.592 93817240 0.02 0.08%
36 remove test tech01 149474 $25.08 597.896 89369907 1.02 3.91%
The cost per unit also reduced by 3.91%. On further analysis all testers were idle 0.4% of the times. We take it
as the constraint for analysis and try to improve.
Iteration 36
Step A: - Idle time is observed on testers
Step B: - To reduce the idle time we add a buffer can be added before testers to supply units uninterruptedly
Step C: - add 2 buffers after Unit login and before tester
Step E: - This production increased by 2 units per day and the cost of the unit reduced drastically again to the
ever lowest of $25.03
Test
36 remove test tech01 149474 $25.08 597.896 89369907 1.02 3.91%
37 add 2 buffer units b1 150148 $25.03 600.592 90177688 0.05 0.20%
On further analysis repair wait labor is the only option left for improvement.

Iteration 37
Step A: - repair labor wait is observed that disables the tester to be utilized to the fullest
Step B: - To reduce the repair wait time we adding test tech can solve the problem
Step C: - add 1 test tech each shift
Step E: - This production increased by 14 units per day and the cost of the unit increased drastically again to
$26.06
Test Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %
37 add 2 buffer units b1 150148 $25.03 600.592 90177688 0.05 0.20%
38 ADD TEST TECH 153669 $26.06 614.676 94456646 -1.03 -4.12%
Since spending extra $1.03 per unit for an increase in 14 units per day is not a good option we
continue to take the model built on the iteration 36 and experiment it to different pseudo random
numbers. This model is replicated 50 times with a skip value of 13. Alternative function is not used.
The output from the validation is:-
The variance chart of the experiment is as shown below.

When looking at the variance data we see that the standard deviation is considerable. The stats are
shown on the figure below.
The confidence chart of the experiment is shown below.

The final machine statistics of the built model is as shown below
This is the best that could be suggested for the repair cells as any further change by me had a
higher cost per unit. The final statistics of the model is shown on the Documentation Stage of Step 12
IN HOUSE REPAIR CELL
The iterations on a tabular format is shown on the appendix section
Iteration 1
This step of the optimization is the base cell as shown on the Step 7&8

Iteration 2
Step A: - On the statistics it was observed that the number of units produced were less
Step B: - The production was restricted to only a shift on one day and planning was done to extend to 3
shifts a day
Step C: - We created a three shift pattern and added labor accordingly to enable higher production
Step D: - the built model was run for a year with 5 working days, 50 weeks a year i.e. 360000 minutes
Step E: - The production of the facility increased from 6223 to 18646 units a year. The cost per unit
decreased by 19.6%.
Test Configuration Units/output $/Unit Unit/day
total
expenditur
e
Increse
or
decrese
increase
or
decrease
%
1 base model 6223 $92.62 24.892 154903 0
2 add 3 shifts 18646 $74.43 74.584 1390693 18.19 19.64%
Even though the production increased there were more cycle wait labor which was hindering
production. This change of constraint is now taken and analyzed as the next iteration.
Iteration 3
Step A: - More % cycle wait labor observed on all machines as the only one employee had to work on
all machine requiring his assistance.
Step B: - Add Test Tech operators to reduce the cycle wait labor % and improve production
Step C: - we increased the number from 1 person a shift to 2.
Step E: - The production of the facility increased by 13.6% a year. The cost per unit increased by 2.2%
total
expenditur
e
Increse
or
decrese
increase
or
decrease
%
2 add 3 shifts 18646 $74.43 74.584 1390693 18.19 19.64%
3 ADD TECH ASSIST 21199 $76.06 84.796 1797590 -1.64 -2.20%
Since there is an increase in the cost the model was analyzed thoroughly. Even with the increase in cost
lot of cycle wait labor% was observed with the repair station requiring technicians.
Iteration 4
Step A: - More % cycle wait labor observed on the repair station is taken as the next constraint
Step B: - Add Technician to reduce the cycle wait labor % and improve production
Step C: - we increased the number from 1 person a shift to 2.
Step D: - the built model was run for a year i.e. 360000 minutes for validation
total
expenditur
e
Increse
or
decrese
increase
or
decrease
%
2 add 3 shifts 18646 $74.43 74.584 1390693 18.19 19.64%
3 ADD TECH ASSIST 21199 $76.06 84.796 1797590 -1.64 -2.20%
4 ADD TECH 25329 $75.40 101.316 2566233 0.67 0.88%
We observe that the cost has reduced to $75.4 with an increase in 19.4% production. But the cost is still
higher than the one obtained on the iteration 2. So on analysis cycle wait labor % still existed on all
machines.
Iteration 5
Step A: - More % cycle wait labor observed on all machines
Step B: - Add Test Tech operators to reduce the cycle wait labor % and improve production
Step C: - we increased the number from 2 person a shift to 3 making a total of 9 people.

total
expenditur
e
Increse
or
decrese
increase
or
decrease
%
4 ADD TECH 25329 $75.40 101.316 2566233 0.67 0.88%
5 ADD TECH ASSIST 25689 $81.59 102.756 2639699 -6.20 -8.22%
With adding the labor the production increased only by 1.4% and the cost again shoot up $6.2 per unit.
The units are blocked in the stations before testers. This will
Iteration 6
Step A: - On analysis, the labor is not of use as the testers are running at the highest capacity with an
idle time 0.3% stopping the incoming units.
Step B: - Add 1 Tester
Step C: - we increased the number from 1 tester to 2 testers .
Step E: - The production of the facility increased by 83 units/day. The cost per unit decreased by
$26.9%
total
expenditur
e
Increse
or
decrese
increase
or
decrease
%
5 ADD TECH ASSIST 25689 $81.59 102.756 2639699 -6.20 -8.22%
6 add tester 46374 $54.64 185.496 8602192 26.95 33.03%
With adding the tester the production increased by 80.52% and the cost reduced by 33.03% per unit.
Even after the addition of testers blocked was observed on the machines before testers.
Iteration 7
Step A: - On analysis, both the testers were running at 75% busy and only 5% idle. Lot of blockage was
observed on the machines before testers i.e. repair station and the quick fix.
Step B: - Add 1 Tester for smooth functioning and process more parts
Step C: - we increased the number from 2 tester to 3 testers.
Step E: - The production of the facility increased by 16.26% a year. The cost per unit decreased by
6.93%
Total
Expenditure
($)
Increase in
Prooducti
on (%)
Increse or
decrease
in cost ($)
increase
or
decrease
(COST)%
6 add tester 46374 $54.64 185.496 8602192 69% 26.95 33.03%
7 add tester 53913 $50.86 215.652 11626446 26% 3.79 6.93%
Once the output was analyzed for the bottleneck, it was seen that bottleneck switched from testers to
repair station
Iteration 8
Step A: - The repair station was running with tiny idle time.
Step B: - Add 1 Repair station for smooth functioning and process more parts
Step C: - we increased the number from 1 repair station to 2.
Step E: - The production of the facility reduced by 16% a year. The cost per unit increased by 12%
total
expenditur
e
Increse
or
decrese
increase
or
decrease
%
7 add tester 53913 $50.86 215.652 11626446 3.79 6.93%
8 Add Repair Station 45727 $57.05 182.908 8363834 -6.20 -12.18%
Once the output was analyzed for the verification, it was seen that the repair station was running again

at full capacity but both of them were blocked. The bottle neck now is switched to blockages
Iteration 9
Step A: - on analysis, blockages were from the testers and the machines behind it on the process flow
Step B: - Add 1 unit adjustment station for smooth functioning and process more parts
Step C: - we increased the number from 1 unit adjustment to 2.
Step E: - The production of the facility increased by 13.7% a year. The cost per unit increased by
12.18%
total
expenditur
e
Increse
or
decrese
increase
or
decrease
%
8 Add Repair Station 45727 $57.05 182.908 8363834 -6.20 -12.18%
9 Add Unit Adjustment 52009 $52.41 208.036 10819744 4.64 8.14%
On verification, Lot of cycle wait labor % were observed on the machines
Iteration 10
Step A: - on analysis, blockages were from the testers and the machines behind it on the process flow
Step B: - Add 1 technician on each shift for smooth functioning and process more parts
Step C: - Increased the number from 6 to a total of 9 people.
Step E: - The production of the facility increased by 17units a day. The cost per unit increased by
2.48%
total
expenditur
e
Increse
or
decrese
increase
or
decrease
%
9 Add Unit Adjustment 52009 $52.41 208.036 10819744 4.64 8.14%
10 add 3 technicians 56354 $53.71 225.416 12703093 -1.30 -2.48%
On verification, the bottleneck switched to idle time
Iteration 11
Step A: - On analysis repair station, repair prep and quick fix has idle times
Step B: - Increasing the input quantity to the above said stations can solve the bottleneck
Step C: - Add 1 quick test
Step E: - The production of the facility increased by 38 units a day. The cost per unit decreased by
9.54%
total
expenditur
e
Increse
or
decrese
increase
or
decrease
%
10 add 3 technicians 56354 $53.71 225.416 12703093 -1.30 -2.48%
11 add quick_test 65916 $48.59 263.664 17379676 5.12 9.54%
On checking the statistics, the cycle wait labor was present on all the machines.
Iteration 12
Step A: - On analysis cycle wait labor turned to be the next constraint
Step B: - This wait time is caused because the workforce leaves the plant at 7hour of their work leaving
the work as such. This also is shown on the column as the units pre-empted.
Step C: - To solve the above constraint a new shift pattern is designed.
Some of them i.e. the 1st shift follows the shift pattern on all morning. Evening and night

The second type of shift follows the following pattern all the time
Tech assist operators and technicians are assigned both the first and second type of the shift

all the others i.e. level 1 operators, Level 2 Operators & test technicians are assigned to the second
type of the shift, an example for level 2 is shown in the figure below
Step D: - the built model was run for a year i.e. 360000 minutes. The following results were observed
Test Configuration Units/output$/Unit Unit/day
total
expendit
ure
Increse
or
decrese
increase
or
decrease
%
11 add quick_test 65916 $48.59 263.664 17379676 5.12 9.54%
12
Change the shift
pattern for all 66386 $48.37 265.544 17628404 0.22 0.45%
Step E: - The production of the facility increased by 2 units a day. The cost per unit decreased by 22
cents. On checking the statistics, the cycle wait labor was present on all the machines.
Iteration 13
Step A: - On analysis testers, repair station, repair prep and quick fix has cycle wait labor
Step B: - Increasing the tech assist operators can solve the bottleneck
Step C: - Add 1 tech assist operator
total
expendit
ure
Increse
or
decrese
increase
or
decrease
%
12
Change the shift
pattern for all 66386 $48.37 265.544 17628404 0.22 0.45%
13 ADD TECH ASSIST 75346 $47.09 301.384 22708079 1.28 2.65%
Step E: - The production of the facility increased by 13.5% a year. The cost per unit also decreased by
2.65%. Idle time were observed on the testers.
Iteration 14
Step A: - On analysis testers, repair station, repair prep and quick fix had idle time still left on with
them
Step B: - Increasing buffers can solve the bottleneck
Step C: - Add buffers b1, b2 before and after testers
Step D: - the built model was run for a year i.e. 360000 minutes for the statistics.
total
expendit
ure
Increse
or
decrese
increase
or
decrease
%
13 ADD TECH ASSIST 75346 $47.09 301.384 22708079 1.28 2.65%
14 add buffers b1, b2 77444 $46.49 309.776 23990293 0.59 1.26%

Step E: - Production of the facility increased by 8 units a day. The cost per unit decreased by 59 cents.
Still idle time was observed on the testers.
Iteration 15
Step A: - On analysis testers, repair station, repair prep and quick fix had idle time still left on with
them
Step B: - The input rule for the testers had a stringent rule on buffer(rejected15p) to prevent the dead
lock form happening.
Step C: - Now since the buffers have been added and the testers are increased in numbers, a
relaxation is done on the input rule and pulled from the buffer. This change is shown below. The !
mark shows the previous rule
total
expendit
ure
Increse
or
decrese
increase
or
decrease
%
14 add buffers b1, b2 77444 $46.49 309.776 23990293 0.59 1.26%
15
change pull from of
testers from b2 79260 $45.85 317.04 25128590 0.64 1.37%
Step E: - Production of the facility increased by 8 units a day. The cost per unit decreased by 64 cents
bringing the lowest cost till now. Cycle wait labor % was observed
Iteration 16
Step A: - . Cycle wait labor % was still prominent in the system and accounted for little more than
7.5% on each testers.
Step B: - adding pre-emption and priority was the only way as any increase in the operators will
increase the cost per unit
Step C: - priority input on the testers

The preemption was applied as shown in the figure. The preemption level was given as 1 and the
priority was applied as 1.
total
expendit
ure
Increse
or
decrese
increase
or
decrease
%
15
change pull from of
testers from b2 79260 $45.85 317.04 25128590 0.64 1.37%
16
add priority and
premption to tester 85546 $43.76 342.184 29272472 2.10 4.57%
Step E: - Production of the facility increased by 25 units a day. The cost per unit decreased drastically
by $2.10. Repair wait labor was the next bottleneck that can be solved.
Iteration 17
Step A: - On analysis repair wait labor was the next step that can increase the production
Step B: - Increasing the tech assist operators can solve the bottleneck
Step C: - Add 1 test_tech operator on each shift
Step D: - the built model was run for a year i.e. 360000 minutes for statistics
total
expendit
ure
Increse
or
decrese
increase
or
decrease
%
16
add priority and
17 add test tesh 87493 $45.63 349.972 30620100 -1.87 -4.28%
Step E: - The production of the facility increased by 7 units a day. The cost per unit also increased by
4.28%. This increased production with higher cost is not desirable. As we had to pay $1.87 extra on
every unit produced.
Iteration 18
Step A: - since the change in model is increasing the cost we switch back to the model on iteration 16.
Step B: - variance is to be checked
Step C: - experimenter is used to check the variances with the change in the pseudo random number
used while modelling
Step D: - the built model was experimented for a year with 50 replications and a ship of 13.

The variance chart after the experiment is shown below
When looking at the variance data we see that the standard deviation is very high
Step E: - With the experimenter results, the built model is highly unreliable and has got the
production value near one of the maximums.
We also know from the statement
“Variability is Performance Villain”
- The Goal by Goldratt
Iteration 19
Step A: - With the experimenter results we can observe that there is a place that reduces the
production level to less than 10000 units a year
Step B: - The area that has very stringiest rule is the buffer rejected15p. It should be loosened to allow
production
Step C: - increase the buffer space on the buffer rejected 15p
Step D: - the built model was experimented again to check variance for a year with 50 replications and
a ship of 13 and no alternatives.

The variance chart obtained is shown below after the increase in buffer space.
When looking at the variance data we see that the standard deviation just 169 units which is much
better than the previous one.
The confidence chart of the experiment is shown below.

My obtained values are little away from the confidence interval of 95%, 90% and 99%.
The statistics with the change in buffer is shown below.
total
expendit
ure
Increse
or
decrese
increase
or
decrease
%
16
add priority and
17 add test tesh 87493 $45.63 349.972 30620100 -1.87 -4.28%
19
increase buffer at b2
because of
experimentation 85696 $43.80 342.784 29375218 1.83 4.00%
Step E: - With the experimenter results the built model is satisfied but the cost of the model is higher
than the one on the iteration 16. Excess amount of resources are checked to be reduced
Iteration 20
Step A: - we analyze that what combinations can reduce the cost but yield us the approximately the
same production
Step B: - On analysis we observe that 2 unit adjustment is not as helpful as thought
Step C: - remove one unit adjustment
Step D: - the built model was run for a year i.e. 360000 minutes for statistics
total
expendit
ure
Increse
or
decrese
increase
or
decrease
%
19
increase buffer at b2
because of
experimentation 85696 $43.80 342.784 29375218 1.83 4.00%
20
remove 1 unit
adjsutment 85671 $43.73 342.684 29358081 0.07 0.17%
Step E: - The production of the facility remained the same. The cost per unit also decreased by 0.17%.
This is the best that could be suggested for the repair cells as any further change by me had a
higher cost per unit. The final statistics are shown on the documentation stage i.e. step 12.
FULL MODEL INITIAL STAGES
We optimize the Receiving Area, Shipping Area and add the built designer modules of Lease
reconditioning cell and In house repair Cell. The steps followed are shown in the form of iterations.
The incoming FedEX Shipments are received at the receiving area.
The calculations for the shipments are as follows:-
Total Units
Time Percentage Split No of Arrving Units
5:00 AM 10.00% 800
7:00 AM 25.00% 2000
8:00 AM 55.00% 4400
10:00 AM 10.00% 800
8000
We create the incoming parts i.e. FedEX Shipments which are active with profiles and arrives at the
specified time.
We create an algorithm to have the units containing either 1 or 2 parts in them by the following

calculations
Historical average recorded= 1.8 units per box
Total incoming units = total units/ average
= 8000/1.8
= 4445 units
Now with recalculating the number of incoming units we get the following for the specified times
Total Units
Time Percentage Split No of Arrving Units
5:00 AM 10.00% 444.5
7:00 AM 25.00% 1111.25
8:00 AM 55.00% 2444.75
10:00 AM 10.00% 444.5
4445
We input the specified values on the arrival time as shown in the figure below
For the validation, the model is run for 330 minutes to check whether we received 445 units.
The built model at this stage looks as shown below
Now we add a receiving station were the units are processed every 7 seconds. This operation requires a
level 1 operator too. The received units are stored into a buffer for validation
To validate that the units are processed every 7 seconds we run the model for 500 minutes

On experimentation for 5 days a week and 50 weeks a year the part statistics obtained are shown
below.
FedEX_Shipments 1111500 1111101 0 0 0 399 768.82 249.01 6
Third_Party_Receiving 408250 406617 0 0 0 1633 703.78 620.6 6
On calculation for a day we get
FedEx shipments = number of units shipped / total days
= 1111101/(5*50)
= 4444.40
With this calculation I can conclude that I can process all the incoming units with an average time of 6
hours for the FedEx shipments
The only thing we have to watch out is if on building the whole model and the cycle time per part is
high, another receiving area machine can be added to reduce the cycle time per unit.
Log In area
All the units from the receiving storage is sent to the box unpacking machine. The machine has an
output function separating the boxes containing 2 units from the ones containing 1 in it. For this we use
a random real attribute on the actions on create of the FedEx components to have a pseudo random
number generated to it as shown below
The logic used is as shown in the figure
X= number of units containing 2 units
Y= number of units containing 1 unit
Total units to be shipped is (2X+Y) = 8000
The number of incoming boxes(X+Y) = 8000/1.8
= 4445
Therefore when solving the equation the units associated with X and Y
X= 3555
Y= 890
Therefore, X is 79.97% and Y is 20.03%
With this percentages we separate the boxes with the output rule on the box unpacking machine as
shown in the figure below

Also to distinguish the box we separate the ones with 1 unit on it. We change the icon color to
red by the code shown below. An output of the model is shown after validation.
We separate the in house repair, lease units and the 3rd
party repair unit by assigning a separate
attribute on the actions on input, an example is shown below for the login unit of processing the box
containing 1 unit.
We split the units with the following rule on the output rule using the defined attribute. An example of
of output rule on the the login unit of processing the box containing 2 unit is shown below.
To enble the box with 2 units to be split as 2 different units we change the machine type of the
unit_login2 as general, where the input to the machine is 1 unit and the output to the machine is pre
defined attribute. The change is shown on the figure below.
The input in the attribute is as follows:-

The output quantity of the unit is kept 1 as the unit coming out of the machine is N+1 i.e. 2 units as
desired. To validate this count of the incoming and outgoing units are calculated. The output is shown
below
The incoming count is 97 i.e. 96 units +1 which is currently working. The output is twice the incoming
i.e. 2X96 = 192. With this the built model is assumed correct. The built model is shown below.
The model is run for a year for getting the statistics. The output statistics is shown below.
Name No.Entered No.Shipped No.Scrapped No.Assembled No.Rejected W.I.P. AvgW.I.P. AvgTime SigmaRating
FedEX_Shipments 41024 37317 0 0 300135 3707 4276.55 10945.74 6
The number of rejections is because the receiving area is completely blocked and the buffer unit fills
up.
Iteration 2
We add 3 SHIFTS address the problem address the problem of low production. With 3 shifts added to
the model, number of operators increase on every shift. The model is run for a year for analysis. The

output has shown increase in production. The statistics are as shownon the figure below.
FedEX_Shipments 114876 111594 0 0 1046217 3282 3926.28 12304.22 6
On analysis the production has increased by 296 units per day and the cost has reduced by 21 cents.
Cycle wait labor percentage was observed.
Test Configuration Units/output $/Unit Unit/day total expenditure
Increse
or
decrese
increase
or
decrease
%
1 base model 37623 $21.47 150.492 5661961 0
2 add 3 shifts 111594 $21.26 446.376 49812883 0.21 0.97%
Iteration 3
Taking cycle wait percentage as the constraint, we add 1 level 2 employee on each shift. The built
model is run for a year to see the changes. The statistics are as follows
Increse
or
decrese
increase
or
decrease
%
2 add 3 shifts 111594 $21.26 446.376 49812883 0.21 0.97%
3
ADD LEVEL 2
EMPLOYEE 136685 $21.69 546.74 74731157 -0.43 -2.02%
The production has increased by 100 units per day and the cost but the cost has also gone up by 43
cents which is bad. Constraint is now switched to blockage
Iteration 4
To address the blockage analysis was done. The result was to add a unit login2 to increase the
production. This built model is again analysed for a year to observe the changes. The statistics for the
run is shown below
Increse
or
decrese
increase
or
decrease
%
3
ADD LEVEL 2
EMPLOYEE 136685 $21.69 546.74 74731157 -0.43 -2.02%
4
ADD UNIT
LOGIN2 213533 $20.21 854.132 182385368 1.48 6.83%
This addition of unit login2 has helped us and led to a drastic increase in the production of 307 units
per day. The cost per unit has also decreased by $1.48 which is good for the model. On checking the
bottleneck cycle wait labor was observed.
Iteration 5
On analysis the cycle wait labor was caused because the nuber of machine was more than the sared
resource required for the processing. This hindered the increase in production. 3 level 2 operators was
added incresing the total to 9 people a day. After a year’s performance on simulation the following
statistics were obtaind.
Increse
or
decrese
increase
or
decrease
%
4
ADD UNIT
LOGIN2 213533 $20.21 854.132 182385368 1.48 6.83%
5
ADD LEVEL 2
EMPLOYEE 250666 $20.41 1002.664 251333774 -0.21 -1.04%
The number of units increased by 147 units per day for a increase in cost of 21 cents. On analysis idle
time was observed on the all the unit_login machine
Iteration 6
Taking the idle time as the constraint anlysis was done on what should be done to increase the
production. The result turned out that adding a buffer only to unit login station will enable some free
flow of parts rather than getting blocked. After a year time simulation resulted in the following

observations
Increse
or
decrese
increase
or
decrease
%
5
ADD LEVEL 2
EMPLOYEE 250666 $20.41 1002.664 251333774 -0.21 -1.04%
6 ADD BUFFER 1 270305 $20.22 1081.22 292259172 0.19 0.95%
Iteration 7
The model is developed to cater the incoming units of 4445 a day which is split into 8000 units a day.
The built model is as follows:-
Step 10:- Experimentation
Iteration 1
Now with this we add the optimized in house and repair units from the module and designer elements
created. 4 lease modules are added to the model as follows
• 3 for the units J4-678
• 1 for the units J11- 3000
8 repair cells are added to the model as follows
• 4 for the units J4-678
The out going units from the units log in station is sent to a dummy machine were Lease units, In house
repairs and the third party untis are seperated according to the random percentage specified for the
different units. The untis are also differntiated with changing it to the different icon

The way the units are seperated accoring to the percentages are as shown below
The units are classified differently by changing the icons and the name of the widget as follows for the
lease units and in house repairs
The different icons generated are
The code used to generate this is as follows
We import the designer elements stored as a module of the optimized repair cell and lease
reconditioning cell.
The model imported and connected is as shown below. The highlighted area is the lease reconditioning
area.

The enlarged picture of the reconditionng area is shown below
The rejected units of the reconditioning cells are sent to the buffers of the concerned units on the repair
area. The enlarged picture of the In house repair area is as follows:-

The highlighted flow paths are the paths to resend the rejected lease equipment under gone repair on
the repair cells and sent back to the pool of the reconditioning units. The outline view of the paths is
shown below
Iteration 2
Once we have connected the lease reconditioning and the repair cells we add the shipping area.
The reconditioned units are to be assembled into a pack a 25 and sent to 3 different leasing customers.
Therefore we have 9 different combinations i.e. 3 different products and 3 different leasing customers.
The overview of the built shipping are for the reconditioning cells is shown below
A handling station is used to push the units in uniformly to different packing station. The output rule
used to have a distribution of the units is sequence wait rule as shown below.

The units sent to the packing station are assembled by the assembly machine into a box of 25 units. An
example is shown in the figure below
The assembled units from the packing station is sent to a labelling station from where it is dropped at
the shipping dock.
The units coming from the repair cells to the shipping area is taken care by a multi cycle machine
which does both labelling and packing as shown below
The multi cycle is specified as shown below

Simulation of medical defibilator facility

Simulation of medical defibilator facility

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