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 FulfilledPartilally FulfilledDid 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

ActualPlanned

Actual

Planned

Actual

Planned

Actual

PlannedActual

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 OutputFedEx 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)

Input Machine People OutputBox Unpacking Level 1 Operator Lease Reconditioning Area

In House repairs3rd party Shipping

Level 2 OperatorUnit LoginReceiving 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.

Input Machine People OutputQuick Test

Repair PrepRepair Station TechnicianLoading and Unloading

Tech Asst Operator

Testers AutomaticUnit Adjustment TechnicianLabel Level 2 Operator

Final CheckTech Asst Operator

Unit History UpdateLogoutUnit 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.

Input Machine People Output

Unit Login and code change

Level 1 Operator

Cosmetic RepairTech Asst Operator

Re-Programming Station

Tech Asst Operator

Product Labeling Level 2 OperatorProduct Literature & Accessories Assembly Level 2 OperatorInspection Level 2 OperatorUnit 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

Input Machine People OutputHandling Level 1 Operator

labeling

labeling

boxing Machine

Lease Reconditioing

Units

In house Repair Units

Shipping DockLevel 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 PickupsShipping 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. The factors to be considered for analysis are:-

• 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 The factors to be considered for analysis are:-

• 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 RatingA 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 Operationslogin 0 20.81 0 0 79.17 0.03 0 0 0 0 10923test 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 9486reprogram 77.42 18.07 0 0 0 4.51 0 0 0 0 9485pdt_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 9485inspection 95.47 4.52 0 0 0 0.02 0 0 0 0 9485package 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 Timelevel1 27.58 72.42 1 20408 20407 1 0 1.42

tech_asst 64.66 35.34 1 40815 40814 1 0 1.66test_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 Shiftsnight 29.2 70.8 72day 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).

FigfFigure (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 RatingA 5179 5174 0 0 0 5 5.05 102.33 6

part statistics

Name % Idle % Busy % Filling % Emptying % Blocked % Cycle Wait Labor % Setup % Setup Wait Labor % Broken Down % Repair Wait Labor No. Of Operationsquick_test 0 7.23 0 0 36.38 56.39 0 0 0 0 1519quick_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 1097repair_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 1778unit_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 1515final_check 69.42 2.89 0 0 0 27.69 0 0 0 0 1515unit_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

Machine statistics

Name % Busy % Idle Quantity No. Of Jobs Started No. Of Jobs Ended No. Of Jobs Now No. Of Jobs Pre-empted Avg Job Timetest_tech 4.38 95.62 1 36 36 0 3 37.32tesh_asst 68.84 31.16 1 6643 6643 0 52 3.18

tech 86.33 13.67 1 2670 2670 0 59 9.91level2 13.59 86.41 1 4556 4556 0 11 0.91

Labour Stats

Name Total In Total Out Now In Max Min Avg Size Avg Time Avg Delay Count Avg Delay Time Min Time Max Timerejected15p 261 261 0 3 0 0.13 53.12 0 1043

Buffer

Name % On-Shift % Off-Shift Completed Shiftsday 29.2 70.8 73

evening 29.14 70.86 73night 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 Sigma RatingA 6230 6223 0 0 0 7 6.94 117.01 6

Part Statistics

Name % Idle % Busy % Filling % Emptying % Blocked % Cycle Wait Labor % Setup % Setup Wait Labor % Broken Down % Repair Wait Labor No. Of Operations

package 94.07 5.93 0 0 0 0 0 0 0 0 6223quick_test 0 29.66 0 0 33.69 36.65 0 0 0 0 6229quick_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 3658repair_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 7257unit_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 6223final_check 72.27 11.85 0 0 0 15.87 0 0 0 0 6223unit_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

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 Timetest_tech 6.12 93.88 1 158 158 0 0 40.64level1 5.93 94.07 1 6223 6223 0 0 1tesh_asst 81.42 18.58 1 34227 34226 1 0 2.5tech 87.44 12.56 1 9882 9881 1 0 9.29level2 16.3 83.7 1 18670 18669 1 0 0.92

Labour Stats

Name Total In Total Out Now In Max Min Avg Size Avg Time Avg Delay Count Avg Delay Time Min Time Max Timerejected15p 3660 3659 1 5 0 1.06 30.54 0 121.83

buffer

Name % On-Shift % Off-Shift Completed Shiftsday 29.2 70.8 73evening 29.14 70.86 73night 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 02 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

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %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 D: - the built model was run for a year i.e. 360000 minutes Step E: - The production of the facility increased from 40628 to 57797 units a year. The cost per unit decreased by $6.02

TestConfiguration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %

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 D: - the built model was run for a year i.e. 360000 minutes Step E: - The production of the facility increased from 57797 to 61654 units a year. The cost per unit increased by 4.77% Test

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %4 add 1 test Unit 57797 $28.71 231.188 13361973 6.02 17.33%

5add 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 D: - the built model was run for a year i.e. 360000 minutes Step E: - The production of the facility increased by 57 units a year and in turn the cost per unit increased by 12cents. Test

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %4 add 1 test Unit 57797 $28.71 231.188 13361973 6.02 17.33%

5add level 2 operators 61654 $30.08 246.616 15204863 -1.37 -4.77%

6add 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 D: - the built model was run for a year i.e. 360000 minutes 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

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %

6add reprogramming station 61711 $30.20 246.844 15232990 -0.12 -0.40%

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 D: - the built model was run for a year i.e. 360000 minutes 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



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 D: - the built model was run for a year i.e. 360000 minutes 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

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %8 add 3 tech asst 82149 $27.60 328.596 26993833 -0.08 -0.29%

9add 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 D: - the built model was run for a year i.e. 360000 minutes 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



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 D: - the built model was run for a year i.e. 360000 minutes

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



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 D: - the built model was run for a year i.e. 360000 minutes Step E: - The production of the facility decreased by 246 units in turn increasing the cost by 16 cents per unit Test




11 add reprogramming station 82182 $27.78 328.728 27015524 -0.16 -0.58%

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 D: - the built model was run for a year i.e. 360000 minutes Step E: - The production of the facility increased by 21.25% and in turn decreasing the cost by $2.35 per unit Test


11 add reprogramming station 82182 $27.78 328.728 27015524 -0.16 -0.58%

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 D: - the built model was run for a year i.e. 360000 minutes 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


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 D: - the built model was run for a year i.e. 360000 minutes 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

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %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 D: - the built model was run for a year i.e. 360000 minutes 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



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 D: - the built model was run for a year i.e. 360000 minutes 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



15 add 3 level 2 operators 107289 $27.01 429.156 46043718 -1.43 -5.59%

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 D: - the built model was run for a year i.e. 360000 minutes 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



17add 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



17add 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% 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 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 D: - the built model was run for a year i.e. 360000 minutes 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

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %18 add cosmetic station 117690 $27.23 470.76 55403744 0.30 1.09%19 add 3 level 2 operators 126697 $27.49 506.788 64208519 -0.26 -0.95%

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 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 D: - the built model was run for a year i.e. 360000 minutes 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


19 add 3 level 2 operators 126697 $27.49 506.788 64208519 -0.26 -0.95%20 add 3 tech asst 131159 $28.31 524.636 68810733 -0.82 -2.99%

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 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 D: - the built model was run for a year i.e. 360000 minutes 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

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %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%

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 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 D: - the built model was run for a year i.e. 360000 minutes Step E: - The change in the labor didn’t affect the production much and reduced the cost by 3.69% Test

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %21 change the shift patterns 140357 $28.45 561.428 78800350 -0.14 -0.48%

22reducing 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



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



24remove 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 D: - the built model was run for a year i.e. 360000 minutes Step E: - The removal of the machine affected the production and increased the cost by 4.92% Test




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 D: - the built model was run for a year i.e. 360000 minutes Step E: - The obtained results are as follows for each iteration Adding 1000 units of buffer B2 after unit login Test

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %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

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %26 add buffer after unit test 141755 $34.22 567.02 80377920 -5.66 -19.82%

27reduce 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

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %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 D: - the built model was run for a year i.e. 360000 minutes Step E: - The change in the shift pattern reduced the cost by 2 cents Test

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %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 D: - the built model was run for a year i.e. 360000 minutes Step E: - This rule increased the production by 22 units per day. Test

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %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 D: - the built model was run for a year i.e. 360000 minutes Step E: - on adding 5 buffer space to B2 Test



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



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 D: - the built model was run for a year i.e. 360000 minutes Step E: - This process increased the production and reduced the cost as follows:- Adding a unit login gave the following results Test



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.


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 D: - the built model was run for a year i.e. 360000 minutes 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

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %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 D: - the built model was run for a year i.e. 360000 minutes 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

Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %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 D: - the built model was run for a year i.e. 360000 minutes Step E: - This production increased by 14 units per day and the cost of the unit increased drastically again to $26.06Test 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 02 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 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 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 Step E: - The production of the facility increased by 19.4% 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%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. 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 by 1.4% a year. The cost per unit increased by 2.2%


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 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 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 D: - the built model was run for a year i.e. 360000 minutes 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 D: - the built model was run for a year i.e. 360000 minutes 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 D: - the built model was run for a year i.e. 360000 minutes 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 D: - the built model was run for a year i.e. 360000 minutes 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 D: - the built model was run for a year i.e. 360000 minutes 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 expenditure

Increse or decrese

increase or decrease %

11 add quick_test 65916 $48.59 263.664 17379676 5.12 9.54%

12Change 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 Step D: - the built model was run for a year i.e. 360000 minutes


total expenditure

Increse or decrese



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 expenditure

Increse or decrese


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

Step D: - the built model was run for a year i.e. 360000 minutes for the statistics.


total expenditure

Increse or decrese


14 add buffers b1, b2 77444 $46.49 309.776 23990293 0.59 1.26%

15change 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.

Step D: - the built model was run for a year i.e. 360000 minutes for the statistics.


total expenditure

Increse or decrese



16add 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 expenditure

Increse or decrese



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 expenditure

Increse or decrese



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 expenditure

Increse or decrese


19

increase buffer at b2 because of experimentation 85696 $43.80 342.784 29375218 1.83 4.00%

20remove 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 UnitsTime Percentage Split No of Arrving Units

5:00 AM 10.00% 8007:00 AM 25.00% 20008:00 AM 55.00% 440010:00 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 UnitsTime Percentage Split No of Arrving Units

5:00 AM 10.00% 444.57:00 AM 25.00% 1111.258:00 AM 55.00% 2444.7510:00 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. Name No. Entered No. Shipped No. Scrapped No. Assembled No. Rejected W.I.P. Avg W.I.P. Avg Time Sigma RatingFedEX_Shipments 1111500 1111101 0 0 0 399 768.82 249.01 6Third_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. Avg W.I.P. Avg Time Sigma RatingFedEX_Shipments 41024 37317 0 0 300135 3707 4276.55 10945.74 6Third_Party_Receiving 19701 15428 0 0 99275 4273 3616.6 19275.32 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. Name No. Entered No. Shipped No. Scrapped No. Assembled No. Rejected W.I.P. Avg W.I.P. Avg Time Sigma RatingFedEX_Shipments 114876 111594 0 0 1046217 3282 3926.28 12304.22 6Third_Party_Receiving 67266 62544 0 0 340984 4722 4017.13 21499.2 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 02 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%

3ADD 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 %


4ADD 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 %

4ADD UNIT

LOGIN2 213533 $20.21 854.132 182385368 1.48 6.83%


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 %


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 • 1 for the units J11- 4000

8 repair cells are added to the model as follows • 4 for the units J4-678 • 2 for the units J11- 3000 • 2 for the units J11- 4000

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

Iteration 3 The thirty party shipping and receiving area are modelled as follows

The third party returns are sent from the box unpacking machines to a third party buffer. Once the processing are done on the incoming products it is stored on the shipping dock specified for the third party. We use flag mechanism to operate the shipping of the units.

• Once the truck arrives the flag is set to 1 • Once the buffer quantity of the third party shipping dock is <=1, Flag is set to 0

The arrival of the truck is specified based on the simulation time as showm in the figure by turning the parts active with profile

3 Once the flag is turned on the units are shipped to the third party receiving buffer (Third party repair Station) again the return times of the third party arrivals are specified with the following times.

• On this times the flag is set to 1 • Once the buffer size is <=1, flag is set to 0

With this the third party receiving area is modelled as shown in the figure below

The overall segment model of the 3rd party shipping and the receiving is highlighted in the figure below

Iteration 4 The shipping dock is modelled as follows

Once all the incoming products it is stored, all the units are shipped the same way third party shipping and receiving dock works. We use flag mechanism to operate the shipping of the units.

• Once the truck arrives the flag is set to 1 • Once the buffer quantity of the shipping dock is <=1, Flag is set to 0

The arrival of the truck is specified based on the simulation time as shown in the figure by turning the parts active with profile

Once the flag is turned to 1 all the units are loaded to the truck with a cycle time of 7 seconds. And once the buffer is <=1 the shipping stops and the buffer starts filling again till the next truck arrives on the next designated time.

Iteration 5 The service level for the quantity missing the desired service level is modelled as follows

• Service level probabilities are created with the help of real variables • Service levels of the units are designated with the help of integer attributes • Expiration of the service time is calculated with the help of real attributes

Different service levels are designated as shown on the above figure. The expiration of the service levels are also calculated as shown in the code written above. The expiration of the repair units service levels are calculated with the following rule on a real attribute as shown below.

Step 11:- Experimentation

The built model is run for a 28800 mins to see how the system works The obtained statistics are shown below:-

Name

FedEX_Shipments

Third_Party_Receiving

Unit_678_J4

Unit_3000_J11

Unit_4000_J11

Unit_4000_J11_R

Unit_3000_J11_R

Unit_678_J4_R

customer_1_678

customer_2_678

customer_3_678

customer_3_3000

customer_2_3000

customer_1_3000

customer_1_4000

customer_2_4000

customer_3_4000

No. Entered 153141 53223 32255 13365 8006 7019 11399 27354 429 429 429 178 178 178 106 106 106

No. Shipped 0 53144 0 0 0 6991 11361 27131 428 428 428 178 178 178 106 106 106

Total Numbers Shipped 53144 6991 11361 27131 10700 10700 10700 4450 4450 4450 2650 2650 2650 The total shipped accounts to 152027 units a year. Now dividing the total by 19 shifts worked by the employees gives us exactly 8001.42 units a day. To verify my model is correct we calculate the number of truck incoming to ship the units i.e. we need to have = (3 trucks a day*7 * 3 weeks) – warm up period arrivals = 60 – 3 = 57 With this the flag mechanism on the shipping dock works verifies with the output

The built model is run for a year with a warm-up time of a day(1440 mins) to analyze the system. The obtained statistics are shown below:-

The nunber of units shipped is

Name

FedEX_Shipments

Third_Party_Receiving

Unit_678_J4

Unit_3000_J11

Unit_4000_J11

Unit_4000_J11_R

Unit_3000_J11_R

Unit_678_J4_R

customer_1_678

customer_2_678

customer_3_678

customer_3_3000

customer_2_3000

customer_1_3000

customer_1_4000

customer_2_4000

customer_3_4000

No. Entered 1993270 696422 419583 174465 104512 90129 149252 358325 5593 5593 5593 2326 2326 2326 1393 1393 1393No. Shipped 0 696294 0 0 0 90108 149215 358128 5592 5592 5592 2326 2326 2326 1393 1393 1393Total Numbers Shipped 696294 90108 149215 358128 139800 139800 139800 58150 58150 58150 34825 34825 34825 The total shipped accounts to 1992305 units a year. Now dividing the total by 249 days run gives us exactly 8001.22 units a day. To verify my model is correct we calculate the number of truck incoming to ship the units I.e. we need to have = (3 trucks a day*5days * 50 weeks) – warm up period arrivals = 750 – 3 = 747 With this the flag mechanism on the shipping dock works verifies with the output The buffer statistics of the model run for a year is as follows

Name Total In Total Out Now In Max Min Avg Size Avg Time Min Time Max TimeReceiving_buffer_1 1107054 1107054 0 2409 0 81.36 26.35 0 76.24

lease.b2 87208 87208 0 5 0 0.02 0.1 0 13.59lease.b1 100349 100349 0 2 0 0.16 0.56 0 64

lease01.b2 140022 140022 0 10 0 0.71 1.83 0 27.09lease01.b1 160813 160811 2 2 0 1.54 3.44 0 83.46lease02.b2 139677 139677 0 10 0 0.72 1.84 0 26.7lease02.b1 160248 160246 2 2 0 1.54 3.45 0 72.48lease03.b2 139729 139728 1 10 0 0.7 1.8 0 26.49lease03.b1 160443 160441 2 2 0 1.54 3.44 0 95.24

REPAIR_J4_678 420248 420191 57 399 0 41.5 35.41 0 324.25lease04.b2 104441 104441 0 10 0 0.12 0.4 0 21.13lease04.b1 119810 119808 2 2 0 0.57 1.7 0 64.45

repair_cell.rejected15p 29971 29969 2 8 0 1.26 15.07 0 149.04repair_cell.B1 84437 84435 2 18 0 2.58 10.94 0 64.16repair_cell.B2 99187 99177 10 10 0 7.38 26.69 0 189.96

repair_cell01.rejected15p 29824 29823 1 8 0 1.27 15.25 0 126.95repair_cell01.B1 83708 83707 1 22 0 2.53 10.84 0 85.13repair_cell01.B2 98548 98538 10 10 0 7.61 27.71 0 189.76

repair_cell07.rejected15p 29881 29880 1 7 0 1.26 15.08 0 131.37repair_cell02.rejected15p 29843 29842 1 7 0 1.27 15.24 0 141.67

repair_cell02.B2 98743 98733 10 10 0 7.51 27.28 0 169.7repair_cell02.B1 84016 84015 1 19 0 2.49 10.65 0 68.45

repair_cell03.rejected15p 29470 29467 3 6 0 0.65 7.94 0 72.21repair_cell03.B2 68781 68781 0 10 0 0.52 2.72 0 138.19repair_cell03.B1 58511 58511 0 10 0 0.48 2.96 0 37.01output_R_J4_678 358130 358130 0 21 0 1.35 1.35 0 17.2


output_R_J11_3000 149216 149216 0 6 0 0.2 0.47 0 9.74repair_cell05.rejected15p 29542 29542 0 6 0 0.65 7.87 0 93


output_R_J11_4000 90110 90110 0 4 0 0.09 0.37 0 7.1repair_cell06.rejected15p 29854 29853 1 8 0 1.25 15.06 0 121.4


Lease_J11_3000_buffer 200652 200652 0 18 0 0.02 0.04 0 30.08Lease_J11_4000_buffer 119812 119812 0 45 0 0.53 1.6 0 113.2

Lease_J4_678_buffer 481535 481498 37 160 0 14.8 11.02 0 107.7REPAIR_J11_3000 175465 175465 0 10 0 0.07 0.14 0 13.9REPAIR_J11_4000 105481 105481 0 11 0 0.07 0.25 0 26.28return_3rdparty 696328 696319 9 1506 8 370.15 190.6 9.77 933.99repair_cell07.B1 84014 84011 3 19 0 2.52 10.75 0 65.02output_L_J4_678 419426 419425 1 20 0 0.05 0.04 0 15.24

output_L_J11_4000 104440 104440 0 6 0 0.01 0.04 0 14.43output_L_J11_3000 174401 174401 0 11 0 0.02 0.04 0 14.35

Shipping_dock 1321856 1321678 178 3913 0 1163.71 315.66 0 1054.86repair_cell07.B2 98753 98743 10 10 0 7.47 27.13 0 161.79thirdparty_buffer 696317 696317 0 13 0 0.02 0.01 0 2.35shipping_dock_tp 696758 696319 439 1270 0 429.88 221.22 0 663.89

repair_receiving_buffer 696319 696319 0 1448 0 151.43 77.98 0 221.79receivingArea_buffer 1107746 1107070 676 3401 0 1485.25 480.75 0 1045.66


lease06.b2 87194 87194 0 7 0 0.02 0.09 0 19.53lease06.b1 100302 100302 0 2 0 0.15 0.55 0 32.41


The cost calculations are as follows

• Units been through the reconditioning cells • Units rerouted from reconditioning to repair cells and then back to reconditioning • Third party shipping units and receiving units

All the calculations are shown on the documentation step

Step 12:- Implementation and Documentation

After implementation of the above verified results we try to document the following The Final Gantt Chart For the steps taken and the dates to complete the project is as follows:- We had many plans which failed because of the lack of beforehand knowledge of what time it constitutes. The ones that are displayed on the red are the deviated days from planned.

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 11-14 Aug 15

Planned

Actual

Planned

Actual

Planned

Actual

Planned

Actual

Planned

Actual

Planned

Actual

Planned

Actual

Planned

ActualPlanned

Actual

Planned

Actual

Planned

Actual

PlannedActual

Date

Project Description, Class Notes

Project Planning

Project Definition

System Definition

Implementation and Documentation

Input Data Preparation

Model Translation

Final Experimental Design

Experimentation

Analysis and Interpretation

Preliminary Expermental Design

Conceptual Model Definition

Lease reconditioning cells After optimization the most that I could get was 600 units with a cost of $25.03. The built model is shown below

The part statistics for the optimized cells are Name No. Entered No. Shipped No. Scrapped No. Assembled No. Rejected W.I.P. Avg W.I.P. Avg Time Sigma Ratinglease_refurbishment.A 172726 150148 22564 0 0 14 16.26 33.89 2.62 The buffer statistics of the efficient model built by me is as follows Name Total In Total Out Now In Max Min Avg Size Avg Time Min Time Max Timelease_refurbishment.b2 150154 150154 0 10 0 1.33 3.19 0 27.34lease_refurbishment.b1 172723 172721 2 2 0 1.98 4.12 0 101.88 The final chart statistics are as follows:-

Name % Idle % Busy % Filling % Emptying % Blocked % Cycle Wait Labor % Setup % Setup Wait Labor % Broken Down % Repair Wait Labor No. Of Operationslease.login(1) 0 31.98 0 0 58.34 9.68 0 0 0 0 57558lease.login(2) 0 32 0 0 58.31 9.7 0 0 0 0 57598lease.login(3) 0 31.98 0 0 58.2 9.82 0 0 0 0 57569lease.test(1) 0.02 90.57 0 0 0.08 0.02 0 0 6.78 2.54 57481lease.test(2) 0.02 90.61 0 0 0.08 0.02 0 0 6.88 2.39 57558lease.test(3) 0.01 90.77 0 0 0.08 0.02 0 0 6.75 2.36 57679

lease.cosmetic(1) 24.92 60 0 0 7.58 7.5 0 0 0 0 54055lease.cosmetic(2) 24.83 56.3 0 0 11.42 7.46 0 0 0 0 50677lease.cosmetic(3) 24.72 50.67 0 0 17.45 7.16 0 0 0 0 45421

lease.reprogram(1) 30.36 43.81 0 0 19.07 6.75 0 0 0 0 78862lease.reprogram(2) 30.26 39.61 0 0 23.96 6.17 0 0 0 0 71289lease.pdt_label(1) 18.32 63.29 0 0 6.68 11.71 0 0 0 0 75946lease.pdt_label(2) 18.46 61.84 0 0 8.21 11.49 0 0 0 0 74203

lease.pdt_lit 17.01 72.99 0 0 2.18 7.82 0 0 0 0 150148lease.inspection 67.19 20.85 0 0 7.01 4.95 0 0 0 0 150148

lease.package 48.14 31.28 0 0 0 20.58 0 0 0 0 150148

The Chart for the machine statistics is below

The Variations in the production of units per day and the cost per unit is plotted. The lease reconditioning cells are plotted as shown below.

Repair cell The most efficient cell that I could manage to get was with a production of 342 units per day with a cost per unit of $43.73.The final built model is shown below

The part statistic for the efficient model is shown below Name No. Entered No. Shipped No. Scrapped No. Assembled No. Rejected W.I.P. Avg W.I.P. Avg Time Sigma Ratingrepair_cell.A 85693 85671 0 0 0 22 24.25 101.88 6 The final chart statistics of the optimized cell is as follows:-

Name % Idle % Busy % Filling % Emptying % Blocked% Cycle Wait Labor % Setup % Setup Wait Labor % Broken Down% Repair Wait LaborNo. Of Operationsrepair_cell.package 74.37 23.8 0 0 0 1.83 0 0 0 0 85671

repair_cell.quick_test(1) 0 59.46 0 0 23.93 16.61 0 0 0 0 42812repair_cell.quick_test(2) 0 59.56 0 0 23.99 16.46 0 0 0 0 42879

repair_cell.quick_fix 22.84 39.29 0 0 22.64 15.23 0 0 0 0 70726repair_cell.repair_prep 0.04 62.4 0 0 27.28 10.28 0 0 0 0 29953

repair_cell.repair_station(1) 2.74 83.28 0 0 6.49 7.49 0 0 0 0 14990repair_cell.repair_station(2) 2.77 83.12 0 0 6.49 7.63 0 0 0 0 14961

repair_cell.base_fn_test_calibration(1) 0.02 89.67 0 0 0 0.01 0 0 7.8 2.5 33344repair_cell.base_fn_test_calibration(2) 0.02 90.01 0 0 0 0.01 0 0 7.58 2.39 33499repair_cell.base_fn_test_calibration(3) 0.02 90.65 0 0 0 0.01 0 0 7.17 2.16 33820

repair_cell.unit_adjustment 9.79 71.39 0 0 4.6 14.21 0 0 0 0 85672repair_cell.unit_label 40.05 47.6 0 0 1.68 10.68 0 0 0 0 85671

repair_cell.final_check 44.71 47.59 0 0 1.39 6.3 0 0 0 0 85671repair_cell.unit_history 63.03 11.9 0 0 0 25.07 0 0 0 0 85671

repair_cell.logout 87.5 5.95 0 0 0.36 6.19 0 0 0 0 85671

The chart for the machine statistics is shown below

The buffer statistics of the efficient model is as follows Name Total In Total Out Now In Max Min Avg Size Avg Time Min Time Max Timerepair_cell.rejected15p 29954 29953 1 7 0 1.33 15.95 0 138.73repair_cell.B1 85673 85673 0 22 0 2.7 11.34 0 84.7repair_cell.B2 100676 100666 10 10 0 8.71 31.13 0 163.32 The Variations in the production of units per day and the cost per unit is plotted. The lease reconditioning cells are plotted as shown below.

Full Model While consolidating the optimized cells and adding the shipping dock, third party receiving and shipping area we get the following statistics and cost sheets

The total shipped accounts to 1992305 units a year on splitting the units assembled and adding to the total. Now dividing the total by 249 days run gives us exactly 8001.22 units a day.

The capital cost is going to remain the same throughout the year.

Resource Quantity Capital/Unit Captial $ M&O/UnitM&OLog-In Area Unpacking Station 5 10000 $50,000.00Log-In Workstation 45 15000 $675,000.00

Product Repair Quick Test 20 $20,000.00 $400,000.00 $2,000.00 $40,000.00Product Repair Quick Fix 10 $15,000.00 $150,000.00 $1,000.00 $10,000.00Repair Prep. 10 $10,000.00 $100,000.00 $500.00 $5,000.00Repair Station 20 $25,000.00 $500,000.00 $2,000.00 $40,000.00Base Test & Calabration Cell w/out Tester 10 $50,000.00 $500,000.00 $1,000.00 $10,000.00Automated Tester (max 3 per cell) 30 $200,000.00 $6,000,000.00 $10,000.00 $300,000.00Unit Adjust Station 20 $15,000.00 $300,000.00 $2,000.00 $40,000.00Label Station 10 $15,000.00 $150,000.00 $2,000.00 $20,000.00Final Check 10 $30,000.00 $300,000.00 $5,000.00 $50,000.00Unit History Update 10 $15,000.00 $150,000.00 $500.00 $5,000.00Product Repair Logout 10 $15,000.00 $150,000.00 $500.00 $5,000.00Product Repair Unit Packaging 10 $10,000.00 $100,000.00 $500.00 $5,000.00Buffer Space 120 1000 $120,000.00 $1,000.00 $120,000.00

Unit Login & Code Change 18 $15,000.00 $270,000.00 $500.00 $9,000.00Test Cell w/out Testers 6 $50,000.00 $300,000.00 $1,000.00 $6,000.00Automated Tester (max 3 per cell) 18 $150,000.00 $2,700,000.00 $10,000.00 $180,000.00Cosmetic Repair 18 $15,000.00 $270,000.00 $2,000.00 $36,000.00Re-Programming Station 12 $20,000.00 $240,000.00 $2,000.00 $24,000.00product labelling 12 $15,000.00 $180,000.00 $2,000.00 $24,000.00Product Literature & Accessories Assembly 6 $15,000.00 $90,000.00 $1,000.00 $6,000.00Inspection 6 $10,000.00 $60,000.00 $0.00 $0.00Packaging $10,000.00 $0.00 $500.00 $0.00Buffer Space 30 1000 $30,000.00 $1,000.00 $30,000.00

Lease Re-Con Packaging $10,000.00 $0.00 $500.00 $0.00Lease Shipment Consolidation Station (9 needed) 9

$10,000.00$90,000.00 $0.00

Consolidation Batch Packaging 1 $15,000.00 $15,000.00 $500.00 $500.00Consolidation Batch Label Printing 1 $15,000.00 $15,000.00 $500.00 $500.00Individual Customer Unit Packaging 4 $15,000.00 $60,000.00 $500.00 $2,000.00Individual Customer Unit Labeling 4 $15,000.00 $60,000.00 $500.00 $2,000.00

$500,000.00Buffer Space 717 1000 $717,000.00 $1,000.00 $717,000.00Level One Employee 160 $42,000.00 $6,720,000.00Level Two Employee 237 $51,000.00 $12,087,000.00Tech Asst Operator 592 $60,000.00 $35,520,000.00Technician 144 $75,000.00 $10,800,000.00Test Tech 96 $69,000.00 $6,624,000.00

$14,742,000.00 $73,938,000.00

Capital Cost

The cost for the units processed through the repair cells is the cost for the unit

Repair cells 597451 = Units shipped repair cell

Resource Per Unit # of Units $ X unitsLog-In Area Unpacking StationLog-In Workstation

Product Repair Quick TestProduct Repair Quick FixRepair Prep.Repair StationBase Test & Calabration Cell w/out TesterAutomated Tester (max 3 per cell)Unit Adjust StationLabel StationFinal CheckUnit History UpdateProduct Repair LogoutProduct Repair Unit PackagingBuffer Space

Unit Login & Code ChangeTest Cell w/out TestersAutomated Tester (max 3 per cell)Cosmetic Repair $5.00 597451 $2,987,255.00Re-Programming Stationproduct labelling $1.00 597451 $597,451.00Product Literature & Accessories Assembly $3.50 597451 $2,091,078.50InspectionPackaging $1.50 597451 $896,176.50Buffer Space 0

Lease Re-Con Packaging $1.50 597451 $896,176.50Lease Shipment Consolidation Station (9 needed)Consolidation Batch PackagingConsolidation Batch Label PrintingIndividual Customer Unit Packaging $1.50 597451 $896,176.50Individual Customer Unit Labeling $1.00 597451 $597,451.00

Buffer SpaceLevel One EmployeeLevel Two EmployeeTech Asst OperatorTechnicianTest Tech

$8,961,765.00

Capital Cost Repair cells

The cost for the units that are rerouted through the lease area

Lease Unit 698560 = Units shipped from Repair Cell

Resource Quantity Per Unit # of Units $ X unitsLog-In Area Unpacking Station 5Log-In Workstation 45

Product Repair Quick Test 20Product Repair Quick Fix 10 $5.00 698560 $3,492,800.00Repair Prep. 10Repair Station 20 $10.00 698560 $6,985,600.00Base Test & Calabration Cell w/out Tester 10Automated Tester (max 3 per cell) 30Unit Adjust Station 20Label Station 10 $1.00 698560 $698,560.00Final Check 10Unit History Update 10Product Repair Logout 10Product Repair Unit Packaging 10 $1.50 698560 $1,047,840.00Buffer Space 120

Unit Login & Code Change 18Test Cell w/out Testers 6Automated Tester (max 3 per cell) 18Cosmetic Repair 18Re-Programming Station 12product labelling 12Product Literature & Accessories Assembly 6Inspection 6PackagingBuffer Space 30

Lease Re-Con Packaging $1.50 698560 $1,047,840.00Lease Shipment Consolidation Station (9 needed) 9Consolidation Batch Packaging 1 $7.50 27933 $209,497.50Consolidation Batch Label Printing 1 $1.00 27933 $27,933.00Individual Customer Unit Packaging 4Individual Customer Unit Labeling 4

Buffer Space 717Level One Employee 160Level Two Employee 237Tech Asst Operator 592Technician 144Test Tech 96

$13,510,070.50

Capital Cost Lease unit

The cost for the units that are rerouted through the repair cell and then back to the lease area

Lease Unit 104027 = Units shipped from Repair Cell

Resource Quantity Per Unit # of Un $ X unitsLog-In Area Unpacking Station 5Log-In Workstation 45

Product Repair Quick Test 20Product Repair Quick Fix 10 $5.00 104027 $520,135.00Repair Prep. 10Repair Station 20 $10.00 104027 $1,040,270.00Base Test & Calabration Cell w/out Tester 10Automated Tester (max 3 per cell) 30Unit Adjust Station 20Label Station 10Final Check 10Unit History Update 10Product Repair Logout 10Product Repair Unit Packaging 10Buffer Space 120

Unit Login & Code Change 18Test Cell w/out Testers 6Automated Tester (max 3 per cell) 18Cosmetic Repair 18 $5.00 104027 $520,135.00Re-Programming Station 12product labelling 12 $1.00 104027 $104,027.00Product Literature & Accessories Assembly 6 $3.50 104027 $364,094.50Inspection 6Packaging $1.50 104027 $156,040.50Buffer Space 30

Lease Re-Con PackagingLease Shipment Consolidation Station (9 needed) 9Consolidation Batch Packaging 1Consolidation Batch Label Printing 1Individual Customer Unit Packaging 4 $1.50 104027 $156,040.50Individual Customer Unit Labeling 4 $1.00 104027 $104,027.00


$2,964,769.50

Capital Cost Lease unit rerouted

The cost for the units that are rerouted through the third party receiving and shipping area

3rd party units 696294 = Units shipped

Resource Quantity Per Unit # of Units $ X unitsLog-In Area Unpacking Station 5Log-In Workstation 45

Product Repair Quick Test 20Product Repair Quick Fix 10Repair Prep. 10Repair Station 20Base Test & Calabration Cell w/out Tester 10Automated Tester (max 3 per cell) 30Unit Adjust Station 20Label Station 10Final Check 10Unit History Update 10Product Repair Logout 10Product Repair Unit Packaging 10Buffer Space 120

Unit Login & Code Change 18Test Cell w/out Testers 6Automated Tester (max 3 per cell) 18Cosmetic Repair 18Re-Programming Station 12product labelling 12Product Literature & Accessories Assembly 6Inspection 6PackagingBuffer Space 30

Lease Re-Con PackagingLease Shipment Consolidation Station (9 needed) 9Consolidation Batch Packaging 1Consolidation Batch Label Printing 1Individual Customer Unit Packaging 4 $1.50 696294 $1,044,441.00Individual Customer Unit Labeling 4 $1.00 696294 $696,294.00


$1,740,735.00

Capital Cost Third party shipping and receiving

The total cost calculation with respect to the above sheets are shown below

Minimum Annual Revinue Requirements

End of Year Book Depr Tax Depr Book Value Equity Return Debt Interest Tax Annual Cost Min. Ann. Rev. Req. (P/F 20,n) Discounted0 $14,742,000.001 $2,457,000.00 $2,948,400.00 $12,285,000.00 $2,063,880.00 $442,260.00 $1,048,320.00 $101,115,340.00 $107,126,800.00 0.8333 $89,268,762.442 $2,457,000.00 $4,717,440.00 $9,828,000.00 $1,719,900.00 $368,550.00 -$360,360.00 $101,115,340.00 $105,300,430.00 0.6945 $73,131,148.643 $2,457,000.00 $2,830,464.00 $7,371,000.00 $1,375,920.00 $294,840.00 $668,304.00 $101,115,340.00 $105,911,404.00 0.5787 $61,290,929.494 $2,457,000.00 $1,698,278.40 $4,914,000.00 $1,031,940.00 $221,130.00 $1,193,774.40 $101,115,340.00 $106,019,184.40 0.4823 $51,133,052.645 $2,457,000.00 $1,698,278.40 $2,457,000.00 $687,960.00 $147,420.00 $964,454.40 $101,115,340.00 $105,372,174.40 0.4019 $42,349,076.896 $2,457,000.00 $849,876.30 $0.00 $343,980.00 $73,710.00 $1,300,735.80 $101,115,340.00 $105,290,765.80 0.3349 $35,261,877.47

$14,742,000.00 $14,742,737.10 Present $352,434,847.56Equal Annual $105,977,158.66$/unit cost $53.19

The total cost per unit comes to $53.19 Future analysis Since due to the time constraints we stop with this step. But there are a lot of scope to improve the process and get the cost per unit down and improve the production The following can be done to reduce the cost incurring per unit.

• Looking at the machine stats we can infer that receiving area is idle 89% of the times. So we can do the following to reduce the cost

• Third party repair machine as idle for 86% • Third party receiving are idle too. • The packaging units are idle a lot of time. 3 • Reduce the buffer spaces on the whole model from 717 unit spaces

The suggested remedies are:-

• Reduce the number of people decreasing the number of shifts to 2 • Assigning the same labor to receiving area and third party receiving • Reduce the shifting time of packing units • Reduce the number of operators on the packaging line • Find alternatives to reduce the used buffer spaces as the cost per unit is very high

References [1] https://en.wikipedia.org/wiki/Theory_of_constraints [2] http://www.leanproduction.com/theory-of-constraints.html [3] https://www.mindtools.com/pages/article/toc.htm [4] http://www.ijird.com/index.php/ijird/article/view/58541/45767 Appendix The iteration for optimization of the reconditioning cells are shown below Test Configuration Units/output $/Unit units/ day total expenditure Increse or decrese increase or decrease %

1 Base 9484 $46.84 37.936 359785 02 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%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%


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%


11 add reprogramming station 82182 $27.78 328.728 27015524 -0.16 -0.58%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%


15 add 3 level 2 operators 107289 $27.01 429.156 46043718 -1.43 -5.59%


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%19 add 3 level 2 operators 126697 $27.49 506.788 64208519 -0.26 -0.95%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%


23 increase 3 level 1 operators 140236 $28.28 560.944 78664543 -0.88 -3.21%



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%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%29 change the shift patterns 141442 $27.22 565.768 80023357 0.02 0.07%


31 add 5 buffers b2 149057 $26.43 596.228 88871957 0.18 0.68%


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%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%37 add 2 buffer units b1 150148 $25.03 600.592 90177688 0.05 0.20%

https://en.wikipedia.org/wiki/Theory_of_constraints

http://www.leanproduction.com/theory-of-constraints.html

https://www.mindtools.com/pages/article/toc.htm

http://www.ijird.com/index.php/ijird/article/view/58541/45767

The iteration for optimization of the Repair cells are shown below


Increse or decrese


1 base model 6223 $92.62 24.892 154903 02 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%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%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%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%11 add quick_test 65916 $48.59 263.664 17379676 5.12 9.54%


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%



17

increase buffer at b2 because of experimentation 85696 $43.80 342.784 29375218 -0.05 -0.11%

Documents

Simulation of medical defibilator facility