LSS Maccor - Green Belt Project

Maccor: Battery Testing Equipment Lean Six Sigma: Green Belt Report

Collaborators: v Brian King -‐ [email protected], Maccor Inc. v Farhana Enam -‐ [email protected], The University of Oklahoma v Jay Patel -‐ [email protected], The University of Oklahoma v Leslie Barnes -‐ [email protected], The University of Oklahoma v Nauman Khan -‐ [email protected], The University of Oklahoma v Nitya Gupta -‐ [email protected], The University of Oklahoma v Tarek Firoze Akhtar -‐ [email protected], The University of Oklahoma

Submitted to: v Dr. B. Mustafa Pulat -‐ [email protected], The University of Oklahoma

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ABSTRACT Maccor Inc, company has been plagued with defects and intermittent connections related to their customer interface. Previous customers have had difficulties with the workmanship, quality and reliability of the Anderson Power Pole connections. Despite initial visual inspections and testing these defects passed Maccor’s Quality Assurance department. Data of 7 metrics showing significant defects of 12 Maccor Battery test systems were gathered.

When analyzing the data and with discussions from Maccor employees it was determined that Maccor had recently hired several new wiring assembly personnel that may not have understood the process or expectations completely. Maccor did not have a formal or visual training program, only peer to peer. As a group we discussed the possibility to help Maccor resolve was to complete a standard operating system and to make a visual poster explaining procedures and techniques including visual example.

The results of defects from the initial 12 Maccor Battery test systems gave the Customer Interface a high amount of defects per unit 58.33% with a low Sigma value of 2.883. The Rolled Throughput Yield was a first attempt of 83.56%. The owner of the company has a desired view of maintaining 99% or better in everything related to shipping a Maccor system.

The results of defects after the implementation of the SOS and Visual poster gave the Customer Interface an improved defect per unit of 25% with a higher Sigma value of 3.536. The Rolled Throughput Yield was a first attempt improved to that of 99.89%. What is not explained in these numbers is that of the seven metrics observed many of them had multiple defects and numerous hours of rework and materials included. The cost of rework of the first 12 Maccor systems was $4193.55 and after the improved phase for a second set of 12 Maccor systems was $48.85. The annual return of investment is 10259%.

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DEFINE PHASE: 1.1 Problem Statement Defects in the customer interface of the Maccor battery testing equipment are creating excessive variability and little standardization in the wiring process, which results in multiple defects and numerous hours of rework. 1.2 Identify Critical to Quality/Performance

Customers are wanting to be able to interface with their batteries without flaw and polarity, which dealt to incorrect placement of color coded wiring insulation (backwards battery connection) free from intermittent connection (connections were not reliable – due to over bent appc creating a gap into the mates of appc connections).

1.3 Performance Metrics

We will collect the number of defects before and after improvement for our measure of quality analysis; the mean and standard deviation of the defects will also be considered.

1.4 Project Objectives

We target to meet the company’s goal of 0.0025% defects per cabinet or less. The current defect percentage does not meet this goal, which will be defined further in the Measure phase. This means there should be at most 1 defect per cabinet (400 wires).

1.5 Customers, Suppliers and Process Outline

SUPPLIER • Mechanical Wiring Assembler

INPUTS • Mechanical Assembler • Wire Cutters • Wire Strippers • Pneumatic Crimper • Air Pressure Source • Battery Testing Equipment with previous processes complete • Anderson Power Pole Contacts

PROCESS • SEE BELOW

OUTPUTS • Completed battery testing equipment product

CUSTOMER • Quality Control Technician • Final Product Customer (ITM, Apple, etc.)

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This process will be repeated multiple times as there are 8 wires for each row with a total of 12 rows for each customer interface, as shown below.

designate and

seperate the wires in regards to

row location on customer interface

measure and cut each wire to specieic length based on the location of the

powerpole interface (column location)

measure and strip the length

of terminated powerpole insulation

attach an Anderson powerpole contact to striped and terminated

wire

crimp Anderson powerpole contact with

pneumatic crimper

insert Anderson powerpole contact

into correct customer interface location based on the color of the wire's insulation

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1.6 Benefits and Value of Project

The value of this project is to improve overall quality and reliability to the customer interface of Maccor Battery test systems. This could possibly have a long-‐standing relationship with the end customers and could sway the customers desire with regards to future purchases. Maccor Inc. builds approximately 150 Systems per year with a customer interface of 96 channels per system. Using J08294 as an example for a single worst case system ROI the following is true.

𝑅𝑂𝐼 =384 𝑐𝑟𝑖𝑚𝑝𝑠 $0.05 𝑝𝑒𝑟 𝑐𝑜𝑛𝑡𝑎𝑐𝑡 𝑐𝑟𝑖𝑚𝑝 + (8 𝑙𝑎𝑏𝑜𝑟 ℎ𝑜𝑢𝑟𝑠/𝑠𝑦𝑠𝑡𝑒𝑚)($65.00/ℎ𝑜𝑢𝑟)

16 𝑠𝑡𝑢𝑑𝑒𝑛𝑡 𝑑𝑒𝑠𝑖𝑔𝑛 ℎ𝑜𝑢𝑟𝑠 $30.00 𝑝𝑒𝑟 ℎ𝑜𝑢𝑟 + ($25.00 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝑐𝑜𝑠𝑡)

𝑅𝑂𝐼 = $539.20$505.00 = 1.0677 = 106.77% 𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑚𝑒𝑛𝑡

𝑅𝑂𝐼 𝑦𝑒𝑎𝑟𝑙𝑦 =$539.20 150 𝑠𝑦𝑠𝑡𝑒𝑚𝑠

$505 = $80,880$505 = 160.158 = 16,015% 𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑚𝑒𝑛𝑡

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MEASURE PHASE: 2.1 Data Collection

The current state metric performance data was collected for twelve systems, and is as tabulated below.

2.2 “AS IS” Value Stream Map

By taking the current manufacturing process and other relevant data into consideration, the “AS IS” Value Stream was mapped using the support process boxes.

Defect J08290 J08291 J08292 J08293 J08294 J08295 J08296 J08297 J08298 J08299 J08300 J08301Wire stripped too long 0 0 0 0 384 121 0 5 1 0 1 0Visible wire strands 0 6 0 0 102 116 0 9 9 0 14 6Crimp on wrong side contact 0 0 0 0 2 2 0 0 0 0 0 0Wire crimped on insullation 0 0 0 0 0 1 0 0 0 0 0 0Wire pulled from contacts 0 0 0 0 3 5 0 0 0 0 0 2Wrong color in housing 0 0 0 0 0 0 0 0 0 0 0 0Contact bent down 0 0 0 0 0 0 0 0 0 0 0 0Sum of Defects 0 6 0 0 491 245 0 14 10 0 15 8Rework in hours in disassembly 0.5 4 4 0.5 0.5 0.5 0.5Rework in hours in reassembly 1 8 8 2 2 2 1

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2.3 Baseline Performance Display.

The observed data was used to calculate baseline performance. The tables below show the Average, Standard Deviation, Defects per Million Opportunities (DPMO) and Rolled Throughput Yield (RTY) for the relevant data.

• Average, Standard Deviation & DPMO

Total Defects 348+121+5+1+1 512 Total Defects6+102+116+9+9+1

4+6262

Total Possible Defects 96*4*12 4608 Total Possible Defects 96*4*12 4608

Percentage of Defects .11111*100 11.11% Percentage of Defects .05686*100 5.69%

0.056858

Visible wire strands

Defects Per Units (DPU) = Total Defects/Total Units

Sampled512/4608 0.111111

Wire Stripped too Long

Sigma (σ)NORMSINV(1-‐

(DPMO/1000000))+ 1.5

2.720699


Sampled262/4608

Defects Per Million Observed (DPMO) =

(DPU*1000000)/Opportunities for error in a unit

(.1111*1000000)/1

111100



(.05686*1000000)/1

56860


(DPMO/1000000))+ 1.5

3.081692

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Total Defects 3+5+2 10 Total Defects 0 0

Total Possible Defects 96*4*12 4608 Total Possible Defects 96*4*12 4608

Percentage of Defects .00217*100 0.22% Percentage of Defects 0*100 0.00%

Wire pulled from Contacts

(.00217*1000000)/1

2170


(DPMO/1000000))+ 1.5

4.35233



Wrong color in Housing



(0*1000000)/1 0


(DPMO/1000000))+ 1.5

∞


Sampled0/4608 0


Sampled(10/4608) 0.00217

Total Defects 0 0 Total Defects 7 7

Total Possible Defects 96*4*12 4608 Total Possible Defects 12 12

Percentage of Defects .0*100 0.00% Percentage of Defects .58333*100 58.33%

2.883

Contact bent down Overall Observation


(DPMO/1000000))+ 1.5

∞


Sampled(7/12)



(.58333*1000000)/7


(DPMO/1000000))+ 1.5

0.58333Defects Per Units (DPU) = Total Defects/Total Units

Sampled0/4608 0



(0*1000000)/1 0 83332.9

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• RTY

2.4 Detailed Problem Statement

The defects in the customer interface of the Maccor battery testing equipment create excessive variability and minimal standardization in the wiring process, which result in multiple defects and numerous hours of rework. After inspection of 12 systems, the problems were narrowed down to seven major categories leading to the concerned defect. These defects are categorized as follows: • Wire stripped too long • Visible wire strands • Crimps on wrong side contact • Wire crimped on insulation • Wire pulled from contacts • Wrong color in housing • Contact bent down

RTY -‐ Rolled Throughput Yield

Step 4

Step 5

100%

100%

100%

88.89%

94.21%(1-‐(267/4608))*100

(1-‐(0/4608))*100

(1-‐(0/4608))*100

Step 1

Step 2

Step 3

(1-‐(10/4608))*100

100*100*100*88.89*94.21*99.78RTY 83.56%

99.78%Step 6

(1 -‐ (No of Defect/Total Possible Defects))*100

Yield Percent

RTYYield at Step 1 * Yield at Step 2… * Yield at Step

6

Yield percent

PPM Wasted= (total Defects/ Total Possible Defects)*1000000

0

0

0

111111.11

57942.71

2170.14

(1-‐(512/4608))*100

(1-‐(0/4608))*100

STEP 1 FROM SOSYield = 100%



STEP 4 FROM SOSYield = 88.89%



Only 83.56% is correct after the first attempt.

0 PPM wasted

0 PPM wasted

0 PPM wasted

111111.11 PPM wasted

57942.71 PPM wasted

2170.14 PPM wasted

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It is notable that out of the seven defect categories mentioned above, the first two, namely, ‘Wire stripped too long’ and ‘visible wire strands’ have the highest occurrences as compared to the remaining “five” categories.

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ANALYZE PHASE: 3.1 Root Cause Analysis

The analysis was done for all seven machines based on cumulative defects and specific unit defects as well.

• Scatter Diagram

• Pie Chart

• Pareto Chart

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The above graph gives us the relation between the cumulative types of defects, their frequency, and also, depicts the domination of the more frequent defects over the others.

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• Histogram

Upon plotting a histogram of machines and their respective defects, two major visual inferences were made.

a) “Wire stripped too long” was a severe defect when present, and relatively non-‐occurring in other cases.

b) “Visible wire strands” was a more common defect as compared to “wire stripped too long” despite the latter having a greater frequency when viewed from a cumulative defects point of view.

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3.2 Identify Waste/lost time

Further analyses are done to identify downtime, and their respective costs.

It is notable to see that from a cost of defects point of view, “wire stripped too long” and “visible wire strands” contribute the most, and are the priority root causes.

Defect J08290 J08291 J08292 J08293 J08294 J08295 J08296 J08297 J08298 J08299 J08300 J08301Wire stripped too long 0 0 0 0 384 121 0 5 1 0 1 0Visible wire strands 0 6 0 0 102 116 0 9 9 0 14 6Crimp on wrong side contact 0 0 0 0 2 2 0 0 0 0 0 0Wire crimped on insullation 0 0 0 0 0 1 0 0 0 0 0 0Wire pulled from contacts 0 0 0 0 3 5 0 0 0 0 0 2Wrong color in housing 0 0 0 0 0 0 0 0 0 0 0 0Contact bent down 0 0 0 0 0 0 0 0 0 0 0 0Sum of Defects 0 6 0 0 491 245 0 14 10 0 15 8Rework in hours in disassembly -‐ 0.5 -‐ -‐ 4 4 -‐ 0.5 0.5 -‐ 0.5 0.5Labor removal of defective interface@$65.00p/hr -‐

$32.50-‐ -‐

$260.00 $260.00-‐

$32.50 $32.50-‐

$32.50 $32.50

New Interface Wire and Labor -‐ 0 -‐ -‐ $955.00 $955.00 -‐ 0 0 -‐ 0 0Gold Anderson Contacts @$0.05 each -‐ $0.30 -‐ -‐ $19.20 $19.20 -‐ $0.70 $0.50 -‐ $0.75 $0.40Rework in hours in reassembly -‐ 1 -‐ -‐ 8 8 -‐ 2 2 -‐ 2 1Labor cost for re-‐interface @$65.00p/hr -‐ $65.00 -‐ -‐ $520.00 $520.00 -‐ $130.00 $130.00 -‐ $130.00 $65.00Total cost incurred per System Job Number -‐ $97.80 -‐ -‐ $1,754.20 $1,754.20 -‐ $163.20 $163.00 -‐ $163.25 $97.90

Cost of repair for initial 12 Maccor systems = $ 4193.55

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3.3 Verify and prioritize root causes

An ANOVA test was performed based on the following hypotheses: • Null Hypothesis, Ho: The mean defects of respective jobs are the same. • Alternative Hypothesis, Ha: There is significant difference between the mean

defects of the respective jobs.

A confidence interval of 90% was considered and the results were as follows:

Observation table:

Results:

SUMMARYGroups Count Sum Average VarianceColumn 1 5 6 1.2 7.2Column 2 5 491 98.2 27414.2Column 3 5 245 49 4030.5Column 4 5 14 2.8 16.7Column 5 5 10 2 15.5Column 6 5 15 3 38Column 7 5 0 0 0Column 8 5 0 0 0Column 9 5 0 0 0Column 10 5 0 0 0Column 11 5 0 0 0Column 12 5 8 1.6 6.8

ANOVASource of Variation SS df MS F P-‐value F critBetween Groups 49970.05 11 4542.732 1.728978 0.095245 1.708545Within Groups 126115.6 48 2627.408

Total 176085.6 59

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From the calculations shown above, we see that the “F critical” value is “1.708545” and the “actual F” value is “1.728978”. Since the actual F value is higher than the F critical it lies on the right hand side or the rejection region of the F Distribution chart. Hence, the Null Hypothesis, Ho is rejected. This implies that there is a significant difference between the mean defects, and this helps us prioritize the two defects from the rest, which are “wire stripped too long” and “visible wire strands”.

Note: The significant ANOVA result is only at 10% level, and not at the typical 5% level most statisticians use.

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IMPROVE PHASE: 4.1 Select Approaches & Tools Used to Fix Process to Desired State:

The tools that will be used to lower the number of defects and get the process to the desired state are a Standard Operating Sheet (SOS Document), a large magnetic board with the SOS blown up on the wall, training the SOS, and including an Operator Log Sheet.

4.2 Map Targeted Future State:

4.3 Improvement Plan:

Process performance will be improved by introducing the SOS document and training will standardize the process thus decreasing the number of defects. The large magnetic board will serve as a reminder to follow the SOS document consistently and repeatedly. Finally, the Operator Log Sheet will serve as a self-‐check for the operators, thus decreasing the number of defects per million. This sheet will also give a tracking method of which operator needs more training or if excessive defects are consistent, dismissed.

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4.4 Technology Useful in Supporting Process:

The SOS documents and Operator Log Sheets are low cost improvements that entail the cost of a couple hours of training and paper/ink. The magnetic board will be slightly more expensive, but the cost is justified significantly in the reduction of waste, such as copper and reworking time. Copper is bought at a higher price by the foot when new and sold at a significantly lower price when recycled. The reworking time is eight working hours per machine and will be significantly reduced.

4.5 Pilot Test:

Pilot test not needed.

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4.6 Completed Items from Improvement Plans

SOS Document on each cabinet.

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4.7 Improved State Data:

The observed data was used to calculate baseline performance. The tables below show the Average, Standard Deviation, Defects per Million Opportunities (DPMO) and Rolled Throughput Yield (RTY) for the relevant data.

• Raw Data

• Average, Standard Deviation & DPMO

Defect J08311 J08312 J08313 J08314 J08342 J08343 J08344 J08498 J08515 J08535 J08550 J08603Wire stripped too long 0 0 0 0 0 0 0 0 0 0 0 0Visible wire strands 0 0 0 0 0 0 0 0 0 0 0 0Crimp on wrong side contact 0 0 0 0 0 0 0 0 0 0 0 0Wire crimped on insullation 0 0 0 0 0 0 0 0 0 0 0 0Wire pulled from contacts 0 0 0 0 0 0 0 0 0 0 2 0Wrong color in housing 0 0 0 2 0 0 0 0 0 0 0 0Contact bent down 0 0 0 0 0 0 0 0 0 0 0 1

Sum of Defects 0 0 0 2 0 0 0 0 0 0 2 1

Rework in hours in disassembly 0.25 0.25 0.25Rework in hours in reassembly 0.25 0.25 0.25

Total Defects 0 0 Total Defects 0 0Total Possible Defects 0 4608 Total Possible Defects 0 4608Defects Per Unit (DPU) = Total Defects/Total Unit Samples 0/4608 0

Defects Per Unit (DPU) = Total Defects/Total Unit Samples 0/4608 0

Percentage of Defects 0*100 0.00% Percentage of Defects 0*100 0.00%Defects Per Million Observed (DPMO) = (DPU * 1000000)/Opportunities for error in a unit

=(0*1000000)/4608 0

Defects Per Million Observed (DPMO) = (DPU * 1000000)/Opportunities for error in a unit

=(0*1000000)/4608 0

Sigma

NORMSINV(1-‐DPMO/1000000)+1.5 infinite Sigma

NORMSINV(1-‐DPMO/1000000)+1.5 infinite

Wire Stripped Too Long Visible Wire Strands

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Total Defects 0 0 Total Defects 0 0Total Possible Defects 0 4608 Total Possible Defects 0 4608Defects Per Unit (DPU) = Total Defects/Total Unit Samples 0/4608 0

Defects Per Unit (DPU) = Total Defects/Total Unit Samples 0/4608 0

Percentage of Defects 0*100 0.00% Percentage of Defects 0*100 0.00%Defects Per Million Observed (DPMO) = (DPU * 1000000)/Opportunities for error in a unit

=(0*1000000)/4608 0


=(0*1000000)/4608 0

Sigma

NORMSINV(1-‐DPMO/1000000)+1.5 infinite Sigma

NORMSINV(1-‐DPMO/1000000)+1.5 infinite

Crimp on Wrong Side of Contact Wire Crimped on Insulation

Total Defects 2 2 Total Defects 2 2Total Possible Defects 96*4*12 4608 Total Possible Defects 96*4*12 4608Defects Per Unit (DPU) = Total Defects/Total Unit Samples 2/4608 0.000434

Defects Per Unit (DPU) = Total Defects/Total Unit Samples 2/4608 0.000434

Percentage of Defects 0.0434 0.043% Percentage of Defects 0.0434 0.043%Defects Per Million Observed (DPMO) = (DPU * 1000000)/Opportunities for error in a unit

=(0.000434*1000000)/4608 0.09419


=(0.000434*1000000)/4608 0.09419

Sigma

NORMSINV(1-‐DPMO/1000000)+1.5 6.710453 Sigma

NORMSINV(1-‐DPMO/1000000)+1.5 6.710453

Wire Pulled from Contacts Wrong Color in Housing

Total Defects 1 1 Total Defects 3 3Total Possible Defects 96*4*12 4608 Total Possible Defects 12 12Defects Per Unit (DPU) = Total Defects/Total Unit Samples 1/4608 0.000217

Defects Per Unit (DPU) = Total Defects/Total Unit Samples 3/12 0.25

Percentage of Defects .000217*100 0.022% Percentage of Defects .25*100 25%Defects Per Million Observed (DPMO) = (DPU * 1000000)/Opportunities for error in a unit

=(0.000217*1000000)/4608 0.047095


=(0.25*1000000)/12 20833.33

Sigma

NORMSINV(1-‐DPMO/1000000)+1.5 6.83759 Sigma

NORMSINV(1-‐DPMO/1000000)+1.5 3.536834

Contact Bent Down Overall Observation

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• RTY

• Cost

Relevant cost of repair for 12 Maccor systems to achieve improved state = (0.75 hr)labor x ($ 65/hr)shop rate + ($ 0.10)contacts = $ 48.85

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CONTROL PHASE: 5.1 Validate Performance Improvements

Average Defect Percentage Standard Deviation DPMO As Is To Be As Is To Be As Is To Be 58.33% 25% 2.883 3.536834 83332.9 20833.33

Initial RTY for the “as is” model showed that only 83.56% is correct after the first attempt. RTY for the “to be” model shows that 99.89% is correct after the first attempt.

The final/achieved ROI is calculated as the ratio of money saved arising from initial and improved state cost of repairs to the money spent on labor and materials. It is to be noted that this is a monthly ROI. Since there are about 12 systems completed per month, and about 150 systems completed per year, the annual ROI comes out to be 10259%. The calculations are shown below.

𝑅𝑂𝐼!"#$!!" =$ 4193.55(!"!#!$% !"#$ !" !"#$%!) − $ 48.55(!"#$%&'( !"#"$ !"#$ !" !"#$%!)

16 𝑠𝑡𝑢𝑑𝑒𝑛𝑡 𝑑𝑒𝑠𝑖𝑔𝑛 ℎ𝑜𝑢𝑟𝑠 $30.00 𝑝𝑒𝑟 ℎ𝑜𝑢𝑟 + ($25.00 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝑐𝑜𝑠𝑡)

𝑅𝑂𝐼!"#$!!" = $4144.70$505 = 8.21 = 821%

𝑅𝑂𝐼!"#$%! = $4144.7012 𝑠𝑦𝑠𝑡𝑒𝑚𝑠 ∗ 150 𝑠𝑦𝑠𝑡𝑒𝑚𝑠

$505 = 102.59 = 10259%

5.2 Sustain and Control Plan

The improvements set in motion were sustained largely by acquiring a commercial printer system to be able to regularly print out Operator Log Sheets and SOS documents when needed. Each workstation got a folder to document the Operator Log Sheets that were regularly replaced each week. This helped ensure the opportunity to back track if ever needed. The magnetic board was constantly kept in professional format and updated to be identical to the SOS documents if/when updated.

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5.3 Steps for Ongoing Improvements

To ensure ongoing improvement, the SOS document will be monitored regularly to add or delete steps, as the process requires. Standardized training will be maintained and workers who show excellent results will be encouraged to take the positions of training to boost morale. The large magnetic board will be regularly maintained to ensure it covers all the instructions thoroughly for the workers. Following the Operator Log Sheets, workers who are highlighted to be in need of additional training will receive more in depth and hands on help during the training sessions. A schematic of an Operator Log Sheet is shown below:

5.4 Identify and Record Other Improvement Opportunities Discovered

Other opportunities of improvement were identified in the form of… 1. Monitor the factory air supply to pneumatic crimping tools to maintain proper crimps 2. Inspect and service all hand tools and equipment before use. 3. Train and certify multiple inspectors to handle workloads and vacations/time off.

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LSS Maccor - Green Belt Project