POLAROID

A Case on Process Control

Presented by:Anish KirolikarJaspreet KaurNrupen KamtekarPrakhar NigamUttam Kumar Das

Introduction

Polaroid - manufactures cameras, photographic equipment and accessories

Two main divisions – The Technical and Industrial Division The Consumer Photograph Division

The company produced two main types of films: The peel apart film The integral film (R2 facility)

Factory Layout

1st floor Spring Production

Stamped out of sheet metal in a ten-staged metal pressing operation.

Pod Production Designed such that reagent would be released from the

front seal of the pod. Flows between negative and positive to develop image. Operates when film frame are ejected from the camera.

Packaging

2nd and 3rd floor Cartridge Production and Assembly

Quality and Process Control at R2

All films that were produced were checked by the Quality Control department Sampled 15 finished cartridges out of every lot of 5000. These finished cartridges each contained 10 frames

If defects were found, Lot was put on hold for further testing Either reworked or rejected

Subsequent lots were subjected to more intense and rigorous testing Also increased the sample size on these lots

Quality and Process Control at R2

The QC department ended up rejecting just over 1% (50 cartridges per 5,000) in 1984

Used to check 32 random cartridges for each lot at each stage of the process

Took specific measurements of different characteristics

Measurement combined with the operators knowledge were used to determine if a lot should be rejected or not

Problems with existing quality control

Testing of the cartridges was very destructive and expensive Resulted in sample crap - $1.28 mn Rejected lots – additional $2 mn

Handling of the cartridges increased the chances of developing defects Passed – repackaged – handled again – increased defects

Use of a perfect testing camera – did not match customers cameras

Sampling did nothing to improve quality Lead to high costs If the control sampling was cut in half, the outgoing defects

would be 0.03% of production.

Project Greenlight

Focused on improving the quality control processes while reducing the number of samples

3 parts : Adoption of statistical process control principles

Production operators would be given the process control tools that the process

engineering technicians had been using expected to make disposition decisions themselves

Quality control auditors concentrate on training operators operationalizing specifications on new products

Project Greenlight

Operators responsible for taking measurements and recording them

If the machine was operating outside of the control limits, the machine was automatically shut down

Maintenance would be called and the machines had to be cleaned, re-calibrated and restarted

Operators were no longer allowed to “tweak” the machines.

Results – Project Greenlight

Mixed results Was supposed to cut down the defects and testing

losses. The reported defective rate from the operators and

auditors was reduced from 1% to 0.05% The defective rate from the central process auditors had

shot up from 1% to 10%

Lack of trust between the auditors and the operators

The nature of defects also changed The variability in the kinds of defects detected increased

Integral Film Production

+ Film Frame

Negative Positive

+ Thick long strips were

placed between the

two+ Craft

Material at the top

+ Lamination

+ Top Cover Sheet

+ Pod

+ Spring

+ Battery

Plastic Box

Plastic Box Subassembl

y

+ End Caps

Cartridge

Process Quality & Capability level

At 1% defect level 10000 DPMO. process would be under 4.1 sigma level.

ZL = (µ-LSL)/sigma = 2.33

Process Capability Cpk = Min(ZL,ZU)/3

Cpk = 2.33/3 = 0.7766

Going beyond 1%

Customer specification limit would remain same, thus to increase Cpk process variation must be reduced as much as possible.

This means making process more robust and decreasing defects.

Try to achieve 6sigma level 3.4 DPMO

Control Chart for Pod WeightSample 1 2 3 4 5 6

2.792 2.81 2.777 2.799 2.803 2.788

2.774 2.783 2.799 2.82 2.812 2.807

2.797 2.79 2.785 2.795 2.866 2.826

2.819 2.787 2.809 2.862 2.823 2.816

2.754 2.793 2.82 2.846 2.823 2.807

2.784 2.781 2.733 2.801 2.823 2.844

2.844 2.799 2.781 2.802 2.82 2.813

2.806 2.786 2.836 2.815 2.836 2.808

2.843 2.766 2.795 2.778 2.835 2.783

2.816 2.79 2.823 2.802 2.78 2.804

Range 0.09 0.044 0.103 0.084 0.086 0.025 CL 2.805SD 0.025

UCL 2.879LCL 2.730

Chart

2.720

2.740

2.760

2.780

2.800

2.820

2.840

2.860

2.880

Mean UCL LCL Max Min

Analysis

Acc to chart, Performance variability is within the control limit Hence process is in control

Reducing quality control expense Wastage of checked samples Reworking costs of tested samples Increase in defects due to reworking process

No reliance on operator’s individual judgement Standardized procedures and settings to be

followed

Control Chart for Finger Height

Sample 1 2 3 4 5 6

2.021 2.158 2.049 1.959 2.107 1.875

1.836 2.256 2.099 2.269 2.193 2.193

2.004 2.166 1.955 2.125 1.988 2.009

2.177 2.171 2.068 2.143 1.979 2.278

2.167 2.032 2.032 1.955 2.018 2.007

2.016 2.108 2.105 2.037 1.957 1.881

1.939 2.302 2.019 2.154 2.104 1.83

2.179 2.189 1.97 2.067 2.088 1.903

1.962 2.128 1.976 2.228 2.036 1.949

2.26 1.99 1.863 2.183 2.02 1.889

Range 0.424 0.312 0.15 0.314 0.236 0.448 CL 2.060SD 0.118

UCL 2.415LCL 1.705

Chart

1.600

1.700

1.800

1.900

2.000

2.100

2.200

2.300

2.400

2.500

2.600

Mean UCL LCL Max Min

1.500

1.700

1.900

2.100

2.300

2.500

2.700

Mean UCL LCL Min Max

Finger Height Control Chart – Shift A

Most of the data in shift A exceeds UCL

1.7001.8001.9002.0002.1002.2002.3002.400

Mean UCL LCL Min Max

Finger Height Control Chart – Shift B

Most of the data in shift B is within the control range

Finger Height Control Chart – Shift C

1.500

1.700

1.900

2.100

2.300

2.500

Mean UCL LCL Min Max

Most of the data in shift C is below the LCL

Types of defects

Acc to auditor, excess reagent defect Prior to Greenlight – 10% Post implementation of Greenlight – 22%

Leads to increased rejection rates of non-defective product as well

Customer is unaware of the existence of such a defect Higher wastage and losses

DefectsType of defect Operator

DefectsAuditor Defects

Cumulative Defects

% of Cumu. Defect

Excess reagent 4 11 15 21%

Frame feed failure 2 9 11 15%

Dirt from assembly 0 5 6 8%

Insufficient reagent 1 4 5 7%

Damaged spring 3 3 5 7%

Double feed 2 3 5 7%

Malformed box 1 3 5 7%

Misalignment on assembly 1 3 5 7%

Positive Sheet defect 2 3 4 6%

Excess flash on box 2 2 4 6%

Marginal lamination 1 2 4 6%

Negative Sheet defect 3 2 3 4%

Grand Total 22 50 72 100%

Analysis of DefectsE

xcess r

ea

ge

nt

Fra

me

fe

ed

fa

ilu

re

Da

ma

ge

d s

pri

ng

Dir

t fr

om

asse

mb

ly

Insu

fficie

nt

rea

ge

nt

Do

ub

le f

ee

d

Po

sit

ive

Sh

ee

t d

e..

.

Ne

ga

tive S

hee

t d

...

Ma

lfo

rme

d b

ox

Mis

ali

gn

me

nt

on

...

Excess fl

ash

on

bo

x

Ma

rgin

al

lam

ina

...

0%

5%

10%

15%

20%

25%

21%

15%

8% 7% 7% 7% 7% 7% 6% 6% 6% 4%

% of Cumulative Defects

Inferences

Improper proportion of reagent is one of the frequently occurring defects

The top 5 defects constitute around 60% defect

Almost 60% defects is produced by the operator himself

FMEA: Risk Priority NumberExce

ss R

egent

Fra

me f

eed f

ailure

Posit

ive S

heet

Defe

ct

Negati

ve s

heet

defe

ct

Dam

aged S

pri

ng

Mis

alignm

ent

on a

ssem

bly

Dir

t fr

om

assem

bly

Double

feed

Malf

orm

ed b

ox

Exce

ss fl

ash o

n b

ox

Insuffi

cient

Regent

Marg

inal Lam

inati

on

0

50

100

150

200

250

224196

175 175 168140 125 120

96 9675 72

Shingo’s Transformational Process

Comparison

Operator can change the speed temperature, and pressure of the machine

Maintenance has unique set of solution with each machine

Operators produce consistent quality by tweaking based on experience

Operators have to shut down the machine

Maintenance has to determine standardized best practice procedure for all the machine

Processes have been standardized

OLD Process Greenlight Process

Change Management

Change management approach to transitioning individuals, teams, and organizations to a desired future state with the help of new process/changes

Polaroid – Operator based statistical process control (SPC) Cut Costs Superior product quality

Implications

Hard to change the concept of work mapped in the head of the workers Operator thinks that shutting down the

machine is more expensive Maintenance thinks their job is depersonalized QC personnel do not trust the operators Engineering technicians feels that they are

better than the operators Increase in defect rate from central process

auditors – (1% to 10%)

Quality Circles

Quality circle - Polaroid O’Leary Murray Bud Rolfs

Recommendations

1. Operator responsibility: Need to treat downstream process as a customer Should aim at ‘Doing it Right the first time’ Should shut machine as soon as process is ‘out-

of-control’

2. Management responsibility: Convince operators about potential impact of

their process on overall quality Introduce SPC training modules for Operators Inculcate a culture of Quality in the Organization

at all levels

Recommendations

3. Process Control measures: Apply SPC at the injection moulding machines to

control defects Automation of data collection methods Process Documentation & Product Quality Rating

should be introduced Schedule maintenance periodically – Use checklist

4. Change Management: Motivate operators towards achieving Polaroid’s

Quality objective Link incentives to operator’s salaries to ensure

adherence to revised Quality norms

Thank You!!!

Who did what

Jaspreet – Intro+ Control Charts Anish – Change management & quality

circle Nrupen – Process Quality and capability,

Assembly chart Prakhar – Fema, Transformational

process, Recommendations Uttam – Fema, Deftects analysis, Putting

together presentation, Recommendations