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Introduction to Operations Management - IB MahadevanWeek 5

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Quality Management

Six Sigma Quality – An introduction



Six Sigma QualityAn introduction

• Generally six sigma quality points to very high quality levels that defects are a rarity in operations

• It also points to

– A disciplined way of handling issues in operations

– A structured way of addressing quality issues

– A trajectory to an unambiguous destination in the quality management journey in an organization



IntroductionAbout Six sigma quality• The moment we talk about quality, the word Six sigma comes to

our mind

• A number of progressive companies are working hard to build six sigma quality level– Motorola and GE are supposed to have pioneered this concept of 6

sigma

– Dabbawallahs of Mumbai has baffled the business world with their six sigma quality standard in their operations involving delivering 200,000 tiffin boxes from home to work place and again from work place back home every day



What is six sigma?

• A mechanisms to deliver near zero defect in operations using principles of process control

• A defect is an unacceptable state of a product or a service for a customer

• Defect becomes an extraordinarily a rare event– For example a few defects in a million potential opportunity in a

service

– One or two defective parts in a million that was produced in a manufacturing shop



Why near zero defects?

Source: Company Presentation, Own Research

CriterionBusiness

CustomersRetail

CustomersTotal

No. of policies issued during the year 247,010 2,520,874 2,767,884

Error Rate 0.50% 1.10% 1.05%

Defective Policies 1,235 27,730 28,965

This implies that at a nearly 99% quality level, 28,965 customers would have been unsatisfied with the service that they have received from the company during the year.



Superior Qualitycontrol

Fewer Disruptionsin Operations

SmootherOutput

Better Quality Management System

Fewer Rework

High qualityFinished goods

GreaterProductivity

Lessinventory

Less Indirect costs

Less inventory, labour, indirect costs & better quality

Why high levels of quality?



Quality ManagementChanging Perceptions…

It is often uneconomical to make quality improvements since it brings down productivity, increases cost and investment.

Productivity goes up and cost comesdown as quality goes up. This fact is

known, but not necessarily to everyone.

Yesterday…

Today…



Metrics for Quality ManagementPPM and DPMO

• If we want defects to really become an extraordinarily a rare event we can think of two measures:

– Manufacturing: Parts per million (PPM) defect rate

– Services: Defects per Million Opportunities (DPMO)

• Six sigma uses these two measures.



Defects Per Million Opportunities (DPMO)

• If in a process

– Number of opportunities for making a defect per unit of execution of that process = “k”

– Number of units of observation of the process = “n”

– Number of defects that occurred in that process during the observation = “d”

– DPMO then will be = 000,000,1**

nk

d



DPMO Computation ExampleA hotel in a tourist location

• Potential opportunities to make a defect in a check-in process = 11

• No. of guests handled during a season = 1,250

• Number of defects observed = 357

• 𝐷𝑃𝑀𝑂 =𝑑

𝑘∗𝑛∗ 1,000,000

=357

1,250∗11∗ 1,000,000 = 𝟐𝟓, 𝟗𝟔𝟑.



Premises of Quality Management

• Premise 1: All Quality initiatives must be continuous and data driven

• Premise 2: System of Quality is one of Prevention & Elimination

– Not Detection & Correction

• Premise 3: The Performance Standard is Zero Defects

• Premise 4: The responsibility for Quality lies primarily with those who produce & deliver products & services



Six Sigma Program

• A six sigma program requires certain enabling mechanisms for an organization–A structured program for quality management &

improvement

– Facilitating mechanisms for the Operations personnel to own, solve and obliterate the quality problems

–Organization structure and mandate for quality improvement issue on a continuous basis



DMAIC Methodology

Define Measure

Analyze

Improve

Control

Source: Mahadevan, B. (2015), “Operations Management: Theory & Practice”, Pearson Education, 3rd Edition, pp 575.



DMAIC Methodology • Define

– Define the problem, the requirements, project scope, project charter – Set goals for improvement

• Measure– Identify variables to be measured, the type of measurement – Data collection and synthesis

• Analyze– Develop a set of tools for analysis– Apply graphical tools of analysis– Identify possible sources of variation and “vital” few root causes– Explore means of eliminating them

• Improve– Generate & validate improvement alternatives

– Creating new process maps for the process• Control

– Develop control plan– Establish revised standard measures to maintain performance– Develop relevant training plans to maintain standards



Organization for six sigma

• In order that the organization sustainably improves the quality to near zero defect levels,

–A good organizational structure

–Mandate to make changes

–Ownership of processes and results and

–Continuous and closer review are required




• Process Owner

– Supervisor or a manager who takes responsibility for various steps of a process that delivers some output to the customer.

– It could be the in a particular work area where the improvement project has been identified

• Team Leader & Members

– Team leader (the project leader) and the members will comprise of the employees in the chosen work area

– They will have day-to-day operational control of activities




• In a six sigma organizational structure three terminologies are used to indicate these organizational entities.

• This includes Master Black Belt, Black Belt and Green Belt.

• The depth of training and experience differentiates these three.




• Six sigma coach

– A consultant or a senior person in the organization who offers expert knowledge on various aspects of six sigma.

– This includes statistical tools, process design & analysis, change management, small group improvement, use of QC tools for improvement etc.

• Sponsor

– A member of the senior management who oversees the overall progress and implementation

– Helps the team refine the project scope, sorts out issues cutting across other parts of the organization, approves projects and provides the necessary support in terms of resources



Quality Management

Total Quality Management



Quality GurusDeming’s contributions

• New perceptions to quality management

–Critical Role of Top Management

• Plan – Do – Check – Act (PDCA) Cycle

• 14 point agenda for quality improvement

• Considered father of Japanese Quality Management Systems

–Highest Award in Japan named after him



Juran’s Quality Trilogy

• Quality planning: the process of preparing to meet quality goals

• Quality control: the process of making quality goals during operations; importance of using statistical methods

• Quality improvement: the process of breaking through to unprecedented levels of performance



Philip CrosbyAbsolutes of Quality

• I Absolute: Definition of quality is conformance to standards

• II Absolute: The system of Quality is prevention

• III Absolute: The performance standard is zero defects

• IV Absolute: Measurement of Quality is the price of non-conformance

• V Absolute: There is no such thing as Quality Problem



Other quality gurus

• Karou Ishikawa

–Cause & Effect (Fishbone) Diagram

–Cause & Effect Diagram with Action Card (CEDAC)

• Shigeo Shingo

–Poka Yoke

• Genichi Taguchi

– Loss function

–Design of experiments



Quality Revolution in the 1980’sSalient features

• Alternative ideas about what constitutes good quality

• Newer methods to build quality into products and services that we offer

• New tools to assess performance of an organization with respect to quality

• Changed roles of middle managers and supervisors from one of control to facilitation of the process of building quality into the products and services



Total Quality Management (TQM)

• The definition points to four critical aspects of any good TQM program

– Role of Top Management

– Employee Involvement & Training

– Use of Tools & Techniques

– Development of a good quality system



Total Quality ManagementElements

QualitySystem

Role ofTop Management

Tools & TechniquesEmployee InvolvementTraining & Team Work



Role of Top Management

• Total in TQM refers to “every one”, every where” and “every time”. This will be possible only when the Top Management gets actively involved in this process

• Possible roles for Top Management

– Lead from the front by example

– Signal the importance of quality for the organization

– Help Middle Management resolve difficult trade-offs by providing guidance & directions



Employee Involvement

• It is about creating certain structures, culture and practices to make employee involvement a reality

– Build a culture of process ownership – facilitate this process

– Role of middle management and experts go through some change

– Provide training on some tools & techniques that people can use in their work place to address quality issues

– Build a climate and culture for team working

– Put in a system of project by project continuous improvement



Elements of a Quality Assurance System

•Understand customer needs•Translate them to meaningful measures for the operating system

Mechanisms for identifying quality

problems

Tools & techniques for the employees

•For tracking problems to their root causes•Identifying corrective measures

Methods for preventing

recurrence of problems

Documentation of all quality related initiatives for

continuous learning & improvement

Employee involvement for continuous focus

on quality improvement

Quality Certifications & Benchmarking

exercises

Top Management Commitment to

Quality

QualityAssurance

System




Tools for Quality Management

• Available tools for Quality Management can be broadly categorized into two:

– Quality Management @ Operations

• Highlighting Problems

• Identifying Improvement Opportunities

• Analyzing problems & their root causes

– Quality Planning & Design

• Building Quality into Products & Services

• Strategic Planning



Quality Management ToolsPurpose of Use Quality Control Quality Management

Highlighting Problems • Control Charts

Identifying ImprovementOpportunities

• Histograms• Check Sheets• Pareto Diagrams• Scatter Diagrams• Graphs

Analyzing problems & their root causes

• Cause & Effect (Fishbone) Diagram

• CEDAC

• Affinity Diagram• Relationship Diagram

Building Quality into Products & Services

• Tree Diagram• Matrix Diagram• Matrix Data Analysis• Process Decision Program Chart (PDPC)• Arrow Diagram• Poka Yoke (Fool Proofing)

Strategic Planning • Quality Function Deployment (QFD)• Quality Costing



HistogramCauses for adjustment snags

Number of occurrences

Leakage 25Missing 24Fouling 5Reworks 26Poor routing 5Loose fitting 15

25.0 24.0

5.0

26.0

5.0

15.0

LEAKAGE MISSING FOULING REWORKS POOR ROUTING

LOOSE FITTING

Nu

mb

er o

f o

ccu

ren

ces

Categories of problems

Causes for adjustment snags

05

05

10

15

20

25




Pareto Diagram

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

Reworks Leakage Missing Loose fitting Poor routing Fouling

Cu

mu

lati

ve o

ccu

ren

ces

(%)

Nu

mb

er o

f o

ccu

ren

ces


Adjustment Snags Analysis




Rework AnalysisCauses for rework

Number of occurrences

Lack of drawing clarity 23Tooling problems 15Process control issues 6Design issues 33Vendor related problems 23

0.010.020.030.040.050.060.070.080.090.0100.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Designissues

Lack ofdrawingclarity

Vendorrelated

problems

Toolingproblems

ProcessControlIssues

Cu

mu

lati

ve o

ccu

rren

ces

(%)

Nu

mb

er o

f o

ccu

ren

ces


Reworks Analysis

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Lack ofdrawingclarity

Toolingproblems

Processcontrolissues

Designissues

Vendorrelated

problems

Nu

mb

er o

f o

ccu

ren

ces


Causes for Rework



Cause and Effect DiagramA generic representation

Materials Work methods

EquipmentLabour

Quality

Cause Effect




Cause & Effect DiagramAn example




Cause Effect Diagram with Action Card (CEDAC) An example




Poka Yoke

• Poka Yoke, which means fool proofing is a technique which works on the basic premise that several defects that creep into an operation are indeed avoidable

• Further, Errors & Defects have a Cause & Effect relationship

• Poka Yoke ensures that a defect once detected can be eliminated once and for all by modifying the process or design of the product or service



POKA YOKE

An example



Matrix Diagram

• A two dimensional matrix to portray and analyze a problem at a strategic level

• Once the two dimensions are identified it lends itself to the analysis of the problem in a structured way

• A visual approach that helps management to identify problems and possible solutions



Matrix Diagram: An ExampleEarth Moving Equipment Manufacturer

Order

WinningQualifying

Less

Important

Bet

ter

Sam

eW

ors

t

Pe

rfo

rman

ce o

f th

e c

om

pan

y

Importance of the Attribute

A - Product costB - Product qualityC - Engg. QualityD - Enquiry lead timeE - Mfg. lead timeF - Delivery reliabilityG - Design flexibilityH - Delivery flexibilityI - Volume flexibilityJ - Service support




Quality Function Deployment (QFD)The four houses of quality

- -

- -

- - - -

Links

customer

needs to

design

attributes

Links

design

attributes to

actions firms

can take

Links

actions to

implement-

action

decisions

Links

implement-

action to

process

plans




House of Quality

7. Technical assessment & target values

1. Customerrequirements

4. Relationship matrix

3. Productcharacteristics

2. Importance

6. Benchmarks

5. Tradeoffs




House of Quality

An illustration

for a Restaurant

Correlation:

++: Strong Positive

+: Positive

+ -: Negative

-- --: Strong Negative

+

+ + +

Competitive Evaluation

X- Own Company

A - Competitor A

B - Competitor B

(5 is best)

1 2 3 4 5

Steaming hot 7 ++ ++ A B X

Enough space to sit & eat 4 - ++ ++ X A B

Less time during peak hours 6 - -- ++ + X B A

Easy to carry home 2 ++ A X B

Quick order processing 2 - -- + + X A B

7 6 9 4 6 4

5

4 X A,B A,B X

3 A X B X,B X,A,B

2 B X A A

1

Num

ber

of t

able

s

avai

labl

e

Mai

ntai

n cu

rren

t Le

vel

Red

uce

it by

10%

of

the

curr

ent

leve

l

Red

uce

time

to 2

min

utes

Mai

ntai

n cu

rren

t le

vel

Incr

ease

the

cou

nter

s by

one

Mai

ntai

n cu

rren

t le

vel

Importance Scale:

Strong: 9

Medium: 3

Small: 1

Tem

pera

ture

of

cook

ed

item

Tim

e ta

ken

to c

ook

the

food

Ord

er p

roce

ssin

g tim

e

Thi

ckne

ss o

f pa

ckin

g

mat

eria

l

Num

ber

of s

ervi

ce

coun

ters

in p

eak

time

Target Values

Technical Evaluation

(5 is best)

Importance Weighting

TechnicalCharacteristics

CustomerRequirements




Quality Management

Statistical Process Control (SPC) - Fundamentals



SPC – An Introduction

• Statistics is at the core of modern quality management

– Helps operationalize some decisions and keep performance and outcome with in limits

– Provides basic framework to systematically analyze the quality problem in various business processes

– A good mechanism to highlight either an existing quality problem or an impending problem



Variations in Business Processes

• Two types of variations occur in business processes; Common Causes & Assignable Causes

• Chance variations due to common causes

– causes due to random events that cannot be controlled

• Ambient temperature and humidity

• Normal wear and tear



Statistical Process Control

• Business processes always exhibit variations

– Filling a 500 gms detergent powder in a sachet

– Guest check-out time in a 5 star hotel

• SPC is a collective set of tools & techniques used to develop a quality assurance system that enables one to make meaningful sense of these variations



Assignable Causes

• Non-random variations due to assignable causes

– When observed variations are not statistically found to be due to random events, it clearly points to the existence of assignable causes

• Errors due to operator skill level differences

• Changes in the operating condition of an equipment

• Changes introduced in the standard operating procedure



Issues addressed thru SPC

• Key issues addressed in SPC based quality assurance system:

– How do we know whether the observed changes are due to random variations or assignable causes?

– How does one ensure that the random events are indeed rare events?



Quality Assurance using SPCSome terminologies: Designed Standard

• Centre of specification limits (Target)

• Upper Specification Limit (USL)• Lower Specification Limit (LSL)

• (USL – LSL): Desired tolerance

This represents the “Voice” of the Customer



Voice of the CustomerExamples

• Customer check-out time

in a 5 star Hotel: 90 ±20 𝑆𝑒𝑐𝑜𝑛𝑑𝑠

– Target = 90 seconds

– USL = 110 seconds

– LSL = 70 Seconds

– Desired tolerance is

70 – 110 Seconds

• Diameter of the pen manufactured: 8 ±0.5 𝑚𝑚

– Target = 8.0 millimeter

– USL = 8.5 millimeter

– LSL = 7.5 millimeter

– Desired tolerance is 7.5 – 8.5 millimeter



Quality Assurance using SPCSome terminologies: Status of process

• Centre of the process (Process Average)

• Upper Control Limit (UCL)• Lower Control Limit (LCL)

• (UCL – LCL): Spread of the process

This represents the “Voice” of the Process



SPC – Attribute to study

• At the outset the questions that we need to address are:

–What is the attribute in a process that needs to be measured for the purpose of quality control?

–How should we measure for the purpose of analysis?



Characteristics for process controlSome examples

Type of Applications Characteristic for Measurement

Manufacturing Number of defects in the product Conformance to test specifications Number of missing elements

Service Systems Number of defects in various business processes

Errors in processing documents Conformance to waiting time/lead time

related specifications




Choosing a characteristicExamples from service industry

• Time taken to complete the

– Settlement of claims in insurance

– Loan approval in a financial institution

–Patient admission process in a hospital

• Voice of the customer: 𝟐𝟎 ± 𝟔𝑴𝒊𝒏𝒖𝒕𝒆𝒔

–How to measure the quality performance in this case?



Methods of measuring defects

• Method A: Count the number of occasions patients were indeed admitted after 26 minutes as defects in the process.

– In 100 observations, let us say there were 7 occasions – this means the proportion of defects is 7%

• Method B: Make detailed measurements of the actual admission time in the 100 cases

– 24.95, 21.87, 25.45, 19.75 …

– Use this data and do analysis



Measurement MethodsAttribute Based

• Simple clustering of the characteristic into a few categories (such as good or bad)

• Measurements are easy to make, quick & less expensive

• Will reveal very little information about the process



Measurement MethodsVariable Based

• Detailed observation of the characteristic (such as length, diameter, weight, time)

• This is called variable based…

• Measurement will be expensive and more time consuming

• Will provide a wealth of information about the process



Types of Charts

• For attribute based measures we have

– p chart

– C chart

• For variable based measures we have

– R Chart

• Before we see the specifics of each of these let us get to know the process of setting up a control chart



Logic of Charts

• We use certain well known statistical principles pertaining to a random process

– The mean (which is the measure of central tendency)

– The Standard Deviation (which is a measure of dispersion)

– In a Normal Distribution, the area covered within ± 𝟑 𝒔𝒕𝒅. 𝒅𝒆𝒗 will be 99.73%



Normal Distribution



Logic of Charts

• What it means is that any variations happening in this range has a 99.73 probability that it is due to random events.

• Once we cross these limits the probability that the variation is due to random is so low that we begin to suspect there is an assignable cause

• This is an indication that the process may be out of control



Control ChartA generalized representation

Process Average

Upper Control Limit (UCL)

Lower Control Limit (LCL)

Plot of

sample data

Process in a state of “Statistical Control”

+𝟑𝝈

−𝟑𝝈




Control ChartA generalized representation

Process Average

Upper Control Limit (UCL)

Lower Control Limit (LCL)

Out of control

indication

Process not in a state of “Statistical Control”

+𝟑𝝈

−𝟑𝝈



Quality Management

Setting up a Control Chart



Setting up a process control system

Choose the characteristicfor process control

Choose the Measurement method

Choose the type ofControl Chart

Collect Data, Establish Control Limits

Plot the data & Analyse

• Attribute Based• Variable Based

• P chart, c chart• 𝑿𝑪𝒉𝒂𝒓𝒕, 𝑹 𝑪𝒉𝒂𝒓𝒕




• Step 1: Choose the measurement characteristic:

Diameter of a cylindrical component (cm)

• Step 2: Choose the measurement method

Actual measurement of diameter (variable based)

• Step 3: Choose the Control Chart:

• Step 4: Decide on a Sampling Plan

• Step 5: Collect Data & Establish Control Limits



Data for the chart

Sub-groups

Observations in each sub-group*

1 2 3 4 5

1 12.45 12.39 12.55 12.38 12.40

2 12.55 12.39 12.40 12.38 12.44

3 12.46 12.44 12.44 12.35 12.36

4 12.38 12.39 12.55 12.38 12.40

5 12.37 12.44 12.45 12.41 12.41

6 12.45 12.37 12.44 12.38 12.41

7 12.46 12.38 12.35 12.50 12.44

8 12.44 12.39 12.37 12.45 12.39

9 12.44 12.55 12.44 12.37 12.55

10 12.35 12.38 12.45 12.44 12.38

11 12.36 12.37 12.41 12.40 12.40

12 12.51 12.36 12.41 12.37 12.39

13 12.38 12.50 12.45 12.37 12.44

14 12.41 12.37 12.45 12.40 12.36

15 12.37 12.44 12.45 12.41 12.37

Sampling Plan

• Sample every 20 minutes• Each time take five

consecutive samples (Sample size is 5)

• Take 15 such samples

* All values in the table in centimeters




Extract the process parameters

Sub-groups

Observations in each sub-group Average 𝑿

Range (R)1 2 3 4 5

1 12.45 12.39 12.55 12.38 12.40 12.434 0.17

2 12.55 12.39 12.40 12.38 12.44 12.432 0.17

3 12.46 12.44 12.44 12.35 12.36 12.410 0.11

4 12.38 12.39 12.55 12.38 12.40 12.420 0.17

5 12.37 12.44 12.45 12.41 12.41 12.416 0.08

6 12.45 12.37 12.44 12.38 12.41 12.410 0.08

7 12.46 12.38 12.35 12.50 12.44 12.426 0.15

8 12.44 12.39 12.37 12.45 12.39 12.408 0.08

9 12.44 12.55 12.44 12.37 12.55 12.470 0.18

10 12.35 12.38 12.45 12.44 12.38 12.400 0.10

11 12.36 12.37 12.41 12.40 12.40 12.388 0.05

12 12.51 12.36 12.41 12.37 12.39 12.408 0.15

13 12.38 12.50 12.45 12.37 12.44 12.428 0.13

14 12.41 12.37 12.45 12.40 12.36 12.398 0.09

15 12.37 12.44 12.45 12.41 12.37 12.408 0.08

Average of all 15 observations 12.417 0.119





Establish Control Limits

Sample size (n)

A2 D3 D4

2 1.880 0 3.268

3 1.023 0 2.574

4 0.729 0 2.282

5 0.577 0 2.114

6 0.483 0 2.004

7 0.419 0.076 1.924

8 0.373 0.136 1.864

9 0.337 0.184 1.816

10 0.308 0.223 1.777

Table for selecting values for establishing the control limits for 𝑿 𝒂𝒏𝒅 𝑹 𝑪𝒉𝒂𝒓𝒕𝒔*

* Source: Juran, J.M. and F.M. Gryna, (1995), “Quality Planning and Analysis”, Tata McGraw-Hill, 3rd Edition, New Delhi, pp 385.




In our example,• A2 = 0.577; D3 = 0; D4 = 2.144

* All values in centimeters



𝑿𝑪𝒉𝒂𝒓𝒕

X-bar Chart

12.34

12.35

12.36

12.37

12.38

12.39

12.40

12.41

12.42

12.43

12.44

12.45

12.46

12.47

12.48

12.49

12.50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Sample Number

Mea

n D

iam

eter

(cm

s)

Sample Means Centre Line UCL LCL




R ChartAn example

R Chart

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Sample Number

Mea

n R

ange

(cm

s)

Sample Range Centre Line UCL LCL




P Chart

• Suppose the same cylinders are subjected to a much simpler testing of merely classifying them as defect

• When the cylinder is beyond the acceptable limits (too small or too big in diameter) it is classified as defect

• Sampling Plan is as follows:

– Sample 100 pieces every 30 minutes for testing

– Collect 12 such samples



𝐏 𝐂𝐡𝐚𝐫𝐭

• Step 1: Choose the measurement characteristic:– Diameter of a cylindrical component (cm)

• Step 2: Choose the measurement method– Classify as good or bad (attribute based)

• Step 3: Choose the Control Chart:

– 𝑷 𝑪𝒉𝒂𝒓𝒕

• Step 4: Decide on a Sampling Plan

• Step 5: Collect Data & Establish Control Limits



Data for the chart

Sampling Plan

• Sample every 30 minutes• Each time take 100

consecutive samples• Take 12 such samples


Sample no. Number of defects

1 10

2 9

3 8

4 11

5 7

6 12

7 7

8 10

9 13

10 12

11 13

12 14




Extract the process parameters


Sample no.Number of

defectsp

(%)

1 10 0.10

2 9 0.09

3 8 0.08

4 11 0.11

5 7 0.07

6 12 0.12

7 7 0.07

8 10 0.10

9 13 0.13

10 12 0.12

11 13 0.13

12 14 0.14

Average of all 12 observations 0.105



P Chart p Chart

0.00

0.03

0.06

0.09

0.12

0.15

0.18

0.21

1 2 3 4 5 6 7 8 9 10 11 12Sample No.

Pro

po

rtio

n o

f de

fect

sp Centre Line UCL LCL




C Charts

• Similar to p chart, Instead of proportion of defects, we merely count the number of defects

• Appropriate in certain situations

–Number of knots in a square meter of a cloth

–Number of scratches in a square meter of a smooth finished surface etc.



Computing the limits for C Chart



Quality Management

Using the Control Charts



Using the Control Charts

• There are two questions that comes to our mind when it comes to using the control charts:

– Is the process of out of control? What are we supposed to do in that case?

– Is there a way we can detect an impending out of control situation much earlier?



Process out of controlPoints outside the control limit

0

3

6

9

12

15

18

21

24

1 2 3 4 5 6 7 8 9 10

Num

ber

of

defe

cts

Sample No.

c Chart

c Centre Line UCL LCL




Steps to be taken

• Step 1: Remove the outlier and re-compute the control limits (revise the chart parameters)

• Step 2: Perform a detailed investigation to explore any assignable causes for the drift in the performance

• Step 3: If there are no assignable causes, resume the process with revised control parameters

• Step 4: If there are assignable causes implement countermeasures, and resume the process

• Step 5: Stabilize the process, re-establish control limits and ensure the process is in control



Early Detection of Problems

• The other question pertains to early detection of an impending problem

• Over several years, some useful rules have been created that helps operating personnel to detect a possible drift in the process



Zones A, B and C

Mean

Zone A Zone B Zone C




When to Stop the Process

• One point beyond Zone A

• Nine points in a row in Zone C or beyond

• Six points in a row, steadily increasing or decreasing

• Fourteen points in a row, alternating up & down

• Two out of three points in a row in Zone A or beyond

• Four out of five points in a row in Zone B and beyond

• Fifteen points in a row in Zone C



Predictive capability of processesWhich process is better?

• Process B is better than Process A

• Spread of a process is indicative of its capability

• Lesser the spread better is the process




Predictive capability of processesWhich process is better?

TargetLSL USL

Process B

Process A

Offset

• Process A is better than Process B

• A process that is aligned closer to the desired target is likely to be more capable




Process Capability

• Process Capability is defined by the spread of the process

• Potential capability (Cp) is defined as the ratio of the difference in specification limits to the process spread

Cp =

• Actual capability (Cpk) takes into consideration the extent to which the process has deviated from the desired target

Cpk =

6

)(

Pr

LSLUSL

Capabilityocess

RangeionSpecificat

3

Pr,

3

Pr CentreocessUSLLSLCentreocessMin



Process Capability & Defects

Process Capability Index (Cpk)

Total Products outside the specification limits

0.25 453,255 ppm

0.50 133,614 ppm

0.60 71,861 ppm

0.80 16,395 ppm

1.00 2,700 ppm

1.20 318 ppm

1.50 7 ppm

1.70 0.34 ppm

2.00 0.0018 ppm

Source: Quality Planning & Analysis, Juran & Gryna, Chapter 17, 3e



Six sigma Organization

• Cpk is a good measure to predict the defects coming out of a process

– It could be used to target improvements in the process

– Suppliers could be asked to submit their Cpk levels and it can be continuously monitored

• A six sigma organization is one which is able to achieve a Cpk

value of 2



Quality Management

Issues in Service Quality



Service Quality IssuesExample

• A flight that is supposed to take off at 7.30 pm is getting delayed.

• The airline customer relationship officer has kept the passengers in the dark about the delay.

• Further, upon mounting pressure announces a departure time which never happened.



Service Quality IssuesExample

• A week end program in a business school was a disaster as it was nowhere near the expectations of the participants

• A number of e-retailers in the US failed miserably during the Christmas season of 1999. They could not deliver the Christmas gift before Christmas.

• Instead they returned the money paid by the customers with a $ 50 add on to it and an apology note..



Intangibility

• Performances rather than objects, therefore precise specs. can be rarely set

• Cannot be counted, measured, inventoried, tested and verified in advance to assure quality

• Difficult to understand how consumers perceive & evaluate their services



Heterogeneity

• Performance vary from producer to producer, consumer to consumer, day to day

• Consistency of behavior from service personnel is difficult to assure

• What firms intend to deliver may be different from what the consumer receives



Simultaneity

• Not engineered in a plant and then delivered in tact to the consumer

• Quality occurs during service delivery while the consumer interacts with the service personnel

• Consumers’ input may be critical to quality

• The service firm may have less managerial control in real time



Service Quality Some considerations…

• Service quality is –A measure of how well the service delivered

matches with expectations

–Pre-dominantly is a function of perceptions of the

customers

(Example of the weekend course in a Business School)



Service Quality Some considerations…

• Quality evaluations are

–Not made solely on the outcome of the service

–They also involve evaluation of the process of delivery

• (Example of Airline Delays & the way it was handled)

• (Example of e-tailers inability to deliver Christmas Gifts)

• Points to difficulty in Service Recovery (after a failure)



Service QualityThe five gaps model

Gap 1

Gap 4

Gap 5

Gap 3

Gap 2

Source: Parasuraman, A., Zeithhaml, V.A. and Berry, L.L., (1985), “A conceptual model of service quality & its

implications for future research”, Journal of Marketing, 49 (4), 41 – 50.

Firm

Consumer

Expected Service

Perceived Service

Service Delivery

Translation of perceptions

into Service Qlty. Specs.

Management perceptions of

Consumer Expectations

External Communications

to Consumers



Gaps in Service QualityWhy do they occur?

• Gap 1: Service firm executives may not always understand

– What the consumer wants?

– What features a service must have?

– What levels of performance?

• Gap 2: Means to meet the expectations absent

– Knowledge of consumer expectations exist but not the perceived means to deliver

– Absence of management commitment to quality

• Gap 3: Variability in employee performance

• Gap 4: Problems arising out of communication

– Firms tend to promise more in communications than what they deliver in reality

– Firms tend to neglect to inform consumers of special efforts to assure quality that are not visible to consumers

• Gap 5 = f (Gap 1, Gap 2, Gap 3, Gap 4)



Service QualityConcluding Remarks…

• Service Quality is more challenging than product quality as it is a function of the perceptions of the customers

• Organizations can use the notion of gaps in service delivery to identify specific improvement opportunities in the service delivery process

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