Managing Quality. Introduction What: quality in operations management Where: Quality affects all...

Managing Quality

Introduction What: quality in operations

management Where: Quality affects all goods

and services Why: Customers demand quality

What is Quality High quality products Low quality products What does quality mean to you?

American Society for Quality “The totality of features and

characteristics of a product or service that bears on its ability to satisfy stated or implied needs”

User-Based Definition “Quality lies in the eye of the

beholder” Higher quality = better

performance Higher quality = nicer features

Manufacturing-Based Definition Quality = conforming to standards “Making it right the first time”

Product-Based Definition Quality = a measurable variable

Our Definition Quality: The ability of a product or

service to meet customer needs

Implications of Quality Company Reputation Product Liability Global Implications

Global Implications National Quality Awards: US: Malcolm Baldridge National

Quality Award Japan: Deming Prize Canada: National Quality Institute

Canada Awards for Excellence

Canada Award Winners 2000 Aeronautical and Technical

Services British Columbia Transplant

Society Delta Hotels Honeywell Water Controls Business

Unit

Quality and Strategy Differentiation Cost Leader Response

Quality and Profitability

Improved Quality Increased Profits

Sales Gains•Improved Response•Higher Prices•Improved Reputation

Reduced Costs•Increased Productivity•Lower Rework, Scrap•Lower Warranty Costs

Costs of Quality Prevention Costs Appraisal Costs Internal Failure External Costs

International Standards ISO 9000 Establish quality management

procedures Documented processes Work Instructions Record Keeping

Does NOT tell you how to make a product!

Total Quality Management TQM – Total Quality Management Quality emphasis throughout an

organization From suppliers through to

customers

W. Edwards Deming

Deming’s 14 Points Create consistency of purpose Lead to promote change Build quality into the product, stop

depending on inspections to catch problems Build long-term relationships based on

performance instead of awarding business on the basis of price

Continuously improve product, quality and service

Start training

Deming’s 14 Points Emphasize leadership Drive out fear Break down barriers between departments Stop haranguing workers Support, help and improve Remove barriers to pride in work Institute a vigorous program of education

and self-improvement Put everybody in the company to work on

transformation

TQM Concepts Continuous Improvement Employee Empowerment Benchmarking Just-In-Time Taguchi Knowledge of Tools

Continuous Improvement

Plan

Do

Check

Act

Continuous Improvement Kaizen Zero Defects Six Sigma

Employee Empowerment Involve employees in every step of

production High involvement by those who

understand the shortcomings of the system

Quality circle

Benchmarking Pick a standard or target to work

towards Compare your performance Best practices in the industry

Just-In-Time Produce or deliver goods just when

they are needed Low inventory on hand Keeps evidence of errors fresh

Taguchi Concepts Quality robustness Quality Loss Function Target-oriented Quality

TQM Tools Check Sheet Scatter Diagram Cause and effect diagram (fishbone) Pareto Chart – 80-20 Rule Flow Charts Histogram Statistical Process Control

Inspection Attribute Inspection Variable Inspection

Inspection At supplier’s plant Upon receipt of goods from supplier Before costly processes During production When production complete Before delivery At point of customer contact

Source Inspection Employees self-check their work Poka-yoke 

Statistical Process Control Apply statistical techniques to

ensure processes meet standards Natural variations Assignable variations Goal: signal when assignable

causes of a variation are present

Statistics Mean Standard deviation Natural variation Assignable variation

Taking Samples

Central Limit Theorem

X

As sample size gets large enough,

sampling distribution becomes almost normal regardless of population distribution.

Central Limit Theorem

XX

Population and Sampling Distribution

Uniform

Normal

BetaDistribution of sample means

x means sample of Mean

n

xx

Standard deviation of

the sample means

(mean)

x2 withinfall x all of 95.5%

x3 withinfall x all of 99.7%

x3 x2 x x x1 x2 x3

Three population distributions

Central Limit Theorem

Sampling distribution of the means

Process distribution of the sample

)mean(

mx

Central Limit Theorem Summary Mean Standard Deviation 95.5% within +/- 2σ 99.73% within +/- 3σ This means that, if a point on the

chart falls outside the limits, we are 99.73% sure that the process has changed

Central Limit Theorem Summary

Properties of normal distribution

x2 withinfall x lal of 95.5%

x3 withinfall x lal of 99.7%

x

x

In Control vs Out Of Control In control and producing within

control limits In control, but not producing within

control limits Out of control

In Control vs Out Of Control

Frequency

Lower control limit

SizeWeight, length, speed, etc.

Upper control limit

(b) In statistical control, but not capable of producing within control limits. A process in control (only natural causes of variation are present) but not capable of producing within the specified control limits; and

(c) Out of control. A process out of control having assignable causes of variation.

(a) In statistical control and capable of producing within control limits. A process with only natural causes of variation and capable of producing within the specified control limits.

Setting Limits Mean of samples means x bar Standard Deviation of process σ Standard Deviation of sample

means σx = Upper Control Limit (UCL) = Lower Control Limit (LCL) =

n

xzx xzx

Making X-Bar Control Charts Mean (x-bar) chart Standard Deviation is difficult to

calculate, so we calculate a Range R – the difference between the biggest and smallest values in the sample

Value of A2 from chart on page 204 UCL = LCL =

RAx 2RAx 2

Making R Control Charts Plot the range on the chart D3 and D4 from chart on page 204 UCL = LCL =

RD4

RD3

What X-Bar and R Charts Tell Us

Summary: Steps to Create Control Charts Collect 20 to 25 samples of n=4 or n=5

from a stable process and compute the mean and range for each sample

Compute overall means (X-bar and R-bar), UCL and LCL

Graph sample means and ranges on control charts

Investigate points that indicate process is out of control

Control Charts for Attributes So far we have been using control

charts for variables: size, length, weight

What about attributes: defective or not defective

We can measure percent defective – p-chart

We can measure count defective – c-chart

P-Chart p-bar = mean fraction defective in

the sample z = number of standard deviations

(2 or 3) σP = standard deviation of

sampling distribution = n

pp 1

P-Chart Continued UCL = LCL =

pzp

pzp

C-Chart Controls number of defects per

unit of output Average count c-bar UCL = LCL =

czc

czc

Patterns to Look For

Process Capability We need a summary measure to

tell us if the process is capable of producing within the design limts

population process the of deviation standard

mean process x where

3

Limit ionSpecificat Lower x

or , 3

x Limit ionSpecificat Upper of minimum

pkC

What does Cpk Tell Us?

Cpk = negative number

Cpk = zero

Cpk = between 0 and 1

Cpk = 1

Cpk > 1

Acceptance Sampling Used to control incoming lots of

purchased products Take random samples of batches (“lots”

of finished product More economical than 100% inspection Quality of sample used to judge quality

of all items in lot Rejected lots returned to supplier or

100% inspected

Operating Characteristic Curve Each party wants to avoid costly

mistake of rejecting a good lot Operating Characteristic (OC) curve

describes how well an acceptance plan discriminates between good and bad lots

Producer’s Risk α – Probability good lot rejected

Consumer’s Risk β – Probability bad lot accepted

Quality Levels Acceptable Quality Level (AQL) –

Poorest level of quality we are willing to accept (ie 20 defects per 1000 = 2%)

Lot Tolerance Percent Defective – Quality level of a lot that we consider bad – we reject lots of this or poorer quality (ie 70 defects per 1000 = 7%)

OC Curve

= 0.05 producer’s risk for AQL

= 0.10

Consumer’s risk for LTPD

Probability of Acceptance

Percent Defective

Bad lotsIndifference zoneGood lots

LTPDAQL

0 1 2 3 4 5 6 7 8

10095

75

50

25

10

0

Average Outgoing Quality (AOQ) Sampling plan replaces all defective

items encountered Determine true percent defective in lot

Pd = true percent defective of the lot

Pa = probability of accepting the lot

N = number of items in the lot

n = number of items in the sample

N

nNPPAOQ ad ))()((