managing quality. introduction what: quality in operations management where: quality affects all...
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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 ))()((