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 Sales Gains Improved Response Higher Prices Improved Reputation Improved Quality Increased Profits 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 Act Plan Check Do

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 Central Limit Theorem As sample size gets large enough, sampling distribution becomes almost normal regardless of population distribution. Central Limit Theorem

Population and Sampling Distribution Uniform Normal Beta Distribution of sample means Standard deviation of the sample means (mean) Three population distributions

Central Limit Theorem Sampling distribution of the means Process distribution of the sample

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

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 Size Weight, 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) =

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 =

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

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 =

P-Chart Continued UCL = LCL =

C-Chart Controls number of defects per unit of output Average count c-bar UCL = LCL =

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

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%)

Probability of Acceptance OC Curve  = 0.05 producer’s risk for AQL = 0.10 Consumer’s risk for LTPD Probability of Acceptance Percent Defective Bad lots Indifference zone Good lots LTPD AQL 0 1 2 3 4 5 6 7 8 100 95 75 50 25 10

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