Lecture 29 Total Quality Management Books Introduction to Materials Management, Sixth Edition, J. R. Tony Arnold, P.E., CFPIM, CIRM, Fleming College, Emeritus, Stephen N. Chapman, Ph.D., CFPIM, North Carolina State University, Lloyd M. Clive, P.E., CFPIM, Fleming College Operations Management for Competitive Advantage, 11th Edition, by Chase, Jacobs, and Aquilano, 2005, N.Y.: McGraw-Hill/Irwin. Operations Management, 11/E, Jay Heizer, Texas Lutheran University, Barry Render, Graduate School of Business, Rollins College, Prentice Hall

Objectives Why quality is important What is quality Dimensions of Quality Why improve quality Statistical quality control Understanding variations Statistical process control Process capability Three sigma vs. six sigma Process control Data types

Orlando Utilities Commission Maintenance of power generating plants Every year each plant is taken off-line for 1-3 weeks maintenance Every three years each plant is taken off-line for 6-8 weeks for complete overhaul and turbine inspection Each overhaul has 1,800 tasks and requires 72,000 labor hours OUC performs over 12,000 maintenance tasks each year

Orlando Utilities Commission Every day a plant is down costs OUC $110,000 Unexpected outages cost between $350,000 and $600,000 per day Preventive maintenance discovered a cracked rotor blade which could have destroyed a $27 million piece of equipment

Why Quality is Important Costs and market share Company’s reputation Product liability International implications

What is Quality? Conformance to requirements? Zero defects? Fitness for use? Consistency? “I can’t define it, but I know it when I see it”?

Garvin’s 8 Dimensions of Quality Dimension Meaning Performance Primary operating characteristics. Features Secondary operating characteristics, added touches. Reliability Extent of failure free operation. Durability Amount of use before replacement is preferable to repair. Consistency Uniformity around a target Serviceability Resolution of problems and complaints. Aesthetics Subjective characteristics that relate to senses. Perceived Quality Indirect measures or inferences: reputation.

What is Quality? Quality means user satisfaction: that goods and services satisfy the needs and expectations of the user. Arnold

Why Improve Quality?

Quality Chain Reaction

Two Ways to Improve Quality

Sources of Improvement

Statistical Quality Control:Process Definition Process: “A ‘process’ is any set of conditions, or set of causes, which work together to produce a given result. In its narrowest sense the term ‘process’ refers to the operation of a single cause. In its broadest sense it may refer to the operation of a very complicated ‘cause system.’ Reference: Statistical Quality Control Handbook, Western Electric

Statistical Quality Control Statistical: With the help of numbers or data Quality: we study the characteristics of our process Control: In order to make it behave the way we want it to behave. Reference: Statistical Quality Control Handbook, Western Electric

Understanding Variation Variation exists in everything Understanding variation is the key to improving quality Two Kinds of Variation Chance variation Assignable variation

Cause and Effect Diagrams Materials Methods Measurement Process Doc. Desired Effect or Undesired Effect PM Motivation Training Machines Manpower Environment

SPC - Assignable Causes The operational definition of assignable variation is variation that causes out-of-control points on a control chart.

Statistical Quality Control: Natural Patterns or Variations Natural patterns exhibit the following characteristics: Most of the points are near the centerline. A few points spread out and approach the control limits. None (or only on rare occasions) of the points exceeds the control limits. Reference: Statistical Quality Control Handbook, Western Electric

Statistical Quality Control Unnatural Patterns or Variations Unnatural patterns exhibit the following characteristics: Absence of points near the centerline produces a pattern known as a “mixture.” Absence of points near the control limits produces an unnatural pattern known as “stratification.” Presence of points outside of the control limits produces an unnatural pattern known as “instability.” Reference: Statistical Quality Control Handbook, Western Electric

Statistical Quality Control Tests for Unnatural Patterns Instability A single point falls outside of the 3 sigma control limits. Two out of three successive points fall in the outer one third of the control limits. Four out of five successive points fall in the outer two thirds of the control limits. Eight successive points fall on one side of the centerline. Systematic variable A long series of points are high, low, high, low without interruption. Reference: Statistical Quality Control Handbook, Western Electric

Statistical Process Control (SPC) Chance variations are the many sources of variation within a process that is in statistical control. They behave like a constant system of random chance causes. If only natural causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable.

Statistical Process Control (SPC) Assignable variation in a process can be traced to a specific reason. Machine wear Misadjusted equipment Fatigued or untrained workers If assignable causes of variation are present, the process output is not stable over time and is not predictable.

Statistical Process Control Why use averages? To create a normal distribution Averages are more sensitive to change than individuals

The Process (2 of 2) The distribution of a process’ output has a mean, , and a standard deviation, ; it can have a wide variety of shapes Process distribution Mean

Process Capability (1 of 3) When selecting a process to perform an operation, the inherent variability of process output should be compared to the range or tolerances allowed by the designer’s specifications

Process Capability (2 of 3) process distribution Lower Specification Upper Specification Much of the process output fits within specification width In other words, is the process capable of producing the item within specifications? Almost all of the process output fits within the specification width A significant portion of the process output falls outside of the specification width

Process Capability (3 of 3) The process capability index (cp) compares the design specifications with a measure of process variability

Three-Sigma Quality Lower Upper design specification 1350 ppm 1350 ppm mean

Three-Sigma Quality vs. Six-Sigma Quality Lower design specification Upper 1350 ppm 1350 ppm +3 Sigma -3 Sigma mean 1.7 ppm 1.7 ppm +6 Sigma -6 Sigma mean

Normal Distribution Standard deviation     Mean 95.5% 99.7% Standard deviation

Process Control (1 of 6) Once a process is in operation, a goal is to maintain the status quo, i.e., keep the process “in control” What can make the process no longer be in control, i.e., go “out of control”? The presence of an assignable cause The presence of an assignable cause may cause the process distribution to shift to the left or right, and/or increase the variability (flatten out)

Process Control (2 of 6) If the process mean shifts, more of output falls outside the specifications upper design specification Time lower design

Process Control (3 of 6) If the process mean shifts, more of output falls outside the specifications If process variance increases, more of the output falls outside of the specifications Time upper design specification lower design

Process Control (4 of 6) In either case, the process is considered to be out of control It should be stopped, investigated (the assignable cause found if present) and corrected (the process brought back to the status quo)

Process Control (5 of 6) Examples of assignable causes include operator raw material equipment environment

Process Control (6 of 6) How does management detect the presence of an assignable cause? Process output is monitored to detect any changes by inspecting the output of the process Inspection means assessing some characteristic of a unit of output

Central Limit Theorem Simulation The distribution of a sample approaches normal even when the parent population is not normally distributed.

Statistical Process Control Tolerance or specification limits Defined by an engineer Related to product design requirements Control limits Defined by the process Related to the variation in the process Unrelated to product needs

Much of the process output fits within specification width Lower Specification Upper Specification Process Capability Lower Specification Upper Specification Almost all of the process output fits within the specification width Lower Specification Upper Specification A significant portion of the process output falls outside of the specification width In other words, is the process capable of producing the item?

Type I Error Mean LCL UCL /2 Probability of Type I error

Types of Sampling Errors

Control Chart 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 UCL LCL Sample number Mean Out of control Normal variation due to chance Abnormal variation due to assignable sources

Observations from Sample Distribution UCL LCL 1 2 3 4 Sample number

Data Types Attribute - characteristic evaluated generates data that are counted (good or bad, yes or no). Variable - characteristic evaluated can be measured within a range of values

Mean and Range Charts Detects shift x-Chart Does not detect shift (process mean is shifting upward) Sampling Distribution UCL LCL x-Chart Detects shift UCL LCL Does not detect shift R-chart

Mean and Range Charts Does not reveal increase x-Chart R-chart Sampling Distribution (process variability is increasing) UCL LCL x-Chart UCL Does not reveal increase R-chart Reveals increase LCL

End of Lecture 29