Managing Quality

Chapter Objectives Be able to: Discuss the various definitions and dimensions of quality and why quality is important to operations and supply chains. Describe the different costs of quality, including internal and external failure, appraisal, and prevention costs. Know what TQM is, along with its seven core principles. Calculate process capability ratios and indices and set up control charts for monitoring continuous variables and attributes. Describe the key issues associated with acceptance sampling, as well as the use of OC curves. Distinguish between Taguchi’s quality loss function and the traditional view of quality.

Managing Quality Quality defined Total cost of quality Total quality management (TQM) Statistical quality control Managing quality across the supply chain.

Definitions of Quality ASQ: The characteristics of a product or service that bear on its ability to satisfy stated or implied needs Fitness for use (value perspective) Free from defects (conformance perspective) How would you evaluate the quality of the following? Software package Hand-held vacuum cleaner No-frills air flight

Quality as a Competitive Advantage Strategic Quality Quality as a Competitive Advantage

Dimensions of Quality Performance Features Reliability Durability Conformance Aesthetics Serviceability Perceived Quality Which dimensions do you think are directly affected by Operations and Supply Chain activities?

Quality Dimension Examples New Car Tax Preparation Performance Tow capability; gas mileage Cost and time to prepare taxes Features Accessories; extended warranty Advance on refund check; E- filing Reliability Miles between required service Not applicable Durability Expected useful life of the engine, transmission, body Conformance Number of defects in the car Number of mistakes on the tax return Aesthetics Styling, interior appearance Neatness of the return Serviceability Qualified mechanics in the area? Maintenance time and cost? Will the tax preparation firm talk with the IRS in case of an audit? Perceived Quality How do prices for used vehicles hold up? What is the reputation of the firm?

Defensive Quality Quality analyzed in economic terms Total Cost of Quality: Failure Costs Appraisal Costs Prevention Costs

Total Cost of Quality — Traditional View Note: Horizontal axis is reversed from that used in the text to reflect that the goal is zero defects.

The total costs of quality fall as defect levels decrease Zero Defects View The total costs of quality fall as defect levels decrease Note: Horizontal axis is reversed from that used in the text to reflect that the goal is zero defects.

Total Quality Management (TQM) Managing the entire organization so that it excels in all dimensions important to the customer.  Product development  Marketing  Operations  Supply chain  Support services

TQM Principles Customer focus Leadership involvement Continuous improvement Employee empowerment Quality assurance (including SQC or SPC) Strategic partnerships Strategic quality plan SQC = Statistical Quality Control SPC = Statistical Process Control

TQM Principles Expanded Customer focus Each employee has a customer whether internal or external to the company Leadership involvement Must be ‘top’ down, throughout the company If not, major cause of TQM failures Continuous improvement Supports other core principles

Continuous Improvement (CI) versus “Leaps” Forward Performance Time

TQM Principles Expanded Employee empowerment Key to success Lack of empowerment major cause of TQM/SPC failures Quality assurance Quality Function Deployment (QFD) discussed in Chapter 6 Statistical quality control (SQC), also called statistical process control (SPC) Acceptance sampling (OC curve) SQC = Statistical Quality Control SPC = Statistical Process Control

TQM to Quality Assurance “Did we do it right?” Switching Focus . . . TQM to Quality Assurance “Did we do it right?”

We Noted That Organizations Must ... Understand which quality dimensions are important Develop products and services that will meet users’ quality needs Put in place business processes capable of meeting these needs Verify that business processes are meeting the specifications

Six Sigma Methodology Core value is having less than 3.4 defects per million opportunities (DPMO). Key elements are: Understanding and managing customer requirements Aligning key business processes to achieve those requirements Using rigorous data analysis to understand and ultimately minimize variation in those processes Driving rapid and sustainable improvement to business processes. The core value defect level is based on allowing the mean of a process to drift to within about 4.5 standard deviations of either specification limit. A true six sigma variation around a mean centered within specifications would correspond to a defect level of 2 parts per billion.

Six Sigma Methodology Two basic Six Sigma processes are: DMAIC (Define-Measure-Analyze-Improve-Control) — an updated version of the PDCA process promoted by Deming. DMADV (Define-Measure-Analyze-Design-Verify) DMAIC is an updated version of the PDSA (Plan-Do-Study-Act) process developed by Walter Shewhart and later promoted by W. Edwards Deming as the PDCA (Plan-Do-Check-Act) process for improvement

The PDCA Cycle Do Plan Check Act

Common Improvement Tools Cause and effect diagrams (aka “Fishbone” or Ishikawa diagrams) Check sheets Pareto analysis Run charts and scatter plots Bar graphs Histograms

Flight delays at Midway A Services Example Flight delays at Midway Cause and Effect Diagrams Check Sheets Pareto Analysis

Problem: Delayed Flights No one is sure why, but plenty of opinions “Management by Fact” CI Tools we will use: Fishbone diagram Check sheets Pareto analysis

Cause and Effect Diagram ASKS: What are the possible causes? Root cause analysis — open and narrow phases

Generic C&E Diagram

Midway C&E diagram

Check Sheets Event: Day 1 Day 2 Day 3 Late arrival II I Gate occupied Too few agents Accepting late passengers III (root cause analysis -- closed phase)

Pareto Analysis (sorted histogram) Late passengers 100 Late arrivals Late baggage to aircraft 85 70 Weather 65 Other (160)

Percent of each out of 480 total incidents ... Late passengers 21% Late arrivals 18% Late baggage to aircraft 15% Weather 14% Other 33%

Run Charts and Scatter Plots Measure Run Time Variable Y Scatter Variable X

Histograms Frequency Measurements

Process Capability Answers the Question: Can the process provide acceptable quality consistently?

Process Capability Ratio (Cp) Upper Tolerance Limit – Lower Tolerance Limit 6σ Where σ is the estimated standard deviation for the individual observations Upper and lower tolerance limits are also called the upper and lower specification limits (USL and LSL).

Process Capability ratio of 1 (99.7% within tolerance range) Shown Graphically: Process Capability ratio of 1 (99.7% within tolerance range)

“Six Sigma Quality” When a process operates with 6σ variation centered between the tolerance limits, only 2 parts out of a billion will be unacceptable.

Process Capability Index (Cpk) Used when the process is not precisely centered between the tolerance limits.

Discovering “problems” Inspect every item Expensive to do Testing can be destructive, should be simply unnecessary Statistical techniques Statistical process control (SPC) Acceptance Sampling

Statistical Process Control “Representative” samples are measured good, but not perfect, picture of process Sampling by Variable (continuous values — length, weight, area, volume, etc.) Sampling by Attribute (good, bad, # defects/unit, %) Variable: a continuous range of possible values (length, width, weight, temperature, pressure…..) Attribute: Limited range of discrete values (defects per unit, yield per lot, failures per sample group, errors per order…)

Example: Fabric Dyeing Rolls of fabric go through dyeing process Target temperature of 140 degrees Too low . . . ? Too high . . . ? Temperature must be “monitored” and action taken when something is “unusual” Is temperature a “variable” or an “attribute”?

Step 1: Sampling the Process Observation Sample 1 2 3 4 5 136 137 144 141 138 143 140 139 135 6 142 7 8 9 10 Things should be working OK when we do this . . . Table 4.5 on page 100 in the text. Note: There is an error in Table 4.6 for observation 5 on day 9, should be 139. Emphasize that when data is collected to determine possible control limits for the process, the process should be executed as consistently as possible to eliminate all but the natural variations (i.e., no intentional adjustments to improve results!) Discuss importance of doing everything as consistently as possible during the collection of data to establish control limits so that only natural causes of variation are present.

Step 2: Calculate the Mean and Range for Each Sample X R 1 139.2 8 2 140 5 3 139.4 9 4 6 7 141.4 139 140.2 10 139.6 X = 139.8° R = 5.3°

Step 3A: Use These Values to Set Up X and R charts Upper control limit for X chart: UCLX = X + A2 × R = 142.9 Lower control limit for X chart: LCLX = X – A2 × R = 136.7 Since there are five observations (n) in each sample, the value for A2 from Table 4.5 in the text is 0.58

Step 3B: Use These Values to Set Up X and R charts (cont’d) Upper control limit for R chart: UCLR = D4 × R = 11.2 Lower control limit for R chart: LCLR = D3 × R = 0 D3 and D4 values taken from Table 4.5 on page 94 of the text where the sample size n = 5.

Use the Charts to Plot the Following Data . . . Sample X R 11 141.2 8 12 142 9 13 144 14 140 5 15 139.6 4 16 140.8 Out of Control Sample

What is the process capability ratio for our dyeing example? What conclusions can you draw? σ = 2.41 from sample data

What would need to be for us to have “” quality ? 12σ = UTL – LTL = 148 – 132 σ = 16/12 = 1.33

Sampling by Attribute p = (8 late)/(50 deliveries) = 0.16 Gonzo Pizza is interested in tracking the proportion (%) of late deliveries Like before, you take several samples of say, 50 observations each when things are “typical” For each sample, you calculate the proportion of late deliveries and call this value p. For example: Example 4.9 in text on pages 103-104 Discuss difference between “typical” (natural causes presents) and abnormal (assignable causes present in addition to natural causes). p = (8 late)/(50 deliveries) = 0.16

Gonzo Pizza (cont’d) For all samples, calculate the average p: 0.16 0.20 0.00 0.14 0.10 p = 0.10

Gonzo Pizza (cont’d) Calculate standard deviation for the p-chart as follows: Where n = size of each sample = 50

Gonzo Pizza (cont’d) And the control limits are: UCLp = p + z × Sp = 0.226 LCLp = p – z × Sp = – 0.026, or zero Here z is 3, but can be chosen as other values to increase the sensitivity of the chart to changes in the process.

Gonzo Pizza Although text says to go ahead with control charts, consider that it is probably too early to develop them since the process is not yet in control (i.e., late deliveries are too high a percentage at present). A more practical approach would be: First, fix the more obvious problem(s) Then take new samples Then put in place control charts

Acceptance Sampling Some definitions Acceptable quality level (AQL) Maximum defect level for 100% customer acceptance Lot tolerance percent defective (LTPD) Highest defect level customer will tolerate Consumer’s risk,  Probability of accepting a bad lot Producer’s risk,  Probability of rejecting a good lot Operating characteristics (OC) curve Probability of accepting a lot given the actual fraction defective in the entire lot and the sampling plan being used.

Putting the terms together OC Curve Figure 4.10, page 106 in the text.

The Big Picture So how do TQM, continuous improvement, and all these statistical techniques “fit” together?

3 Lines of Defense PREVENT defects from occurring TQM and continuous improvement DISCOVER problems early Process control charts CATCH DEFECTS before used or shipped inspection / acceptance sampling

Traditional View of the Cost of Variability $ Low Spec High Target Cost of Bad Quality

Taguchi’s Quality Loss Function An alternative perspective on the cost of quality

Consider Big Bob’s Axles ... Axles have slightly larger or smaller diameter than target value ( Wheels have slightly larger or smaller holes than target value What are the possible outcomes?

Taguchi’s view of the cost of variability $ Low Spec High Target Cost of Bad Quality What are the managerial implications? (HINT: think continuous improvement)

TQM Principles Expanded Strategic partnerships Value of good suppliers and distributors i.e., GIGO (garbage in, garbage out) Quality consistent throughout supply chain Strategic quality plan ISO 9000 family of quality standards, www.iso.org American Society for Quality, www.asq.org

Managing Quality Case Study Dittenhoefer’s Fine China