Fundamentals of Operations Mgmt Quality Sept. 18, 2012

1. Fundamentals of Operations MgmtQualitySept. 18, 2012

2. Eight Quality Dimensions - Products • Performance • Features • Reliability • Conformance • Durability • Serviceability • Aesthetics • Perceived Quality Can vary in importance based on the requirements, tastes, and expectations of the Customer. Can also vary across functional groups within an organization * Adapted from Foster, Quality Management, Fourth Edition, Prentice Hall

3. Five Quality Dimensions - Service • Tangibles • Reliability • Responsiveness • Assurance • Empathy Unlike product quality, with Service quality the Customer can be directly involved. Service quality can also be directly applicable to career performance * Adapted from Foster, Quality Management, Fourth Edition, Prentice Hall

4. Elements of a Quality Model Infrastructure Enablers Outcomes Quality tools Process improvement Leadership Teams Customer satisfaction Culture Communication Service / Product Quality Resources Training & Education Employee Satisfaction Information and Analysis Foster, Quality Management, Fourth Edition, Prentice Hall

5. Basic Assumptions The HIGHER the Quality, the higher the Customer Service, the lower the Inventory, and the lower the Cost The LOWER the Quality, the lower the Customer Service, the higher the Inventory, and the higher the Cost

6. Quality and Productivity • Productivity is a ratio of Inputs and Outputs. The greater the quality, the fewer inputs required and the greater the productivity • Statistical Analysis enables improved performance in both quality and productivity • Measure variability • Determine the impact of the variability • Trigger the reduction / elimination of the variability • Improvement projects sometimes reduce productivity in the early stages, before the longer term results are achieved • Learning curve • Resistance to change • Measure against objective standards • Yields • Cycle time • Inventory • Profit

7. Rework, Yield, and Scrap Rework • Steps completed before a defect is detected that must be redone. The item was not “done right the first time” • Can be performed at the original source or in a designated rework area • Impacts process capacity and can move the bottleneck from one operation to another (covered later in semester) Yield • The percentage of sellable or usable items that are produced by the process • Can also be represented by the formula (1 – scrap rate) Scrap • The item cannot be sold or used as a component and must be discarded • Can be detected during production process or at final inspection / test • Can also be represented by the formula (1 – yield)

8. Rework, Yield, and Scrap illustration

9. Quality Tools

10. Ishikawa’s Basic Seven (7) Tools of Quality • Process Maps • Check Sheets • Histograms • Scatter Plots • Control Charts • Cause & Effect (“Fishbone”) Diagrams • Pareto Analysis

11. Quality ToolsProcess Maps

12. High Level Supply Chain Process Map Practical Definition: A “snapshot” of the steps required to execute a business process Product Planning Demand Planning Master Production Scheduling Material Planning Production Delivery & Service

13. Detailed Process Map, including Functional Responsibilities

14. Steps in creating process maps • Communicate the purpose to impacted individuals • Observe behavior • Interview participants and stakeholdrrs • Review reports and outputs • Draw the flow chart using the symbols • Evaluate Value Added (VA) and Non Value Added (NVA) activities • Review multiple times to ensure accuracy and buy-in Use the map to streamline and simplify the process, including eliminating NVA steps Use the map for the Vision of the improved Future Process

15. Quality ToolsControl Charts

16. Separating Random variation from Non-Random variation Process Control provides data to isolate “Common Cause ” problems from “Assignable Cause” problems • Product Quality • Machine performance • Budgets • Forecasts • Body temperature • Traffic patterns Not every imperfect measurement or event triggers immediate Corrective Action

17. Generalized Procedure for Developing Control Charts • Identify critical operations where inspection might be needed • If the operation is performed improperly, the product will be negatively affected • Identify critical product characteristics that will result in either good or poor functioning of the product • Determine whether the product characteristic is variable or attribute • Select the appropriate Control Chart • Calculate the Control Limits and use the chart to continually monitor and improve • Update the limits when changes have been made to the process * Adapted from Foster, Quality Management, Fourth Edition, Prentice Hall

18. Control Charts: Variable & Attribute Data • Variables • Weight • Thickness • Height • Heat • Tensile strength • Attributes • Pass / Fail • Defects (Parts Per Million) * Adapted from Foster, Quality Management, Fourth Edition, Prentice Hall

19. All Dimensions of Product Quality have Attributes An attribute is an ESSENTIAL CHARACTERISTIC of something . Its presence can be answered with “Yes” or “No”

20. Monitoring Samples vs. Entire populations • Lower cost • Less time • Less disruptive • A practical alternative when destructive testing is required

21. Factors when selecting Sample Groups • Ensure that every piece has the same probability of being chosen to be sampled • Gather data at selected time intervals (e.g. every 1 minutes / hour / shift) • Gather data at selected Quantities produced (e.g. every 25th unit, 100th unit) • Understand significant inputs or regular events (e.g. Time of Day, Shift changes, preventative maintenenance)

22. Key Elements when implementing Process Monitoring • Type of Inspection • Population • Random • Which sub-groups • Which critical variables and attributes to be sampled • Size of samples • Who will perform the inspection • How will operators be trained to gather and report the data • Who will monitor and analyze the data

23. Example of a Control Chart

24. Interpreting Control Charts

25. Control Chart Summary Outputs Process Inputs • Establish Variable or Attribute Date • Define Key Characteristics to Measure • Confirm Normal Distribution • Choose Data Gathering Methodology • Train Users • Collect Data • Plot Data • Check for Randomness • Identify Randomness • Identify non-Randomness • Stop production if necessary • Discover improvement opportunities • Recalculate Control Limits

26. Quality ToolsPareto Analysis

27. Pareto’s Law – The “80/20” Rule The majority (80%) of problems are the result of relatively few (20%) causes Focus your improvement efforts on the few causes that make the BIGGEST DIFFERENCE to your business

28. Pareto Example: Gross Domestic Product by Country 16,000,000 14,000,000 12,000,000 10,000,000 8,000,000 6,000,000 4,000,000 2,000,000 0

29. Key Points regarding Quality Tools • Know if you are looking at time and trend data or at static / population data • Process Maps are static • Check sheets can be both • Histograms, Scatter Plots, Pareto Charts are for populations • Control Charts / SPC look at time and trend • Data gathering and analysis enables a more effective “Plan-Do-Check-Act” cycle • The data TELL you something! … Act on what the data tell you • Root Cause identification • Separating Causes from Effects (“5 Whys”) • Planning and Implementing improved performance and Results • Refine the Measurement System so that focus is on facts and collaborative problem solving – not blame or “spin” • Precision vs. Enough (Diminishing Returns)

30. WHEN a problem is detected impacts Cost (Figure 10.13) Process Step Bottleneck End of Process Market Defect Occurred Defect Detected Defect Detected Defect Detected Defect Detected $ $ Cost of Defect Recall, Reputation, Warranty Costs $ Based on Sales Price (including Margin) Based on Labor & Material Costs * From Cachon, Matching Supply with Demand, Third Edition, McGraw Hill Irwin

31. Process Capability andSix Sigma

32. Defects & Defective Units A Defect is a “Flaw” or “Imperfection” within a larger unit • There is Management judgment in identifying defects • Key characteristics are measured more precisely, using tolerances around specifications • Other characteristics can be imperfect but “good enough” • Defects are “Countable” • Sometimes one defect can make the unit “Defective” • Other times you can “live with” a small number of noncritical defects and still have a good unit • Examples of Defects • Lumps in Paint • Bad sectors in a Disc Drive • Misspelled word in a report

33. Defective Units A Defective Unit is NOT ACCEPTBALE to Sell to a paying customer or to Deliver to an internal customer A good item can have a small number of Defects. A Defective item is unusable

34. Six Sigma and Product Quality From technicalchange.com

35. Six Sigma and Process Capability From sixsigma.knowledgehills.com

36. Illustration of Process Capability vs. Product Specifications The Supplier is likely to produce conforming parts all the time 36 USL UCL 34 32 X Key Characteristic (dimension, functionality, delivery, etc..) 30 28 LCL 26 LSL 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Observation

37. Illustration of Process Capability vs. Product Specifications The Supplier is likely to produce a quantity of non-conforming parts 36 UCL 34 USL 32 X Key Characteristic (dimension, functionality, delivery, etc..) 30 LSL 28 LCL 26 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Observation

38. Taguchi Method of Design Analyzes design from four (4) Perspectives • Robust Design • Virtually “defect free” • Insensitive to random variation • Concept design • Take into account tooling, equipment, and processes available and proven • Parameter design • Variables in the process that management can manipulate • Determination of optimal levels (e.g. 8X11 paper) • Tolerance design • Separating key characteristics from others • Avoiding improbable stack ups

39. Taguchi Process encourages Teamwork 1. Problem Identification 2. Brainstorming Session Identify: factors, factor settings, possible interactions, objectives 3. Experimental Design Choose orthogonal arrays, design experiment 4. Run Experiment 5. Analyze Results 6. Confirmation Runs

40. Combining Multiple Tools (Figure 10.14) Focus on the Problem Collect Data Identify Assignable Causes Eliminate Causes / Reduce Variability Evaluate Results Monitor Conformance • Control Charts • Same Tools as for “Collect Data” • Robust Process Design • Pareto Charts • Define Specification • Choose between: • (a) Attribute • (b) Variable • Measure Capability • Map Process Flow • Impact of Defects on Process Flow and Cost * From Cachon, Matching Supply with Demand, Third Edition, McGraw Hill Irwin

41. Additional Conceptsto be covered later in the course

42. Variability requires Inventory to Compensate Supply Demand • Forecast variance • Market Conditions • Global Supply (Allocation) • Competitor Pricing • Competitor Supply • Customer Returns (Quality) • Other • Build variance • Scrap • Rework • Shortages • Delays • Other Requires excess Finished Goods and component Inventory to eliminate (minimize) impact on Customers

43. Suppose process A can start making defective units and once it starts to make defective units it does so until corrective action is taken, Suppose quality inspection to discover defective units is only done at process step C. With two units allowed in the buffers, there will be four defective units made before the problem is discovered. Batching and quality = Good unit = Bad unit A B C

44. But with 6 units allowed in the buffer, there will be 12 defective units before the problem is discovered! Hence: Large batches are problematic when quality is an issue. Large batches can lead to lots of wasted capacity – imagine if step B were the bottleneck! Firms should adopt “quality at the source” whenever possible: Inspect for quality when an item is produced. Inspect the 1st item in a batch rather than inspecting only when the batch is completed. Inspecting for quality is most valuable in front of the bottleneck. Quality at the source = Good unit = Bad unit A B C