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Six Sigma


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A Business Management Strategy

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Six Sigma

  1. 2. What we will cover… <ul><li>Introduction to Six Sigma </li></ul><ul><li>Full Life-Cycle Case Study </li></ul>
  2. 3. Introduction <ul><li>Six Sigma was invented by Motorola, Inc. in 1986 as a metric for measuring defects and improving quality. Since then, it has evolved to a robust business improvement methodology that focuses an organization on customer requirements, process alignment, analytical rigor and timely execution. </li></ul>,,3074-5804,00.html
  3. 4. Six Sigma, the GE Way <ul><li>Six Sigma - A vision of quality which equates with only 3.4 defects per million opportunities for each product or service transaction. Strives for perfection. </li></ul><ul><li>DFSS – (Design for Six Sigma) is a systematic methodology utilizing tools, training and measurements to enable us to design products and processes that meet customer expectations and can be produced at Six Sigma quality levels. (DMADV - Define, Measure, Analyze, Design, Verify) </li></ul><ul><li>DMAIC – (Define, Measure, Analyze, Improve and Control) is a process for continued improvement. It is systematic, scientific and fact based. This closed-loop process eliminates unproductive steps, often focuses on new measurements, and applies technology for improvement. </li></ul>Source:
  4. 5. Other Quality Systems <ul><li>Total Quality Management (TQM) </li></ul><ul><li>Toyota Production System (TPS) </li></ul><ul><li>Kaizen </li></ul><ul><li>Lean </li></ul><ul><li>Theory of Constraints </li></ul><ul><li>Agile </li></ul><ul><li>PDCA – Plan, Do, Check, Act </li></ul><ul><li>Good Manufacturing Process – Pharma </li></ul><ul><li>ISO 9000 </li></ul>
  5. 6. Key Concepts <ul><li>A process is all the activities involved in producing a product or service for a customer. It is cross-functional in nature </li></ul><ul><li>Quality is defined by customer requirements for the chosen process </li></ul><ul><li>Defects are defined and counted </li></ul><ul><li>Inconsistencies in the process, known as variation, are studied </li></ul><ul><li>Causes of variation are identified and addressed </li></ul>
  6. 7. Key Terminology Critical to Quality Attributes most important to the customer Defect Failing to deliver what the customer wants Opportunity Event where success or failure can be determined Process Capability Level of quality your process can deliver X Underlying factors that affect Ys Y Measures being addressed by the project
  7. 8. Key Terminology Variation What the customer sees and feels Stable Operations Ensuring consistent, predictable processes to improve what the customer sees and feels Common Cause of Variation A source of failure that is always present as part of the random variation inherent in the process Special Cause of Variation A source of failure that lies outside the Process, and so is intermittent, unpredictable, unstable
  8. 9. DMAIC Define Measure Improve Analyze Control Team Chartering Customer Focus Process Mapping Measurement Variation Data Collection Data Analysis Process Analysis and Focus Root Cause Analysis Quantify Opportunity Generate Solutions Select Solutions Implementation Planning Monitor the Process Documentation Institutionalize
  9. 10. Case Study IT services business Customer service call center
  10. 11. Project Selection <ul><li>Business strategy </li></ul><ul><ul><li>How important is customer satisfaction? </li></ul></ul><ul><ul><li>How important is it to attract new customers? </li></ul></ul><ul><li>Competitive position </li></ul><ul><ul><li>How do we compare to our competitors? </li></ul></ul><ul><ul><li>Benchmarking </li></ul></ul><ul><li>Best projects </li></ul><ul><ul><li>Issue is well-defined with supporting data </li></ul></ul><ul><ul><li>Scope is well-defined </li></ul></ul><ul><ul><li>Objectives are stated in business terms and are measurable </li></ul></ul>
  11. 12. Project Selection <ul><li>Customer satisfaction </li></ul><ul><ul><li>Average </li></ul></ul><ul><ul><li>Lower than best-in-class in industry </li></ul></ul><ul><li>Positive correlation with account growth </li></ul><ul><ul><li>Customer satisfaction and new accounts are statistically related to one another </li></ul></ul><ul><ul><li>Business judgment </li></ul></ul><ul><li>No correlation with customer service spending </li></ul><ul><ul><li>Per call costs were not higher at strong competitors </li></ul></ul><ul><li>Goals: Reduce support costs while improving new account growth </li></ul>
  12. 13. Define <ul><li>Team Chartering </li></ul><ul><ul><li>Goal statement: &quot;Increase the call center's industry-measured customer satisfaction rating from its current-level (90th percentile = 75 percent) to the target level (90th percentile = 85 percent) by end of the fourth-quarter without increasing support costs.“ </li></ul></ul><ul><ul><li>Milestones, tasks, responsibilities, schedule and communication plan. </li></ul></ul>
  13. 14. Define <ul><li>Customer Focus </li></ul><ul><ul><li>SIPOC diagram – identify customers (stakeholders) </li></ul></ul><ul><ul><ul><li>Customers </li></ul></ul></ul><ul><ul><ul><li>Staff </li></ul></ul></ul><ul><ul><ul><li>Business </li></ul></ul></ul><ul><ul><li>Voice of the Customer interviews </li></ul></ul><ul><ul><ul><li>&quot;What influences your level of satisfaction with our services?&quot; </li></ul></ul></ul><ul><ul><li>Summarize customer requirements </li></ul></ul><ul><ul><ul><li>Identify measures for each requirement </li></ul></ul></ul><ul><ul><ul><li>Next slide </li></ul></ul></ul>
  14. 15. Define Requirement Measure (CTQ) Quickly connect with a helpful person Wait Time Get the information I need Transfers; Service Time Apply the information, with help if needed Customer Satisfaction, Support Cost Understand how to avoid problems recurring Days to Close
  15. 16. Define <ul><li>Process mapping </li></ul><ul><ul><li>Helpful during the Measure phase, as the project team considers how and where to gather data that will shed light on the root cause of the issues most pertinent to the project's goals. </li></ul></ul>
  16. 17. Measure <ul><li>Define measures and how the data will be gathered </li></ul><ul><li>Example: </li></ul><ul><ul><li>Customer Satisfaction </li></ul></ul><ul><ul><ul><li>By industry standard monthly survey </li></ul></ul></ul><ul><ul><ul><li>The project will require additional, more frequent, case-by-case customer-satisfaction data. A measurement system that tracks with the industry survey will be devised and validated. </li></ul></ul></ul>
  17. 18. Measure <ul><li>Define performance standards </li></ul><ul><li>Example: </li></ul><ul><ul><li>Customer Satisfaction </li></ul></ul><ul><ul><ul><li>Current Baseline </li></ul></ul></ul><ul><ul><ul><ul><li>90th Percentile / 70-80% Satisfied </li></ul></ul></ul></ul><ul><ul><ul><li>Performance Target </li></ul></ul></ul><ul><ul><ul><ul><li>90th Percentile / 85% Satisfied </li></ul></ul></ul></ul>
  18. 19. Measure <ul><li>Identify segmentation factors for data collection plan </li></ul><ul><ul><li>Focus data collection effort </li></ul></ul><ul><ul><li>Use cause-and-effect tools </li></ul></ul><ul><ul><li>How is Y naturally segmented? </li></ul></ul><ul><ul><ul><li>Call center, product type? </li></ul></ul></ul><ul><ul><li>What factors may be driving the Ys? </li></ul></ul><ul><ul><ul><li>Take a guess at what your important Xs might be </li></ul></ul></ul><ul><ul><ul><li>Call type, customer type? </li></ul></ul></ul>
  19. 20. Measure <ul><li>Assess measurement system </li></ul><ul><ul><li>Accuracy </li></ul></ul><ul><ul><ul><li>Does the measure agree with the “truth”? </li></ul></ul></ul><ul><ul><li>Repeatability </li></ul></ul><ul><ul><ul><li>Does the system always produce the same value? </li></ul></ul></ul><ul><ul><li>Reproducibility </li></ul></ul><ul><ul><ul><li>Will different people get the same results? </li></ul></ul></ul><ul><ul><li>Stability </li></ul></ul><ul><ul><ul><li>Is the system accurate over time? </li></ul></ul></ul>
  20. 21. Measure <ul><li>Collect the data </li></ul><ul><ul><li>Automated </li></ul></ul><ul><ul><li>Manual </li></ul></ul><ul><ul><li>New metrics may be needed </li></ul></ul><ul><li>Display the data </li></ul><ul><ul><li>Look for clues into causes of variation </li></ul></ul><ul><ul><li>Simple charts and graphs </li></ul></ul>
  21. 22. Analyze <ul><li>Measure process capability </li></ul><ul><ul><li>Compare current performance to standards </li></ul></ul><ul><li>Refine improvement goals </li></ul><ul><ul><li>Adjust goals if data shows departure from expectations </li></ul></ul><ul><li>Segment data </li></ul><ul><ul><li>Slice and dice data to look for patterns to find causes of variation </li></ul></ul>
  22. 23. Analyze <ul><li>Identify possible Xs </li></ul><ul><ul><li>“ Likely suspect” causes of variation </li></ul></ul><ul><li>Identify and verify the critical Xs </li></ul><ul><ul><li>Narrow down to most important causes of variation </li></ul></ul><ul><ul><li>Why do Problems and Changes cost more than other call types? </li></ul></ul><ul><ul><li>Why are calls processed on Mondays and Fridays more expensive? </li></ul></ul><ul><ul><li>Why do transfer rates differ by call type? (higher on Problems and Changes, lower on others) </li></ul></ul><ul><ul><li>Why are wait times higher on Mondays and Fridays and on Week 13 of each quarter? </li></ul></ul>
  23. 24. Analyze <ul><li>Refine the benefit forecast </li></ul><ul><ul><li>Update the forecast of how much improvement can be expected </li></ul></ul><ul><ul><li>Found that key support cost drivers (the delays and interruptions during call-servicing) were the same as those known to drive down customer satisfaction – so a win-win seemed to be possible. </li></ul></ul>
  24. 25. Improve <ul><li>Identify Solution Alternatives </li></ul>Driving Xs Solution Alternatives Staffing <ul><li>Add staff Mondays and Fridays, reduce staff on Sundays </li></ul><ul><li>Develop staffing model </li></ul><ul><li>Create on-call list to fill-in for absentees </li></ul>Web Service Percentage <ul><li>Focus on services that can be done best on the Web </li></ul><ul><li>Define and communicate the value prop to customers </li></ul><ul><li>Evaluate incentives to move traffic to the Web </li></ul>Transfers and Callbacks <ul><li>Improve call center processes to reduce transfers and callbacks without impacting customer satisfaction </li></ul>
  25. 26. Improve <ul><li>Verify the Relationships Between Xs and Ys </li></ul><ul><ul><li>Solution Selection Matrix </li></ul></ul><ul><ul><ul><li>Solution Alternatives </li></ul></ul></ul><ul><ul><ul><li>Customer Requirements (CTQs) </li></ul></ul></ul><ul><ul><li>Regression Analysis </li></ul></ul><ul><ul><ul><li>Determine the strength of each solution against the CTQs </li></ul></ul></ul>
  26. 27. Improve <ul><li>Select and Tune the Solution </li></ul><ul><li>Details of the plan for the Monday staffing pilot program: </li></ul><ul><ul><li>Xs to adjust: Staffing level (add five for pilot, full increment to wait for evidence plan works) </li></ul></ul><ul><ul><li>Ys to measure for impact and unintended side effects: </li></ul></ul><ul><ul><ul><li>Wait time, v/s ratio, customer satisfaction, transfers, callbacks, service time. </li></ul></ul></ul><ul><ul><ul><li>Compare &quot;new staff&quot; versus &quot;old staff&quot; (hypothesis test). </li></ul></ul></ul><ul><ul><ul><li>Measure monthly to observe learning curve effect, if any </li></ul></ul></ul>(continued on next page)
  27. 28. Improve <ul><li>Details of the plan for the Monday staffing pilot program: </li></ul><ul><ul><li>Measurement system issues: Revise existing sampling plan and data collection process to distinguish new staff from old staff. </li></ul></ul><ul><ul><li>Because the current customer satisfaction sampling gives only 1 data point per month (not enough to see a change), arrange a special sample – five per day for the first 60 days of the pilot (80 percent from existing staff, 20 percent from new staff). </li></ul></ul><ul><ul><li>People and logistics issues: Communicate what is happening and why. Emphasize evaluation is not of individuals, only overall impact. </li></ul></ul>
  28. 29. Improve <ul><li>Implement Solution </li></ul><ul><ul><li>Pilot, if possible </li></ul></ul><ul><li>Collect data during pilot </li></ul><ul><ul><li>Xs and Ys </li></ul></ul><ul><ul><li>Watch for unintended impacts </li></ul></ul><ul><li>Report out and obtain approval for full implementation </li></ul>
  29. 30. Control <ul><li>Develop Control Plan </li></ul><ul><ul><li>Management control dashboards – Ys </li></ul></ul><ul><ul><li>Operational control indicators – Xs </li></ul></ul><ul><li>Determine Improved Process Capability </li></ul><ul><ul><li>Business Growth </li></ul></ul><ul><ul><li>Customer Satisfaction </li></ul></ul><ul><ul><li>Support Cost per Call </li></ul></ul><ul><ul><li>Days to Close </li></ul></ul><ul><ul><li>Wait Time </li></ul></ul><ul><ul><li>Transfers </li></ul></ul><ul><ul><li>Service Time </li></ul></ul>
  30. 31. Control <ul><li>Implement Process Control </li></ul><ul><ul><li>Ongoing data collection and presentation </li></ul></ul><ul><li>Close Project </li></ul><ul><ul><li>Roll out process changes </li></ul></ul><ul><ul><ul><li>Training </li></ul></ul></ul><ul><ul><ul><li>Transition control to management </li></ul></ul></ul><ul><ul><ul><li>Validate results </li></ul></ul></ul><ul><ul><ul><li>Refinements </li></ul></ul></ul><ul><ul><li>Project post mortem </li></ul></ul>
  31. 32. Tools
  32. 33. Tools <ul><li>ANOVA ANalysis Of VAriance (ANOVA), a calculation procedure to allocate the amount of variation in a process and determine if it is significant or is caused by random noise. </li></ul><ul><li>Cause and Effect Diagram A cause and effect diagram is a visual tool used to logically organize possible causes for a specific problem or effect by graphically displaying them in increasing detail. It helps to identify root causes and ensures common understanding of the causes. It is also called an Ishikawa diagram. </li></ul>
  33. 34. Tools <ul><li>Control Chart A graphical tool for monitoring changes that occur within a process, by distinguishing variation that is inherent in the process (common cause) from variation that yield a change to the process (special cause). </li></ul><ul><li>Kano Analysis Kano analysis is a quality measurement tool used to prioritize customer requirements based on their impact to customer satisfaction. </li></ul>
  34. 35. Tools <ul><li>Pareto The Pareto principle states that 80% of the impact of the problem will show up in 20% of the causes. A bar chart that displays by frequency, in descending order, the most important defects. </li></ul><ul><li>Run Chart A performance measure of a process over a specified period of time used to identify trends or patterns. </li></ul>
  35. 36. Tools <ul><li>X-Bar and R Charts This set of two charts is the most commonly used statistical process control procedure. Used to monitor process behavior and outcome overtime. </li></ul>
  36. 37. Resources <ul><li> </li></ul><ul><li> </li></ul><ul><li> </li></ul><ul><li> </li></ul><ul><li>The Six Sigma Way: How GE, Motorola, and Other Top Companies are Honing Their Performance by Peter S. Pande, Robert P. Neuman, Roland R. Cavanagh </li></ul><ul><li>Fourth Generation Management by Brian L. Joiner </li></ul><ul><li>Leading Six Sigma by Ronald D. Snee and Roger W. Hoerl </li></ul><ul><li>The Pocket Idiot’s Guide to Six Sigma by Marsha Shapiro and Anthony Weeks </li></ul>
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