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Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
Lean Six Sigma
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Lean Six Sigma

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  1. Lean Six Sigma An Overview S. Zaman Khan, Ph. D. March 28, 2009
  2. Agenda • History of quality • History of Six Sigma • Basic Statistics • What is Six Sigma • What is Lean Thinking • Lean (LSS) Methodology • LSS Application strategy • Basic Process Improvement Tools 2
  3. Lean Six Sigma and change • In order to develop, sustain, and become competitive, we have to make changes. • Lean Six Sigma is all about: – Changing the culture of an organization – Changing the processes to meet new customer requirements and to remove constraints. Lean Six Sigma is a physical transformation of the processes, and it is a transformation of the organizational cultural 3
  4. Change is necessary “To cherish traditions, old buildings, ancient cultures and graceful lifestyles is a worthy thing - but in the world of technology, to cling to outmoded methods of manufacture, old product lines, old markets or old attitudes among management and workers, is a prescription for suicide.” Sir Leuan Maddock 4
  5. There is always resistance to creativity and change ―In order to allow ourselves to be creative, we have to relinquish control and overcome fear. WHY? Because real creativity is life-alerting. It threatens the status-quo; it makes us see things differently. It brings about change and we are terrified of change.‖ Madeleine L’Engle ―The world hates change, yet it is the only thing that has brought progress." Charles F. Kettering 5
  6. Evolution - Quality Gurus • Walter A. Shewhart (1891 – 1967): The work on quality is pioneered by Dr. Walter A. Shewhart (Bell Tel. Labs) in 1920’s. The field of quality control got its name from his book ―Economic Control of Quality of Manufactured Product‖. • Edward Deming (1900 – 1993): Dr. Shewhart’s colleague (and student) Dr. Deming introduced new ideas in the field of quality control. His fundamental philosophy based on defect prevention rather than detection. • Josef Juran (1904 - 2008): Introduced new ideas in the quality improvements and taught quality-improvement methods in Japan and the US. Deming’s fourteen points and Juran’s four points are the backbone of modern quality concepts. • Philip B. Crosby (1926 - 2001): Diversified the quality concepts on all levels in an organization. He insists on exclusive use of facts, not judgment or guesswork, in making decisions regarding quality. • Kaoru Ishikawa (1915 – 1989): Known for his work in finding out the factors contributing to quality (fishbone or cause-and-effect diagram) and also his contemporary concepts of total quality control and total quality management. • Genichi Taguchi (1924 - ): Famous for his ideas of loss function and novel approaches to design of experiments as applied to manufacturing quality. 6
  7. Evolution - Quality Paramid Lean Six Sigma NPD Sharon’s model Time line Business Improvement Programs DFSS Value engineering DRIVE VBM 7-S framework Six Sigma BPR MBWA Baldrige National Tuckman’s Quality model Quality Quality ISO 9000 QFD councils revolution Work-Out Quality Benchmarking 1980s Taguchi control TQM methodology and Quality team Beyond Lean Motivational manufacturing Project teams theories If Japan can, Why can’t we? SQC Qaulity is free Zero Defect 1970s (70s/80s) movement Void Quality circles Quick response PDCA TRIZ TOC methodology (1970s) JIT (1950s) TQC (1970s) 1960s Throughput strategies Deming/Juran TOK Lean principles Shewhart SPC (1920s) SPC & QM (1950s) 1920s – Quick production strategies (1800s) 1950s Sampling methods (AQL) Industrial revolution & Inspection (18th century) Scientific management Pre-Industrial period Cost & Complexity 7 Note: Some of the terms are trademarks of other organizations but widely used in literature
  8. Phases of Quality Evolution • Pre-Industrial age • Industrial revolution (17th & 18th centuries) – Manual to machine culture – Inspection – Does it work approach … • Birth of quality management (19th century) – Quality control – Variation control – Quality assurance … – Motivational programs • If Japanese can do, Why can’t we • Zero defect movement • Our goal is 0.001 defect • Quality is free … – Scientific, statistical and management approaches • SPC/SQC • TOC, TRIZ, TQM • ISO 9000 • Six Sigma … – Common sense SPC – Statistical process control • Do it right the first time SQC – Statistical quality control • Self-inspections TOC – Theory of constraint • Lean principles … TRIZ – Theory of inventive problem solving TQM – Total quality management – LEAN SIX SIGMA 8
  9. The birth of Six Sigma quality standard • 1980s: Electronic industries faced challenges from Japanese competitors. • Motorola management selected a team of professionals including Bill Smith and Mikel Harry to evaluate the current quality approaches. • The team emphasized the correlation between the performance of a product in the market with the amount of rework required at the point of manufacturing. • In 1986, it was recommended to raise the Motorola quality standard from ±3 sigma to ±6 sigma and a new quality metric ―six sigma‖ was introduced. 9
  10. The birth of Six Sigma quality standard • 1993: Six Sigma tools at Asea Brown Boveri (ABB). • 1994: Mikel Harry developed Six Sigma Breakthrough Strategy (MAIC) and established Six Sigma Academy in Arizona, USA. • 1994: Allied Signal (now Honeywell) implemented Six Sigma at corporate level. • 1994-95: GE started corporate wide six sigma initiative. • Many companies followed Allied Signal and GE’s successful deployment of Six Sigma. • 1990s: Methodology improved to become DMAIC. • 1999: Combination of Lean and Six Sigma experimented. • 2002: Lean Six Sigma (LSS) became a standard approach in many industries. DMAIC – Define, Measure, Analyze, Improve, Control 10
  11. Basic Statistical notations 11
  12. Mean, Variance, and Standard deviation Mean (μ for population and x-bar for sample) is arithmetic average of a set of values. Data: 17, 16, 21, 18, 13, 16, 12, 11 Variance (σ2 for population and s2 for sample) Standard deviation (σ for population and s for sample) 12
  13. Properties of Normal Distribution • Normal Distribution: In statistics, the normal distribution or Gaussian distribution is a continuous probability distribution that describes data that clusters around a mean or average. • First property: A normal distribution can be described completely by knowing the mean and standard deviation. • Second Property: The area under sections of the curve can be used to estimate the cumulative probability. What is the difference among these three normal distributions? 13
  14. What is Six Sigma? 14
  15. Six Sigma Metric • Sigma (σ), a Greek letter, denotes standard deviation. • Six Sigma is a metric that measures the performance of a process. • Six Sigma as a metric – A process running at Six Sigma quality level produces no more than 3.4 defective parts per million opportunities (DPMO). – As the sigma quality level increases, the DPMO decreases and the rolled throughput yield (RTY) increases. 15
  16. Metrics and terms enforced by Six Sigma • A defect is a shortfall that causes inadequacy or failure by not meeting customer specification. • An opportunity is the total quantity of chances for a defect. • Defect per unit (DPU) Total number of defects DPU  Total number of units • Total opportunities (TO) TO  Total number of units  Opportunit ies • Defects per opportunities (DPO) Total number of defects DPO  Total number of opportunities • Defects per million opportunities (DPMO) DPMO  DPO 1,000,000 16
  17. Rolled throughput yield • Throughput yield: The percentage of the good pieces divided by the total pieces sent into the process. • First pass yield (FPY): The percent of good pieces resulting from a process step. It is the percentage of good pieces divided by total pieces started into the process step. • Rolled throughput yield (RTY): Rolled Throughput Yield (RTY) is the probability that a single unit can pass through a series of process steps free of defects. It is the product of first pass yield (FPY) of each process step. • Traditional throughput yield focuses on the final outcome of a process and allows a ―hidden factory‖ flourish (percentage of good piece/total pieces sent into process). • RTY allows to understand what areas/steps of the process are creating defects and how the process output is impacted by those defects. 17
  18. Cost of poor quality and Lean Six Sigma • Cost of poor quality (COPQ) is directly linked to the defects per million opportunities (DPMO) or sigma level. • Typical three-sigma company spends about 25 percent of each sales dollar on the COPQ. • The COPQ exceeds the % profit margin where COPQ is not known. Exercise: Brainstorm the cost of quality (COQ) and COPQ. Document at least five types in both categories. 18
  19. Six Sigma basics • Six Sigma quality level is derived from Gaussian curve for normal distribution (in use since 1733) and widely promoted by Carl Friedrich Gauss since 1794. • In 1922, Dr. Shewhart developed statistical process control techniques and a +/- three sigma quality standard was adopted by industry. • The six sigma quality standard emphasizes on shrinking the variation in the process so that it does not produce defects 99.99966% of the time. 19
  20. Area under the normal curve Large variation • The total area under the curve (between -∞ and +∞) is 100%. • Area between +/- 1 standard deviation is 68.27%. • Area between +/- 2 standard deviation is 95.45%. • Area between +/- 3 standard deviation is 99.73%. • A process running at +/- 3 Sigma 1  (x  )2  quality level produces 66,000 f ( x)  exp    2   2 2   PPM defective (after drifts, the in (x  ) long term). z   x    20
  21. Area under the normal curve Reduced variability • More data points would lie closer to the mean if variation is reduced. • If all the data points lie within +/- 6 standard deviations, than the throughput yield of the process is 99.9999998%. 21
  22. Six Sigma Quality level PPB – Parts per billion PPM – Parts per million ST – Short term LT – Long Term Area under the normal distribution curve beyond z » 4.57 is close to zero (1- 99.99966%). This translate in to 3.4 PPM & defined as Six Sigma Quality Level. 22
  23. Six Sigma quality level and process capability A process running at Six Sigma quality level produces no PPB – Parts per billion more than 3.4 parts per million defectives. PPM – Parts per million ST – Short term 23 LT – Long Term
  24. Why to raise the quality standard? 3.8-Sigma 6-Sigma 3.4 defects per million 99% Good 99.99966% Good opportunities • 20,000 lost articles of mail per hour. . • Seven articles lost per hour. • 5,000 incorrect surgical operations • 1.7 incorrect operations per per week. N week. • Two short or long landings at most • One short or long landing every major airports each day. five years. • 200,000 wrong drug prescriptions • 68 wrong drug prescriptions per each year. year. Based on U.S. statistics in the 1990s 24
  25. What quality level we want to be at? • The goal of SSQL depends on nature of the process, business needs, cost, and customer requirements. • Most companies use LSS for: – Problem solving – Cost reduction and increase in profit margins – Process optimization – New process/ product development – Personnel development and leadership – Growth. 25
  26. What is Lean Thinking? • Lean Thinking is also known as lean, lean production, lean manufacturing, Toyota production system (TPS), Just-in-time (JIT) etc. • It is a common sense approach. • Lean ideas originally developed in the United States (Ford Motors, 1914) and than widely used by Japanese (Toyota, 1950). • Lean is focused at eliminating the waste in the processes that in turn increases the speed, improves the quality, and reduces the cost. • ―Strategy that uses less of everything compared with traditional manufacturing: half the human effort, half the space, half the investment in tools, half the engineering hours to develop a new product. Also it requires keeping far less than half the needed inventory on site, results in many fewer defects and produces a greater and ever growing variety of products.‖ Machine that changed the world by James Womack (1990) 26
  27. Commonly used Lean Thinking tools • Value stream mapping (VSM) and process mapping • Kaizen events • Total productive maintenance (TPM) • Single minute exchange of dies (SMED) • 5 S (sort, set in order, Shine, Standardize, Sustain) • Load balancing • Kanban • Pull systems • Point-of-use inventory (as opposed to warehouses) • Vendor managed inventory (VMI) • Mistake-proofing • … 27
  28. Understanding Wastes Overproduction Unwanted Transportation Waiting Unwanted Movement Over Inventory 8 Wastes Overprocessing Unused Employee Creativity Defects 28
  29. Exercise • How to remember 8 wastes 29
  30. Variation and COPQ A good process running at traditional high quality has a potential to produce defects. Cost of poor quality (COPQ) =2666.19*Cost per part . Over time COPQ multiplies. A process running at a Six Sigma quality level has less opportunity to produce the defects. The process will produce less defects even after shifts and drifts over time. COPQ: cost of poor quality 30 USL – Upper specification limit LSL – Lower specification limit
  31. Focus of Lean Six Sigma • Every process has a target that is measured around the mean. • Variability is inherent to the processes that makes the mean dynamic. The measure of this variability is standard deviation. • Every process has a constraint that directly impacts the purpose or profit. • Every process has a waste that makes it slow. • Lean Six Sigma helps to – Move the mean to the target – Shrink the variation for consistency – Reduce and eliminate the constraints – Eliminate waste. 31
  32. What does Lean Six Sigma Means to a business • Metric – Produce no more than 3.4 parts per million opportunities (cost, quality, & delivery). • Problem solving – Use DMAIC breakthrough methodology to reduce variation, eliminate waste, and remove the bottlenecks (cost, quality & delivery). Understand customer requirements and reduce variation to meet those requirements. • Management system, strategy and Vision – Reduce cost, increase value, increase revenues, develop human resources, win over competitions, new business/ product development, … – A high performance system for executing business strategy - Motorola DMAIC: Define, Measure, Improve, Control 32
  33. Lean Six Sigma defined • Lean Six Sigma is a rigorous, disciplined, and data driven business process optimization and problem solving methodology which aims to reduce variability, eliminate non-value added activities (waste), and reduce cost. • Lean Six Sigma is applicable to any process/activity. • Used world-wide and is Well-proven methodology. 33
  34. Lean Six Sigma and financial benefits • From 1986 – 2001, Motorola saved $16 billions. • From 1996 – 1999, GE saved $4.4 billions. • From 1998 – 2000, Honeywell saved $ 1.8 billions. • From 2000 - 2000 Ford saved $1 billion. • Over the past 20 years Six Sigma saved Fortune 500 companies an estimated $427 billion. 34
  35. Industry embraced Six Sigma • 53 percent of Fortune 500 companies are currently using Six Sigma-and that figure rises to 82 percent if we look at just the Fortune 100 – 2006 survey • True six sigma produced 40% more savings than those with less rigorous programs – 2006 survey • More than 55% of 418 enterprises interviewed, implement lean six sigma – 2006 survey 35
  36. Harvesting the Fruit of Lean Six Sigma Difficult to Reach Fruit Design for Six Sigma (DFSS) Middle Fruit Lean and Six Sigma Low Hanging Fruit Lean, Basic quality tools Degree of Complexity Ground Fruit Logic and Intuition Basic tools 36
  37. Lean Six Sigma - Training strategy • Training strategy Plan – Plan – Train Review Train – Apply – Review Apply Lean Six Sigma Change Agents go through different levels of rigorous training, coaching and mentoring 37
  38. Lean Six Sigma Methodology Application Strategy Practical Problem Phase 2 Lean Six Sigma Methodology Measure Analytical Problem Characterization Phase 3 Analyze Analytical Phase 1 Solution Define Phase 4 Improve Practical Solution Optimization Phase 5 Control Lean Six Sigma uses the DMAIC (Define, Measure, Analyze, Improve, Control) process as a disciplined and methodological approach for problem solving and process improvement 38
  39. Lean Six Sigma Application Process Define Measure Analyze Improve Control & Sustain • Project identification • Process Mapping • Brainstorming • VA Improvement • EWMA and CuSum • Value Stream Mapping • Data Collection plans • Basic Tools • Brainstorming Control Charts • VOC and Kano Analysis • Constraint Identification • Components of Variation • Replenishment Pull • Pareto Charts • Project Approval Form • Setup Reduction • FMEA • Process Flow • Visual Process Control • COPQ analysis • Generic Pull • Multi-Vari • Benchmarking • Poka-Yoke • Internal Rate of Return • C&E Diagrams • Box Plots • DOE/ RSM • Process Control Plans Analysis • C&E Matrix • Interaction Plots • Stocking, Purchasing and • Project Commissioning • Cash Flow Analysis • Kaizen • Regression Sales Strategy • Procedures & policies • RACI • TPM • ANOVA • Supply-chain optimization • Safety measures • Stake holder analysis • Control Charts • C&E Matrices • Batch Sizing • Training • MSA and Gage R&R • Hypothesis testing • Line Balancing • Final Control Plan • Process Capability Indices • Piloting and Simulation • Training • Identify Problem • Map Business Process • Propose Critical X’s • Critical X’s Confirmed • Implement Process • Develop List of Customers • Value stream mapping • Prioritize Critical X’s • Develop Potential Changes and Controls • Develop List of CTQ’s • Qualify measurement • Verify Critical X’s Solutions • Write Control Plan • Finalize Project Focus systems • Estimate the Impact of • Select Solution • Calculate Financial Impact and Key Metrics • Collect Data Each X on Y • Optimize Solution • Process Metrics • Financial benefits • Determine process • Quantify the Opportunity • Pilot Solution • Transition Project to • Complete Project stability • Prioritize Root Causes • Process capability analysis Future Owners approval form • Conduct process • Conduct Root Cause • Identify Project Capability analysis Analysis on Critical X’s • Translation Opportunities • Baseline analysis VOC – Voice of customer TPM – Total productive maintenance FMEA – Failure mode and effect analysis EWMA – Exponentially Weighted Moving Average X’s – Input variables COPQ – Cost of poor quality C&E – Cause and Effect ANOVA – analysis of variance CuSum - cumulative sum Y’s – Output variables RACI – Responsible, Accountable, MSA – Measurement system analysis VA – value add CTQ – Critical to quality Consulted, Informed R&R – Repeatability and Reproducibility DOE – Design of experiment RSM – Response surface methodology 39
  40. Define Phase • Define the problem. • Identify the customer(s). • Organize the team and define its roles and responsibilities. • Establish goals and milestones. • Establish the scope of the LSS project. • Define the metrics. What is important to • Map the process. customers OR business goals? • Develop data collection plan. 40
  41. Measure Phase • Collect data on current process. • Confirm the customer’s needs, and expectation. • Validate measurement system. • Determine input variables (X’s) that may impact output (Y’s). • Establish baseline measurement of current process. How is the process performing? How does it look / feel like to the customer? How good is the data? 41
  42. Analyze Phase • Narrow the focus to specific issues. • Develop a mechanism to analyze data. • Identify what is causing defects, waste and variation. Characterize the variables (X’s). • Find improvement opportunities. • Based on data analysis, revisit problem statement and assess the need to further scope the issues. Graphical Analysis Tools – Box Plot Histogram Interpretations The shape has a bell shape. The shape has two humps. The shape has a long tail. It is symmetric. It is bimodal. It is not symmetric. The shape is flat. There are one or more outliers. 40E 41Q Describing the Distributions What are the most 22 points of data Median 22 points of data (Half of the distribution) important causes of Mode process waste, defects 5 6 7 8 9 10 11 12 13 14 & variation? 41P Mean (8.16) 42
  43. Improve Phase • Validate hypothesis about the root cause of the problem • Identify critical variables (X’s) Move the mean. Shrink the variance. • Identify alternate solutions Eliminate the waste. • Determine optimal solution • Perform cost/benefit analysis • Design improvements • Pilot improvements • Implement and validate improvements 43
  44. Control Phase • Ensure corrective actions are taken. • Mistake-proof the process. • Transition the control of the new process to the process owner. • Provide techniques to sustain the improvements. • Measure the final capability. • Monitor performance. How can we 12.5 11.5 maintain the process 10.5 improvements? 9.5 8.5 7.5 0 10 20 30 44
  45. Design for Six Sigma (DFSS) Define Measure Analyze Design Verify • DFSS is used to determine the customer and business needs and translating those needs into new process or product in the most optimal way to achieve most optimal and sustaining results results. • DFSS is process generation (as opposed to process improvement). • Also called DMADV or new product or process development (NPD). 45
  46. LSS organizational structure Roles and responsibilities Champions • Own the vision, direction, integration, and results • Identify Black Belts/Green Belts, and help in project identification Black Belts Green Belts Sponsor Process Owners • Apply Lean Six Sigma to specific projects • Identify and assist in scoping projects • Lead and direct teams to execute projects • Own the process • Ensure changes are sustained Lean Six Sigma Master Black practitioners are Belts assisted by financial experts to estimate and • Support Champion in effective project scoping verify savings • Train, coach, and develop Black Belts/Green belts • Work on complex projects • Approve BB certification 46
  47. Training requirements • Champions training: 2 days – one week. – Some companies include another week of on-the-job training. • Green Belt training: Two weeks and one project. • Black Belt training: Four – five weeks and two projects. • Master Black Belt training: Three – five weeks training, coaching, mentoring and facilitation. 47
  48. LSS Practitioner qualities • Customer focus, self-motivated and positive personality • Leadership skills • Excellent communication skills • Excellent presentation skills • Project management skills • Process and product knowledge is preferred • Team player • Result oriented • Data mining • Passionate • Patience • Learner 48
  49. Typical Lean Six Sigma project areas • Late Delivery • Low Customer Satisfaction • Poor Product Reliability • Excessive Variation • High Cost of Quality • Poor Design • Incoming Product Quality • High Operating Costs Problems • Excessive Scrap/Rework • Unpredictable Quality or Product • High rate of rejections Performance • High Inventories • Poor Process Capability • Long Cycle Times • High Incidence of ―Past Due‖ notices • Capacity Constraints • High Maintenance Costs • Excessive Set-Up Costs • Low Machine Utilization • Waste • Transactional Defects • Low Rolled Yield Rate 49
  50. Lean Six Sigma Project Selection Criteria • A high value project • A repeatable process • Strong management sponsorship • Strategic linkage • Process is within your control • Data availability • Compelling problem statement • Despite attempts, process owner could not solve the problem • Workable scope • Short completion period • Firm defect definition 50
  51. Common Causes of Project Failures • Inadequate management support. • Inadequate time for Green Belts/ Black Belts and other team members . • Project Scope Is Too large • ―Boiling the ocean‖ • Scope Creep. • Project Scope Is Too small • Projects with little business impact. • Solution-in-Mind • ―Just Do It‖ projects do not require the rigors of the LSS DMAIC process. • Data not available or not valid. • Politics (pet projects). • Lack of ―soft skills‖ (communication, leadership, team building, and change management). 51
  52. Lean Six Sigma deployment Modified from Lean Six Sigma by Michael L. George, 2002, pp., 227 Vision Planning Major Improvement Benchmarking Initiating Executive teams Incremental Improvement …. Marginal Improvement Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Comply Commit Embed Encode Invest Believe and Embrace Part of DNA publicize and spread 52
  53. Tool Time
  54. S.M.A.R.T. Goals • Specific: A specific goal has a greater chance of success. • Measureable: Criteria to measure the progress. • Attainable: Identify goals that are most important to you. • Realistic: Set a goal that is realistic. • Time bound: Set a timeframe to achieve the goal. 54
  55. Project charter • Precisely and quantitatively define the problem, establish the objectives, both in one or two sentences. • Problem statement: On-time delivery performance of all ABC units is only 60%. This results in customer complaints and shipment rejections that in turn increases the inventory levels. • Objectives: Improve on-time delivery to 95% by the end of June 2009. • In scope/Out of scope. • Financial and other Benefits. • Identify customers. • Select a cross functional team and get the support of the subject matter experts. • … 55
  56. Seven basic tools of quality • Ishikawa diagram (also called cause-and-effect diagram or fishbone diagram) • Pareto chart • Check sheet • Control chart • Flowchart (or process map) • Histogram • Scatter diagram 56
  57. Make the process visible • Helps to see the abnormalities immediately. • Uncovers the hidden factory. • Helps to become proactive. • Use a simple chart to show how the process is performing. • Display the primary metric close to the process. • Display the process map or flow chart of the process. 57
  58. RS On-time Delivery Performance RS 2005 On-time Delivery performance 100 90 80 70 60 50 40 30 20 10 0 Jan Feb M ar Apr M ay Jun Jul Aug Sep Oct Nov Dec Customer M etric (Performance to request), % RS M etric (Performance to schedule), % Goal, % On-time performance: RS metric: 60% Customer metric: 35% Goal (by end of June 2006): 95% by end of June 2009 58
  59. Process map • Identify start and end point (process boundaries). • Document the process steps by creating the flow chart. • Identify the inputs and outputs of each step. • Characterize the inputs – Controllable (C) – Standard operating procedure (SOP) – Noise (N). • Document data such as cycle time of the process steps, number of operators, etc. 59
  60. Types of variables • Controllable (C): These inputs can be adjusted or controlled during the process (e.g., speed, feed, temperature). • Standard Operating Procedures (SOP): Common sense items (e.g., cleaning, safety). • Noise (N): Things that we can’t control or don’t want to control (too expensive or too difficult; e.g., ambient temperature, humidity, wind etc.). Input (X’s) Output (Y’s) Process Characterize Identify Input variables Output variables 60
  61. Value-added and non value-added activities • Value Added: Activity that increases the value. • To determine if an activity is value-added ask: – Is this something the customer is willing to pay for? – Does the activity positively change the form, fit, or function of the product? • Non Value Added: Any action which consumes resources without directly contributing to the product. • Non value added but necessary: Activities that do not add value but are necessary to complete the job. • Optimize value-added, reduce non value-added but necessary, and eliminate non value-added activities. 61
  62. High level process map RS Valve manufacturing process - MRP - Overnight schedule print out - Daily trucks - Offices at strategic locations - Bulk pull from stock - Various shippers - Divided by regions - MRP - Staging processes - Forecast Sales & Scheduling/ Shipping/ Customers Planning Production Marketing Warehouse Receiving - Daily orders - 2 shifts - 24 different sizes and Types - Machining and sub-processes - No forecast - Assembly - Domestic and International - Testing Purchasing - Bulk orders (8 weeks SS) - No LT for commercials - MRP - Dynamic LT for specials (1 to 60 days) Suppliers - 15 Overseas (LT = 24 weeks) - 25 Domestic (LT = 8 weeks) LT – Lead-time SS – Safety stock MRP – Material requirement planning Note: Compare this high-level process map with the Org chart 62
  63. Detailed RS Valve assembly process map (As Is) SOP , C, 3 days Av. Daily and weekly Manual SOP , C, System SOP , C, 4 days av., Overnight production Daily 5 people Daily 3 people schedules VA 3 people NVA C, 45 days Av. waiting Planning Scheduling Warehouse – N Enter customer Complete Incomplete kit review/prioritize create, print and pull and stage BOM ? orders staging overnight orders distribute WO the material C, NVA, 2 people, 3 days Av. Y C, 50 yards one way C, 5 days Av. C, Daily, 150 yards one way, 45 minutes 25 minutes Pick prioritized Match the Take the Deliver the kit in Assembler – Staging production paperwork and paperwork and the shop staging pick the material orders pick the parts material to Shop area with paperwork C, NVA, 30 min Av. C, NVA, 30 min Av. Rework Rework SOP, C, VA , 45 minutes Av. SOP, 5 min. Av. N C, NVA, 3 min Av. N Place the parts Assemble (liner, on the assembly disc, stem, Leakage test OK ? Torque test OK ? line bushing, body) Y Y Place Y Take parts to assemblies in QC OK ? Pack and ship Shipping Legend the staging area WO – Work order N BOM – Bill of materials SOP, C, 5 min Av. C, VA, 2 days Av. Kit – Production order package C, NVA, 5 min Av. SOP, C, NVA, 5 min Av. C – Controllable Assembly to N – Noise SOP – Standard Op. Procedure Processing time rework (or scrap) Av. - Average Transactions = 16 days C, NVA, 30 min Av. VA – Value added Processing = 3.74 hours per product CT – Cycle time NVA – Non-value added Waiting time = ? 63 Average CT per product in indicated
  64. Exercise • Mr. Z is attending SEF2009 from Bahrain. Develop a process map showing steps from receiving forum information to attending the forum. Write down estimated time for steps (where applicable) and other characterizations and data. 64
  65. Rolled throughput yield versus throughput yield Hidden Factory 95% customer Manufacture Manufacture Manufacture Assemble quality part 1 part 2 part 3 Not OK OK Rework/Scrap Inspect Yield = 95% Process step yield (First pass yield) RTY helps to uncover the hidden factory and 95% 97% 94% 95% enforces prevention Manufacture Manufacture Manufacture Assemble part 1 part 2 part 3 RTY = 82 % 95% 95*97=92% 95*97*94=86% 95*97*94*95=82% and not 95% 50,000 PPM wasted 30,000 PPM wasted 60,000 PPM wasted 50,000 PPM wasted Total wasted opportunities: 190,000 PPM Real yield or RTY Process Sigma = 2.37 65
  66. Defects per million opportunities (DPMO) • Assuming that a process produces 30 assemblies – Each assembly has 5 parts – Number of opportunities for defect in each assembly = 5 + 1 = 6 (five for each part and a sixth for appearance). • Total opportunities, TO = 30*6 = 180 • Number of units checked = 30 – Defects found = 5 • DPO = 5/180 = 0.0277778 • DPMO = 0.0277778*1,000,000 = 2,7777.78 or 27,778 • This means that if the process has produced 1,000,000 assemblies, we expect to end up with 27,778 defects in those assemblies. • Add up DPMO for each sub process to find overall PPM defective. 66
  67. 5 W and 2 H problem identification approach Who? Identify customers complaining about the problem What? Define the problem accurately When? Timing - When did the problem start? Where? Location - Where is it occurring? Why? Identify the causes (5 WHYs) How? In what mode the problem occur How many? Magnitude or frequency of the problem 67
  68. 5 Whys help to find the root cause? 1. Why did the system fail? A: The motor burned out. 2. Why did the motor burn out? A: The shaft seized. 3. Why did the shaft seize? A: There was no lubrication. 4. Why was there no lubrication? A: The line filter was clogged. 5. Why was the line filter clogged? A: It was the wrong sized mesh! 68
  69. Time is Money Make use of time, let not advantage slip. Better three hours too soon, than one minute too late. Time is the supreme Law of nature. William Shakespeare - Arthur Stanley Eddington Time and tide wait for no man. One thing you can't recycle is wasted time. Geoffrey Chaucer Time is the school in which we learn, Time is the wisest counselor of all. time is the fire in which we burn. - Pericles - Delmore Schwartz Time is money. Time Money, I can only gain or lose. But time I can only Will never wait. lose. So, I must spend it carefully You may delay, but time will not. Benjamin Franklin Time is what we want most, but what we use worst. Time stays long enough for those who use it. - William Penn - Leonardo Da Vinci We must use time as a tool, not as a couch. - John F. Kennedy There is one kind of robber whom the law does not strike at, and who steals what is most precious to people—time.
  70. Cycle time (CT) • Time it takes an operator to go through all of his/her work elements before repeating them. • It is the time measured by direct observation in which a good item or good product is completed by the process. Cycle time Operation Operation Operation Operation 1 2 3 4 Work in process (WIP) Finished parts 70
  71. Little’s Law to approximate Process CT • Little’s law provides a quick and reliable mean to measure the process cycle time (PCT) also called Lead Time (LT). • The time from work order release into the process until completion and measured as: Exits/day Operation Operation Operation Operation 1 2 3 4 Work in process (WIP) Finished parts Process Cycle Time (PCT) or Lead Time (LT) 71
  72. Process cycle time (PCT) • WIP is the number of pieces or transactions being worked in the process at any given time, WIP = 14 pieces. • Exits is the amount of work completed over a given period of time (Weekly, Daily, hourly etc.), Exits = 7 units/day. • CT or LT = (WIP/Exits per day) = 14/7 = 2 days. Exits/day Operation Operation Operation Operation 1 2 3 4 Work in process (WIP) Finished parts Process Cycle Time (PCT) or Lead Time (LT) • A reduction in WIP leads to cycle time reduction. 72
  73. Takt Time (TT) • Takt time is the maximum time allowed to produce a product or process a transaction in order to meet customer demand. = • Time available – Time available per day minus break, wash-up, set up etc. • Customer demand – Number of units on order on a given day. • Takt Time metric helps to synchronize the pace of production to the pace of sales. 73
  74. Understanding TT • CT = TT, ideal situation. Customer receives on-time. • CT TT, Hidden factory, too much waste. • CT TT, over capacity. Producing faster than customer order pace is waste of resources. 74
  75. Takt Time (TT), example • Customer demand = 100 units/day. • Assuming one shift of eight hours and lunch time is not paid. • Total time = 8 hours/day. • Breaks = 2*10 minutes, wash up = 10 minutes, meetings = 20 minutes, operator maintenance = 10 minutes. • Time available = (8*60) – 60 = 420 minutes /day. • TT = 420/100 = 4.20 minutes. • In order to meet the customer demand on-time, each unit should be completed in 4.20 minutes. 75
  76. Cause-and-Effect diagram • Two approaches: – Use check sheets based on data collected by team – Brainstorming without previous preparation. • Two way to construct: – Use a flip chart, write down the problem (effect) on the right side of a main line with arrow on it and draw lines with major headings and than brainstorm causes under each topic. – Gather team thoughts on cause in a tabular form. Each column heading will represent the major cause and sub-causes will be recorded. • Construct the CE diagram Cause-and-Effect Diagram Example Major cause 3 Major cause 1 sub-sause sub-sause sub-sause sub-sause sub-sause sub-sause sub-sause sub-sause W rite the agreed problem sub-sause sub-sause sub-sause sub-sause sub-sause sub-sause sub-sause sub-sause Major cause 4 Major cause 2 76
  77. Exercise • Assume that the local industries and KFUPM want to increase the interaction and cooperation to make the research and development more effective and useful. Develop a cause and effect diagram. 77
  78. Determine critical causes • Brainstorm with the cross functional team and subject matter experts to determine the critical causes. • Use data (if available) to determine the impact and criticality of a cause. • Use multi voting techniques and knowledge of subject matter experts to narrow the list of causes. 78
  79. Check sheet • A simple tool to collect the real time data at the location where it is generated. • Typically a blank form is used to record each occurrence of interest. • Check sheet is an effective data collection tool. Defects in outside diameter of a steel disc Machine# Sat Sun Mon Tue Wed Total Machine 1 II I I II III 9 Machine 2 III I I I II 9 Machine 3 II III II II II 11 Machine 4 III IIII III III IIIII 18 Total 10 10 7 8 12 47 79
  80. Scatter plot • Scatter plot is used to display the relationship between two variables. • We can use MS Excel to display the scatter plot. • The graph shows that there is a positive relationship between number of hours studied each day and exam score. Study Math Students Hours Score A 3 82 B 5 92 C 2 77 D 6 82 E 7 92 F 1 52 G 2 67 H 7 87 I 1 42 J 7 102 80
  81. Pareto chart • Based on team input and/or Chart of mistake categories data, display the impact of 60 critical causes on the output of 50 the process using a Pareto 40 chart or bar chart. 30 20 • Pareto analysis provides 10 0 information on what 20% of the variables cause 80% of the problem. • In many cases you may need a second and third level Pareto analysis 81
  82. Implementation plan • Develop a solution to eliminate or reduce the impact of the critical causes on the output of the process. • Develop an implementation (or action plan). It can be a simple Excel spread sheet. – Action item (to reduce the impact of critical causes) – Impact – Responsibility – Start date – End date – Status – … 82
  83. Specifications and process data • Statistical software help to use various tools (such as control charts, process capability analysis, descriptive statistics etc). • Such tools require training and practice. • Simple line chart can be used to see the data with reference to the specifications (use MS Excel). Upper specification 4.5 limit (USL) 4 Mean 3.5 3 2.5 2 1.5 Lower specification limit (LSL) 1 1 2 3 4 5 6 7 8 9 10 83
  84. Final thoughts Lean Six Sigma • Process centered and project focused. • Focuses on customer requirements. • Emphasizes permanent change and transformation. • Fact based and data driven. • Applicable to transactional as well as manufacturing processes. • Requires planning, training, coaching and mentoring efforts. • Helps in sustaining and consistent change across functions. • Requires leadership involvement/ commitment and line management buy-in. • Top down approach is most successful • Brings about breakthroughs. 84
  85. Appendix 85
  86. Thank You
  87. Six Sigma (new standard) versus Three Sigma (old standard) .001 PPM .001 PPM Six Sigma and PPM 1300 PPM 1300 PPM 1  (x  )  f ( x)  exp    2  2 ²   65 4 3 2 1 0 1 2 3 4 5 6    x   3 2 1  1 2 3 Difference between±3 and ±6 data distribution (no shift, short term) Three Sigma Process • PPM expected = 2700 Six Sigma Process Distribution span = 6 Sigma (±3) • PPM expected = 0.002 • Large variance Distribution span = 12 Sigma (±6*/2) • Higher standard deviation ( = 0.0038) • The variance shrunk • Smaller data distribution around mean (less • The standard deviation reduced to half ( = 0.0019) frequency distribution close to mean) • The data distribution around mean (first quartile) is • Data spread along the z line higher • Data points exist close to upper and lower (x ) specs i.e., at 3 and –3 distance from the z  score  mean.  87
  88. 5- Why Analysis Why Because Why did some Trouble 1- Spare Parts Availability (35.10%) Tickets exceed 3 days to 2- Unclear Process! (21.22%) Resolve!? 3- Cost Estimation (15.08%) 4- Wrong Assignment (10.61%) 5- Customer Availability (39.10%) 6- Lack of Technical skills (28.49%) 7- Restricted/ Remote areas (23.46%) 8- Incorrect Descriptions! (21.78%)
  89. 5- Why Analysis (Cont..)
  90. Eight wastes in a business must be measured and eliminated or reduced T Transportation O Over production W Waiting I Inventory S Skills D Defect O Over processing M Movement Another Acronym used to remember 8 waists is TIM P WOOD 90
  91. Mean Shift and Variance Reduction On Center Off Center Large Spread Large Spread LSL T USL LSL T USL Off Center On Center Small Spread Small Spread LSL T USL LSL T USL 91
  92. Understanding and reducing variation # of Goals Lower Specification Upper Specification Limit (LSL) Limit (USL) 92
  93. Understanding and reducing variation # of Goals Lower Specification Upper Specification Limit (LSL) Limit (USL) 93
  94. Acronyms used on slide#3 SPC – statistical process control TOK – theory of knowledge TOC – theory of constraint TRIZ – Theory of inventive problem solving TQC – total quality control SQC – statistical quality control QM – quality management PDCA – plan-do-check-act QFD – quality function deployment TQM – total quality management MBWA – management by walking about BPR – business process re-engineering VBM – value based management DFSS – design for six sigma DMADV – design, measure, improve, control, verify DRIVE – define, review, identify, verify, execute (TQM) AQL – Acceptable quality level NPD – New product/process development (same as DFSS) Tuckman’s model – forming, storming, sorming, performing model McKinsey 7-S framework – shared Value, structure, system, style, staff, skills, strategy Industrial Revolution – a period in the late 18th and early 19th centuries when major changes in agriculture, manufacturing, production, and transportation had a profound effect on the socioeconomic and cultural conditions in Britain. 94

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