Surviving Six Sigma What Statisticians Should Do To Not Be Left Behind

Transcription

1 Swiss Statistics Meeting Quality and Statistics Aarau, Switzerland, 1719 November 2004 Surviving Six Sigma What Statisticians Should Do To Not Be Left Behind Professor Tony Bendell Director, Centre of Quality Excellence, University of Leicester, UK and Managing Director, Services Ltd., Nottingham, UK Services Ltd., Quality & Reliability House, 82 Trent Boulevard, West Bridgford, Nottingham, NG2 5BL, UK Tel: Fax:

2 Centre of Quality Excellence, University of Leicester Established January 2002 Mission: To foster a rigorous, yet creative approach to the development of Quality Excellence in management, engineering, systems and services across the UK and world Industry, commerce and the public and voluntary sectors. Incompany, Distance Learning and campus based MSc and postgraduate programmes in The Management of Quality Excellence Customer Service Management Broad research base funding from EFQM, BQF, RollsRoyce, DTI, Patent Office, LRQA, SGS Yarsley, EMDA etc. Recognition Centre for Six Sigma Black and Green Belts Sponsor and Associate organisations

3 Services Ltd. Quality and Productivity Consultancy and Training organisation established 1983 One of the three largest BQF licensed trainers for the EFQM Excellence Model and partner for Six Sigma IRCA licensed trainers for ISO 9001:2000 Licensed by the British Accreditation Bureau etc. Specialisms: Excellence, TQM ISO 9000, ISO14000 etc. Six Sigma, Design for Six Sigma Reliability Analysis Benchmarking Creativity, HRM etc. SPC, Taguchi, QFD, tools Statistical Modelling, OR

4 Six Sigma The Threat and the Opportunity for Statisticians Six Sigma having major business impact Trains and supports its own people Statisticallybased, but not just statistics Should statisticians welcome or oppose Six Sigma? How should they get involved?

5 Six Sigma Success in the Late Twentieth Century Extensively applied, growing application, high esteem Management and Engineering press, conferences and growing academic literature Individual Black/Green belt first project annualised savings 10, million The most important initiative GE has ever undertaken it has taught GE employees about the importance of fulfilling expectations Jack Welch The Rewards for GE Six Sigma Progress (In millions) Cost Benefit $

6 Origins 1989 Motorola 1995 GE But a clever package, nothing much new! origins in Deming, Juran and Ishikawa s work from 1950 s and Shewhart from 1920 s Deming Juran Ishikawa Shewhart

7 The Meaning of Six Sigma Technical Term (Statistical) 12 Standard Deviations inside Specification Best in Class three nonconformances out of one million opportunities Variation Reduction/Improvement approach that allows comparability between dissimilar processes Measurementbased Corporate Strategy for improvement Philosophy

8 The Technical Meaning for Six Sigma DEFECTS A 3 sigma quality product/process has 6 standard deviations inside the specification SPECIFICATION DEFECTS Given the specification, Six Sigma Quality for a product/process metric means that more of the distribution is contained within the Specification than at the Three Sigma level. Yield (% Inside Spec) DPMO (defectives per million opportunities At ± 2 sigma 95.44% 45,600 At ± 3 sigma 99.73% 2,700 At ± 4 sigma % 63 At ± 5 sigma % 0.57 At ± 6 sigma % A 6 sigma quality product/process has 12 standard deviations inside the specification Defects 3 Sigma 3 Sigma Defects Defects 6 Sigma 6 Sigma Defects

9 Standard Six Sigma Methodology Allows For a 1.5 Sigma Shift! If the product/process mean now shifts 1.5 sigma (worse case), then: 1.5 sigma shift in mean 3 Sigma 3 Sigma 6 Sigma 6 Sigma (defectives Yield (% Inside Spec) DPMO per million DEFECTS 6 SIGMA SPECIFICATION DEFECTS opportunities At ± 2 sigma 69.15% 308,500 At ± 3 sigma 93.32% 66,810 At ± 4 sigma % 6,210 At ± 5 sigma % 233 At ± 6 sigma % 3.4

10 Six Sigma Infrastructure Strategic programme ProjectbyProject improvement in fencedoff areas Clear responsibilities and authority Champions, Owners, Black and Green Belts Before and after performance measurement (typically cost) 140+ statistical tools and concepts DMAIC(T) methodology: Define, Measure, Analyse, Improve, Control, (Transfer)

11 Six Sigma Improvement Path Defect Rate (ppm) ppm 10x Improvement 6210 ppm The 5 Sigma Wall PROJECTBYPROJECT SIX SIGMA (DMAIC) ppm Sigma Quality Level 30x Improvement DESIGN FOR SIX SIGMA (DFSS) 70x Improvement ppm 33.4 ppm.4

12 Six Sigma Infrastructure Continued Black and Green Belts Fulltime or Parttime (20%+) improvement engineers, managers, supervisors 20/10+ days training Excel and Minitab (or alternative) Projects selected prior to training Onsite mentoring Certification

13 Important Tools GE/Six Sigma Academy, 140+ statistical tools to define, measure, analyse, improve and control What are the key ones? Simple data description and summarisation tools Regression and Anova SPC and Measurement System Analysis Experimental Design (including Taguchi methodology, Response Surfaces) Quick technical problem solving Nonstatistical tools FMEA/FMECA QFD Poka Yoke Right brain and creativity tools

14 Not Just About Statistical Tools ProjectbyProject approach, clear metric and DMAICT crucial Previous tool use poor, disorganised, noncoherent Measurement System Analysis Emphasis on improvement, not Quality Assurance (e.g. attribute charting) Much socalled SPC is really SQC Control the X s not the Y s process variables not the product characteristics Experimental Design key to successful projects

15 Versions of Six Sigma Manufacturing (and Engineering) Transactional Less DOE Lognormal distributions Design for Six Sigma reduced process control emphasis development cycle Lean Sigma dangerous: potential conflict in methodologies

16 Connections to Other Quality Approaches ISO 9001:2000 EFQM Excellence Model Design for Six Sigma

17 ISO 9001, Statistical Techniques Identification of Need The supplier shall identify the need for statistical techniques required for establishing, controlling and verifying process capability and product characteristics Procedures The supplier shall establish and maintain documented procedures to implement and control the application of the statistical techniques identified above

18 Quality Management Process Model

19 EFQM Excellence Model ENABLERS 50% People 9% People Results 9% RESULTS 50% Leadership 10% Policy & Strategy 8% Partnerships & Resources 9% Processes 14% Customer Results 20% Society Results 6% INNOVATION & LEARNING Key Performance Results 15%

20 People People Results Leadership Policy & Strategy Processes Customer Results Key Performance Results Partnerships & Resources Society Results 6 Sigma Managing Variation Reduction Tools: SPC D.O.E Taguchi Cpk

21 Design for Six Sigma Six Steps to Six Sigma: 1. Identify requirements of endproduct 2. Determine the characteristics of the components of the product that are key to meeting the endproduct requirements 3. Determine for each key characteristic, the process step that affects or controls it 4. Identify target value for each characteristic that minimises impact of variation upon endproduct, and determine maximum allowable range or tolerance of that characteristic 5. Identify actual or expected variation in each characteristic and determine capability of relevant process step for that characteristic 6. Ensure that process steps are in statistical control and centred around the targets

22 Six Steps Example: A Table Step One Step Two Step Three Step Four Step Five Step Six Table is balanced on its legs Legs are correct size Sawing process 27 inches inches (allowable range) inches (actual variation) C P = Allowable Range = 0.5 Actual Variation Improve sawing accuracy

23 Designing a Table Requirements Balanced on its legs Component Characteristics Legs are correct size Controlling Process Step Sawing Process Target Actual 27 ± 0.1" ± 0.2"

24 Some Other Issues Danger of dilution Concentration on Statistical techniques and left brain Oldfashioned statistics Lack of formal link to Policy Deployment Emphasis on Cost Down, not Profit Up Culture/Buyin/ hype Integration with other Programmes

25 My Experiences Very effective focus programme Even if infrastructure not right still effective projects May improve processes but not other infrastructure Whole programme should be planned and scoped Very good people development

26 Toolkit and Training Issues Learning by rote Training requirement Black Belts without DOE!/Danger of dilution Leftbrain only 141 statistical tools/ oldfashioned statistics Rightbrain creativity/innovation tools e.g Six Hats, Triz etc.

27 Six Sigma and the Statistician Impact of Japanese approaches in Six Sigma on traditional engineering and statistical roles, structures and work practices enfranchisement of workforce in statistical methods questions role of statistical elite potential move from specialists to mentors, trainers and Project Managers extend roles but also include others Black Belts MAY be engineers or statisticians Need for mindset change Factbased Datadriven decision combined with Engineering knowledge and judgement Cost, Customer Focus and Business Decision Making focus Balanced treatment of variation and risk

28 Are The Statisticians Included? Thin on the ground! Nervousness on purity Packaged solutions Issues on role/erosion of practice Conflicts of method

29 The Image and the Opportunity Statistical Elite? Specialists? Analysers Gatekeepers OR Mentors, enablers, supporters

30 Enfranchisement of Statistical Methods Do we want to see correct use by a few or largely correct use by the many? Do we want to be in or out?

31 So, What Should We Do? Participate Support Mentor Help Focus programme