Introduction To Six Sigma

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Introduction To Six Sigma

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  • 1. Introduction To SIX - SIGMA Presented by :http://www.QualityGurus.com

2. Agenda 0750 - 0800 Participants introduction 0800 - 0930 Introduction to Six Sigma concept Key Concepts 0930 - 0945 Tea / Coffee Break 0945 - 1200 Forms of waste What is Sigma Components of Six Sigma 1200 - 0100 Lunch Break0100 - 0200 Selecting a Project 0200- 0300 Open session / Q&A 3. Participants Introduction

  • Your Name
  • Department
  • Your job profile
  • Your exposure to Quality Management/ Six Sigma

4. Ground Rules

  • Program success depends on your participation. Actively participate.
  • Please avoid cross-talks.
  • Observe specified timings.
  • Please keep your mobile phones switched off.
  • Feel free to ask question at any point of time.
  • - Restrict question to specific issue being discussed, while general
  • questions can be discussed during Q & A session.
  • Enjoy the program !

5. Introduction to Six Sigma Purpose of six sigma :To make customer happier and increase profits 6. Origin of Six Sigma

  • 1987 Motorola Develops Six Sigma
    • Raised Quality Standards
  • Other Companies Adopt Six Sigma
    • GE
      • Promotions, Profit Sharing (Stock Options), etc. directly tied to Six Sigma training.
    • Dow Chemical, DuPont, Honeywell, Whirlpool

7. Time Line 2002 1995 1992 1987 1985 Dr Mikel J Harry wrote a Paper relating early failures to quality Motorola Allied Signal General Electric Johnson & Johnson, Ford, Nissan, Honeywell 8. Pilots Six-Sigma Performance Width of landing strip 1/2 Width of landing strip If pilot always landswithin 1/2 the landing strip width, we say that he has Six-sigma capability. 9. Current Leadership Challenges

  • Delighting Customers.
  • Reducing Cycle Times.
  • Keeping up with Technology Advances.
  • Retaining People.
  • Reducing Costs.
  • Responding More Quickly.
  • Structuring for Flexibility.
  • Growing Overseas Markets.

10. Six Sigma Benefits?

  • Generated sustained success
  • Project selection tied to organizational strategy
    • Customer focused
    • Profits
  • Project outcomes / benefits tied to financial reporting system.
  • Full-time Black Belts in a rigorous, project-oriented method.
  • Recognition and reward system established to provide motivation.

11. Management involvement?

  • Executives and upper management drive the effort through:
    • Understanding Six Sigma
    • Significant financial commitments
    • Actively selecting projects tied to strategy
    • Setting up formal review process
    • Selecting Champions
    • Determining strategic measures

12. Management Involvement?

  • Key issues for Leadership:
    • How will leadership organize to support Six Sigma ? (6council, Director 6 , etc)
    • Transition rate to achieve 6 .
    • Level of resource commitment.
    • Centralized or decentralized approach.
    • Integration with current initiatives e.g. QMS
    • How will the progress be monitored?

13. What can it do?

  • Motorola:
    • 5-Fold growth in Sales
    • Profits climbing by 20% pa
    • Cumulative savings of $14 billion over 11 years
  • General Electric:
    • $2 billion savings in just 3 years
    • The no.1 company in the USA
  • Bechtel Corporation:
    • $200 million savings with investment of $30 million

14. GE Six Sigma Economics Source:1998 GE Annual Report, Jack Welch Letter to Share Owners and Employees - progress based upon total corporation cost/benefits attributable to Six Sigma. 6 Sigma Project Progress 1996 1998 2000 2002 0 500 1000 1500 2000 2500 1996 Cost Benefit (in millions) 15. Overview of Six Sigma PAIN, URGENCY, SURVIVAL COSTS OUT GROWTH TRANSFORM THEORGANIZATION CHANGETHE WORLD 6 SIGMA AS A STATISTICAL TOOL 6 SIGMA AS A PHILOSOPHY 6 SIGMA AS A PROCESS 16. Overview of Six Sigma

  • It is a Philosophy
    • Anything less than ideal is an opportunity for improvement
    • Defects costs money
    • Understanding processes and improving them is the most efficient way to achieve lasting results
  • It is a Process
    • To achieve this level of performance you need to:
    • D efine,M easure,A nalyse,I mprove andC ontrol
  • It is Statistics
    • 6 Sigma processes will produce less than 3.4 defects per million opportunities

17. Philosophy

  • Know Whats Important to the Customer (CTQ)
  • Reduce Defects (DPMO)
  • Center Around Target(Mean)
  • Reduce Variation (Standard Deviation)

18. Critical Elements

  • Genuine Focus on the Customer
  • Data and Fact Driven Management
  • Process Focus
  • Proactive management
  • Boundary-less Collaboration
  • Drive for Perfection; Tolerance for failure

19. Data Driven Decision

  • Y
  • Dependent
  • Output
  • Effect
  • Symptom
  • Monitor
  • X1 . . . Xn
  • Independent
  • Input-Process
  • Cause
  • Problem
  • Control

f(X) Y= The focus of Six sigma is to identify and control Xs 20. Two Processes

  • Define
  • Measure
  • Analyze
  • Improve
  • Control
  • Define
  • Measure
  • Analyze
  • Design
  • Verify

DMAIC DMADV

  • Existing Processes
  • New Processes
  • DFSS

21.

  • KeyConcepts

22. COPQ (Cost of Poor Quality) - Lost Opportunities - The Hidden Factory - More Setups - Expediting Costs - Lost Sales - Late Delivery - Lost Customer Loyalty - Excess Inventory - Long Cycle Times - Costly Engineering Changes Average COPQ approximately 15% of Sales

  • Hidden Costs:
  • Intangible
  • Difficult to Measure
  • Traditional Quality Costs:
  • Tangible
  • Easy to Measure

- Inspection - Warranty - Scrap - Rework - Rejects 23. COPQ v/s Sigma Level Cost of Quality % Sales Sigma Level 24. CTQ (Critical-To-Quality)

  • CTQ characteristics for the process, service or process
  • Measure of What is important to Customer
  • 6 Sigma projects are designed to improve CTQ
  • Examples:
    • Waiting time in clinic
    • Spelling mistakes in letter
    • % of valves leaking in operation

25. Defective and Defect

  • A nonconforming unit is a defective unit
  • Defect is nonconformance on one of many possible quality characteristics of a unit that causes customer dissatisfaction.
  • A defect does not necessarily make the unit defective
  • Examples:
    • Scratch on water bottle
    • (However if customer wants a scratch free bottle, then this will be defective bottle)

26. Defect Opportunity

  • Circumstances in which CTQ can fail to meet.
  • Number of defect opportunities relate to complexity of unit.
  • Complex units Greater opportunities of defect than simple units
  • Examples:
    • A units has 5 parts, and in each part there are 3 opportunities of defects Total defect opportunities are 5 x 3 = 15

27. DPO (Defect Per Opportunity)

  • Number of defects divided by number of defect opportunities
  • Examples:
    • In previous case (15 defect opportunities), if 10 units have 2 defects.
    • Defects per unit = 2 / 10 = 0.2
    • DPO = 2 / (15 x 10) = 0.0133333

28. DPMO (Defect Per Million Opportunities)

  • DPO multiplies by one million
  • Examples:
    • In previous case (15 defect opportunities), if 10 units have 2 defects.
    • Defects per unit = 2 / 10 = 0.2
    • DPO = 2 / (15 x 10) = 0.0133333
    • DPMO = 0.013333333 x 1,000,000 = 13,333

Six Sigma performance is 3.4 DPMO 13,333 DPMO is 3.7 Sigma 29. Yield

  • Proportion of units within specification divided by the total number of units.
  • Examples:
    • If 10 units have 2 defectives
    • Yield = (10 2) x 100 /10 = 80 %
  • Rolled Through Yield (RTY)
    • Y1 x Y2 x Y3 x . x Yn
    • E.g 0.90 x 0.99 x 0.76 x 0.80 = 0.54

30.

  • Forms of Waste

31. What are the forms of waste?

  • Waste of Correction
  • Waste of Overproduction
  • Waste of processing
  • Waste of conveyance (or transport)
  • Waste of inventory
  • Waste of motion
  • Waste of waiting

32. 1. Waste of correction

  • Repairing a defect wastes time and resources (Hidden factory)

Operation1 Test Test Product Operation2 Failure Investigation Rework Failure Investigation Rework Hidden Factory 33. 2. Waste of Overproduction

  • Producing more than necessary or producing at faster rate than required
    • Excess labor, space, money, handling

34. 3. Waste of processing

  • Processing that does not provide value to the product
    • Excess level of approvals
    • Tying memos that could be handwritten
    • Cosmetic painting on internals of equipment
    • Paint thickness more than specific values

35. 4. Waste of conveyance

  • Unnecessary movement of material from one place to other to be minimized because -
    • It adds to process time
    • Goods might get damaged
  • Convey material and information ONLY when and where it is needed.

36. 5. Waste of inventory

  • Any excess inventory is drain on an organization.
    • Impact on cash flow
    • Increased overheads
    • Covers Quality and process issues
  • Examples
    • Spares, brochures, stationary,

37. 6. Waste of Motion

  • Any movement of people, equipment, information that does not contribute value to product or service

38. 7. Waste of Waiting

  • Idle time between operations
  • Period of inactivity in a downstream process because an upstream activity does not deliver on time.
  • Downstream resources are then often used in activities that do not add value, or worst result in overproduction.

39. Some more sources of Waste

  • Waste of untapped human potential.
  • Waste of inappropriate systems
  • Wasted energy and water
  • Wasted materials
  • Waste of customer time
  • Waste of defecting customers

40.

  • What is Sigma?

41. Have you ever

  • Shot a rifle?
  • Played darts?

What is the point of these sports? What makes them hard? 42. Have you ever

  • Shot a rifle?
  • Played darts?

Who is the better shooter? Jack Jill 43. Variability

  • Deviation = distance between observations and the mean (or average)

Observations Deviations 10 10 - 8.4 = 1.6 9 9 - 8.4 = 0.6 8 8 - 8.4 = -0.4 8 8 - 8.4 = -0.47 7 - 8.4 = -1.4 averages 8.4 0.0 Jack 8 7 10 8 9 Jill 44.

  • Deviation = distance between observations and the mean (or average)

Variability Observations Deviations 7 7 - 6.6 = 0.4 7 7 - 6.6 = 0.4 7 7 - 6.6 = 0.4 6 6 - 6.6 = -0.6 6 6 - 6.6 = -0.6 averages 6.6 0.0 Jack Jill 7 6 7 7 6 45.

  • Variance = average distance between observations and the mean squared

Variability Variance Observations Deviations 10 10 - 8.4 = 1.6 9 9 8.4 = 0.6 8 8 8.4 = -0.4 8 8 8.4 = -0.47 7 8.4 = -1.4 averages 8.4 0.0 Squared Deviations 2.56 0.36 0.16 0.16 1.96 1.0 Jack 8 7 10 8 9 Jill 46.

  • Variance = average distance between observations and the mean squared

Variability Variance Observations Deviations 7 7 - 6.6 = 0.4 7 7 - 6.6 = 0.4 7 7 - 6.6 = 0.4 6 6 6.6 = -0.66 6 6.6 = -0.6 averages 6.6 0.0 Squared Deviations 0.16 0.16 0.16 0.36 0.36 0.24 Jack Jill 7 6 7 7 6 47. Variability

  • Standard deviation = square root of variance

Jack Jill Average Variance Standard Deviation Jack 8.4 1.0 1.0 Jill 6.6 0.24 0.4898979 But what good is a standard deviation ? 48. Variability The world tends to be bell-shaped Most outcomesoccur in themiddle Fewerin the tails (lower) Fewerin the tails(upper) Even very rareoutcomes arepossible Even very rareoutcomes arepossible 49. Variability Here is why:Even outcomes that are equally likely (like dice), when you add them up, become bell shaped 50. Normal distributions are divide up into 3 standard deviations oneach side of the mean Once your that, youknow a lot aboutwhat is going on And that is what a standard deviationis good for ? Normal bell shaped curve 51. Causes of Variability

  • Common Causes :
    • Random variation within predictable range (usual)
    • No pattern
    • Inherent in process
    • Adjusting the process increases its variation
  • Special Causes
    • Non-random variation (unusual)
    • May exhibit a pattern
    • Assignable, explainable, controllable
    • Adjusting the process decreases its variation

52. Limits

  • Process and Control limits:
    • Statistical
    • Process limits are used forindividual items
    • Control limits are used withaverages
    • Limits = 3
    • Define usual (common causes) & unusual (special causes)
  • Specification limits:
    • Engineered
    • Limits = target tolerance
    • Define acceptable & unacceptable

53. Usual v/s Unusual,Acceptable v/s Defective Another View LSL USL USL LSL Off-Target Large Variation On-Target Center Process ReduceSpread The statistical view of a problem USL LSL LSL = Lower spec limit USL = Upper spec limit 54. More about limits Good quality:defects are rare (C pk >1) Poor quality:defects are common (C pk 1)

  • Poor quality: defects are common (Cpk

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