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Quality Tools ISQA 511 MBA Mellie Pullman 1. Managing Quality Quality defined Quality assurance  Continuous improvement tools  Statistical quality control.

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Presentation on theme: "Quality Tools ISQA 511 MBA Mellie Pullman 1. Managing Quality Quality defined Quality assurance  Continuous improvement tools  Statistical quality control."— Presentation transcript:

1 Quality Tools ISQA 511 MBA Mellie Pullman 1

2 Managing Quality Quality defined Quality assurance  Continuous improvement tools  Statistical quality control Total cost of quality 2

3 Peanut M&M Open Package  Count the M&Ms  Inspect them and determine what defines quality for this product.  Make a list  What kind of quality attributes can we measure if we worked in this factory? 3

4 Quality Specifications Design quality: Inherent value of the product in the marketplace  Dimensions include: Performance, Features, Reliability, Durability, Serviceability, Response, Aesthetics, and Reputation. Conformance quality: Degree to which the product or service design specifications are met Quality as fitness for use Quality as excellence 4

5 Judging Fitness for Use What are desired benefits received by the customer from the product? 5

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7 Common Improvement Tools Cause and effect diagrams (aka “Fishbone” or Ishikawa diagrams) Check sheets Pareto analysis Run charts and scatter plots Bar graphs Histograms Value Stream mapping 7

8 Cause and Effect Diagram ASKS: What are the possible causes? Root cause analysis — open and narrow phases 8

9 Problem: M&M print is off kilter & hard to read No one is sure why, but plenty of opinions Let’s “Management by Fact” With as many of the tools as possible 9

10 Generic C&E Diagram 10

11 Check Sheets (take a sample every day and record problems) Event:Day 1Day 2Day 3 Off center printII I Irregular shape candy llllllllllll No print on candy II Partial Print on Candy III 11

12 Pareto Analysis (sorted histogram) Issue A Issue B Issue C Issue D Other (160)

13 Percent of each out of 480 total incidents... Issue A 21% Issue B 18% Issue C 15% Issue D 14% Other 33% 13

14 Run Chart or Scatter Diagram RunScatter 14

15 Inspect every item  Expensive to do  Testing can be destructive, should be simply unnecessary Statistical techniques  Statistical process control (SPC) Discovering “problems” 15

16 Costs of Quality: Four Sources Costs associated with discovering the condition of products and raw materials (e.g., inspection) Costs from product defects found before shipment to the customer (e.g., rework, scrap) Costs associated with defects found after shipment to customer (e.g., warranty) Appraisal Costs Internal Failure Costs External Failure Costs Costs associated with preventing defects and limiting failure and appraisal costs (e.g., training, improvement projects, data gathering, analysis) Prevention Costs 16

17 Cost of Quality End user’s hand Warranty cost Loss of market share Reputation Own process Next process End of the line Final inspection Defects found at Cost to the company Impact to the company Very minor Minor delay Some rework Rework (mat’l, labor, capacity, etc) Reschedule of work Significant rework Delivery delay Inspection costs 17 Rule-of-Thumb: $1 spent in prevention leads to $10 of saved internal, external, and appraisal costs.

18 Question? What are recent quality failures in the news? Significance? 18

19 Recent Johnson & Johnson’s Articular Surface Replacement (hip joint) 19

20 KEY METHOD FOR FINDING PROBLEMS Quality 20

21 Control Charts are tools for tracking variation based on the principles of probability and statistics SPC: Statistical Process Control 21

22 Variation Exists in any process  error rate made by receptionist entering guest record data,  bus time between two points,  ounces of beverage in a bottle,  number of minutes past the alarm that you stay in bed in the morning. 22

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24 Sources of Variation: 2 Types Common (random) causes: chance or generally unidentifiable sources of variation.  Slight variation in walking speed  Slight variation in raw material Controllable or Assignable causes: A reason why the change occurred  people blunder, faulty setup, or a batch of defective raw material,worn equipment, fluctuating temperature  Super huge peanuts arrived from supplier so we only got 8 in each bag. 24

25 Common Causes 425 Grams 25

26 Controllable Causes Grams (a) Location Average 26

27 Controllable Causes (a) Location Grams Average 27

28 Controllable Causes (a) Location Grams Average 28

29 Controllable Causes (b) Spread Grams Average 29

30 Controllable Causes (b) Spread Grams Average 30

31 Controllable Causes (c) Shape Grams Average 31

32 The Normal Distribution -3  -2  -1  +1  +2  +3  Mean 68.26% 95.44% 99.74%  = Standard deviation 32

33 How variation impacts our process: Generally random variation cannot economically be eliminated from a process. Controllable variation can be detected and elimination of its causes is economically justified. Observations beyond the control limits are attributed to controllable variation. 33

34 Process Control Chart Activities Periodically sample from our operation or process. Calculate some characteristic like average, standard deviation, or range. Plot the characteristic in time order on the chart. 34

35 Purpose of Charts To ensure the process variation is in control To ensure that the process is capable of meeting the requirements (specifications and tolerances of the organization) 35

36 Variable Control Charts (X bar & R) Measurement charts: some characteristic we can measure (weight, time, distance) X: average measurement for the sample R: range of the measurements in the sample Variable charts have lots of information, better for advanced analysis of a process 36

37 Control Limit Formulas & Constants (A2, D3, & D4) In Textbook chapter 9 pg. 190 Our sample size n= ? 37

38 Control Chart Examples Nominal UCL LCL Sample number Variations Appears to have normal variation 38

39 Control Chart Examples Nominal UCL LCL Sample number Variations A process with a gradual trend 39

40 Control Chart Examples Nominal UCL LCL Sample number Variations Points Outside of the Control limit 40

41 Excel Line Chart UCL LCL Overall mean Daily sample means 41

42 Attribute Chart: P-Charts Number of defective units in a sample. Yes/No, Pass/Fail, Go/No Go criteria  p- easy to measure (pass/fail) but sample size must be big enough to detect at least one defective item on average  p=percentage faulty in sample  N= average size of the sample 42

43 P Chart In the Oregonian today: P chart for monitoring patient falls in the hospital # falls/number of occupied beds 43

44 Control Charts for Attributes UCL p = p + 3  p LCL p = p - 3  p  p = p (1 - p )/ n P-Chart 44

45 Excel Line Chart UCL-p LCL-p Overall mean: p bar Daily defect rate 45

46 Process Capability 46

47 Process Capability Can the process provide acceptable quality consistently? 47

48 Video: Look for products which need some kind of tolerance # on their label. 48

49 Oregon Attorney General’s Lawsuit (Motrin Recall) A unit of Johnson & Johnson is recalling infant and children's liquid products including infant’s and children’s Tylenol®, Motrin®, Zyrtec® and Benadryl® products. They initiated this voluntary recall because:  "some of these products may not meet required quality standards“ and may contain a higher concentration of active ingredient than is specified” 49

50 Process Capability Ratio (C p ) Upper Tolerance Limit – Lower Tolerance Limit 6σ6σ σ is the estimated standard deviation for the individual process observations (SPC) Tolerance or specifications are dictated by the customer, part drawings, laws, or other non- process related entities. 50

51 Shown Graphically: Process Capability ratio of 1 (99.7% coverage) 51

52 Climbing Carabineer In the lab, average breaking strength is:  = 6500 lbs and  = 150 lbs Industry Safety Specification = 6000 lbs lower limit and 7000 lbs upper limit. Can the process make a product to this specification 99.7 % of time ? 52

53 Process Capability Ratio (C p ) Upper Tolerance Limit – Lower Tolerance Limit 6σ6σ ( )/(6*150)= 1.11 Capable? 53

54 Process Capability Index (C pk ) Used when the process is not precisely centered Assume process mean is actually  = 6300 C pk = min [( )/(3*150), ( )/(3*150)] 54

55 “Six Sigma Quality” When a process operates with  6σ variation inside the tolerance limits, only 2 parts out of a million will be unacceptable. 55

56 With 6-Sigma, Hershey’s Kisses Errors Goodbye 56

57 Web Order System for Sales Reps Fulfill 4000 shipments per month  Critical to customer satisfaction tree developed: cost, quality, and delivery  Process map all functions Take out non-value added steps and identify steps where errors occur that affect the 3 focal areas  DPMO: defects per million opportunities Example: not meeting required lines shipped per hour each day is a process defect. 57


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