1 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT

2 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT 1.D. C. Montgmery, Introduction to Statistical Quality Control, John Wiley & Sons, 6 th Ed. 2.Mitra A., Fundamentals of Quality Control and Improvement, PHI, 2 nd Ed., J Evans and W Linsay,The Management and Control of Quality, 6 th Edition, Thomson, Bester field, D H et al., Total Quality Management, 3 rd Edition Personal education, D. C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, 5 th Ed., References

3 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Kano Model Exciting Expected Basic Satisfied Not PresentPresent

4 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Kano’s Model of (Non- Linear) Customer Satisfaction Customer Satisfied Customer Not Satisfied Requirement Fulfilled Requirement Not Fulfilled Delighter (D) Linear Satisfier (L) or Performance Indifferent (I) Time Must Have (M) or basic Needs

The Kano Diagram Customer Satisfied Customer Dissatisfied Product Fully Functional Product Dysfunctional Radio antenna Rear view mirrors that can be controlled without opening the windows Fuel Consumption, power Car Brakes, Safety, Smooth Engine Start Must-be Attractive Indif f erence e.g: cigarette lighter One-Dimensional

Market Dynamics and the Kano Model “Delighters” move to “Must Be” features over time! New Delighters must be discovered! Customer Satisfaction Execution Excellence Standard performance INNOVATIVE Leadership performance Competitive performance

7 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Example: Requirements Survey If the phone has SMS text capability, how do you feel? 1.Live 2.Must x 3.Do not care 4.Can live with it 5.Dislike If the phone does not have SMS text capability, how do you feel? 1.Live 2.Must 3.Do not care 4.Can live with it 5.Dislikex Functional form of Question Dys- Functional form of Question

8 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT QuestionResponse If the pen is easy to hold, how do you feel? I like it that way Its must be that way I am neutral I can live with it that way I dislike it that way If the pen is not easy to hold, how do you feel? I like it that way Its must be that way I am neutral I can live with it that way I dislike it that way Based on the response to the two parts of the question, the product feature can be classified into one of six categories: A=Attractive, M=Must-be, O=One-dimensional, I=Indifferent

9 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Customer requirementsCategory of requirement Easy to holdM Easy to carryI Easy to write withM Looks coolA Doesn’t leave smudges on paper or handM Lasts a long timeO Easy to tell that its mineA Categorizing the Requirements A=Attractive, M=Must-be, O=One-dimensional, I=Indifferent

10 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Market Dynamics and the Kano Model “Delighters” move to “Must Be” features over time! COMMODITY Customer Satisfaction Execution Excellence Standard performance INNOVATIVE Leadership performance Competitive performance

11 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Quality Function Deployment Voice of the Engineer “House of the Quality” Voice of the customer correlations Competitive Analysis Technical Comparison

12 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Quality Function Deployment Voice of the Engineer “House of the Quality” Voice of the customer correlations Competitive Analysis Technical Comparison The house quality

13 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Designing for the Customer: The House of Quality Car door Design Easy to close7 Can open on a hill5 Easy to open3 Doesn’t leak in rain3 No road noise2 Importance Weighting Target Values Customer Requirements Engineering Characteristics X=Us A=Comp. A B=Comp. B (5 is Best) xAB xB x x x x x x xAB A AB Technical Evaluation (5 is Best) A A A A B B BB BA X X X X XX X Correlation: ∆ Strong Positive o Positive X Negative * Strong Negative Relationships: ∆ Strong=9 o Medium=3 * Small=1 Customer requirements information forms the basis for this matrix, used to translate them into operating or engineering goals

14 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Design FMEA Analysis (Must be’s Characteristics) Item and Function Potential Failure Mode Potential Effects of Failure SEVSEV Potential Cause(s) of Failure OCCOCC Detection Method & Quality Controls DETDET RPNRPN Recommended Actions List Part Name, Number and Function List the possible modes of failure List the consequen ces of failure on part function and on the next higher assembly List those such as: Inadequa te design, improper materials etc. List these measures Available to, detect failures before they reach the customer List them for each of the failure modes identified as being significant by RPN = Critical characteristic which may effect safety, compliance with Gov. regulations, or require special controls. SEV = Severity rating (1 to 10) OCC = Occurrence frequency (1 to 10) DET = Detection Rating (1 to 10) RPN = Risk Priority Number (1 to 1000)

15 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT RPN / Risk Priority Number Top 20% of Failure Modes by RPN RPNRPN Failure Modes

16 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Customer-Driven Quality cycle Identification of customer needs Translation into product/service specifications (design Quality ) Performance/Output (actual quality) Customer perceptions (Perceived quality) Customer needs and expectations (expected Quality) PERCEIVED QUALITY = ACTUAL - EXPECTED Measurement and feedback

17 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT The Engineering Method and Statistical Thinking Develop a dear description Conduct experime nts Conclusion and recommendations Identify the important factors Propose or refine a model Manipulat e the model Confirm the solution

18 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Physical system Model Experiments Uncertainly MeasurementsAnalysis Figure continuous iteration between model and physical system.

19 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT The Engineering Method and Statistical Thinking Engineering Example The dot diagram is a very useful plot for displaying a small body of data - say up to about 20 observations. This plot allows us to see easily two features of the data; the location, or the middle, and the scatter or variability. Figure: Dot diagram of the pull-off force data when wall thickness is 3/32 inch.

20 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT The Engineering Method and Statistical Thinking Engineering Example The engineer considers an alternate design and eight prototypes are built and pull-off force measured. The dot diagram can be used to visually compare two sets of data = inch = Figure: Dot diagram of the pull-off force data when wall thickness is 3/32 inch.

21 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Data Types Sales (in $1000’s) Atlanta Boston Cleveland Denver Time Series Data Cross Section Data

22 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Data Sources Primary Data collection Secondary Data collection Actual Observation Survey Questioners Designed Experimentation Print or Electronic

23 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Data Summary and Display Definition If the n observations in a sample are denoted by the sample mean is

24 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Data Summary and Display Figure The sample mean as a balance point for a system of weights.

25 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Median = 3 Median Not affected by extreme values In an ordered array, the median is the “middle” number – If n or N is odd, the median is the middle number – If n or N is even, the median is the average of the two middle numbers

26 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Median  For an odd number of observations: The Median is the middle value. Median = 19 7 observations In ascending order

27 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Median  For an even number of observations: The Median is the average of the two middle values. Median = (19+26)/2 = observations In ascending order

28 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Median  Averaging the 35 th and 36 th data values:  Median = ( )/2 = Note: Data is in ascending order.

29 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Mode A measure of central tendency Value that occurs most often Not affected by extreme values Used for either numerical or categorical data There may be no mode There may be several modes Mode = 5No Mode

30 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Mode  450 occurred most frequently (7 times) Mode = Note: Data is in ascending order.

31 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Variation Measures of variation give information on the spread or variability of the data values. Same center, different variation

32 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Range Simplest measure of variation Difference between the largest and the smallest observations: Example: Range = = 13

33 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Range  Apartment Rent Sample Data  Range = Largest value – smallest value Range = 615 – 425 = Note: Data is in ascending order.

34 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT 80 th Percentile Note: Data is in ascending order. Averaging the 56 th and 57 th data values: 80 th Percentile = ( )/2 = 542

35 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT 80 th Percentile “At least 80% Of the items Take on avalue of 542 or less” “At least 20% Of the items Take on a value of 542 or less” 56/70 =.8 or 80%14/70 =.2 or 20%

36 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Interquartile Range  3 rd Quartile (Q3) = 525  1 st Quartile (Q1) = 445  Interquartile Range = Q3 – Q1 = 525 – 445 = 80 Note: Data is in ascending order.

37 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Box Plots 1.5 1QR 1QR Whiskers extends To smallest data Point Within 1.5 Interquartile ranges from first quartile Whiskers extends to largest data point within 1.5 Interquartile ranges from first quartile First quartile Second quartile Third quartile Extreme OutliersOutliers

38 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Table Compressive Strength (in psi) of 80 Aluminum - Lithium Alloy Specimens

39 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Box Plots Strength Figure Box plot for compressive strength data

40 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Data Summary and Display How Does the Sample Variance Measure Variability? x1 x2 x3 x4x5 x6x7 x8 Figure How the sample Variance measures variability through the deviations

41 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Data Summary and Display Definition If is a sample of n observations, the sample variance is The sample standard deviation, s, is the positive square root of the sample variance.

42 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Data Summary and Display TableCalculation of Terms for the Sample Variation and Sample Standard Deviation

43 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Excel output Microsoft Excel descriptive statistics output, using the house price data. House Prices: $2,000, , , ,000 House prices Mean Standard error Median Mode Standard Deviation Sample Variance6.40E+11 Kurtosis Skewness Range Minimum Maximum Sum Count5

44 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT The Engineering Method and Statistical Thinking Physical laws Sample Product Designs Population Statistical interference is one type of reasoning. Types of reasoning Statistical interference

45 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT µ, population mean σ,population standard deviation population sample(x 1, x 2,..., x n ) x, Sample average s, sample standard deviation Relationship between a population and a sample. Observations in a sample are used to draw conclusions about the population

46 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Population Vs. Sample We measure the sample using statistics in order to draw inferences about the parameters of the population. StatisticParameterSample Population Population of Interest

47 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Population Sample …this (bad)…

48 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Population Sample …or this (too bad)…

49 Prof. Indrajit Mukherjee, School of Management, IIT Bombay QUALITY MANAGEMENT Others Convenience Stratified Judgment Non-Probability Samples Probability Samples Simple Random Systematic Stratified Cluster Samples Sampling Techniques