Introduction

1. Introduction • Name • Work/educational experience • Background/classes taken in math, quality, continuous improvement, statistics, SPC, designed experiments • Expectations

2. Chapter 1

3. What is quality? Quality = Performance Expectation Exceeding customer expectations Producing the best results Fitness for use Total customer satisfaction Excellent products or services Conformance to specifications

4. Kano Model The Kano model relates three factors to their degree of implementation or level of implementation, as shown in the diagram. 1) Basic ("must be") factors 2) Performance ("more is better") factors 3) Delighter ("excitement") factors. The degree of customer satisfaction ranges from disgust, through neutrality, to delight.

5. … what about other factors? • Quality • Cost • Delivery • Responsiveness • Safety

6. Start Station 1 Station 2 Station 3 Customer Exercise: Why control a process? • Each team will be handed 20 cards. • Each team will have three operators, each of whom will drop one card at a time onto a target area. • The method of drop will be to hold the card at arms length over the target area or not. Only those cards that fall completely within the target area may move on. • The goal is to deliver 20 completed products or units to the customer. • Metrics- • # of good units per station (A) • # of cards used per station (B) • Total time of exercise (C) • Total # of defects (D)

7. Exercise cont… A = # of good units B = # of cards A1= B1= A2= B2= A3= B3= FPY: Y1 = A1/B1 = Y2 = A2/B2 = Y3 = A3/B3 = RTY = Y1 * Y2 * Y3 = Total Cost = ($10 * D) + ($2 *[B1+B2+B3])= Average cost per unit = Total cost / 20 = Average cycle time = C / 20 =

8. a.k.a. LeanSigma RTY chart

9. Interesting Quote “Quality control by statistical methods is now so extensively applied in all lines of industry, and in all sections of the United States, that everyone who is interested in manufacturing should also have a definite interest in the methods.” -Control Charts, E.S. Smith - 1947

10. What is SPC? • First, it is nothing new. Developed in the 1920’s. • Description: Involves the use of statistical signals to identify sources of variation, to maintain or improve performance to a higher quality level Process Control Statistical

11. Why SPC? • Quality is a must • Detection mentality is out the door • Must build quality in • Quality is a part of all job functions

12. The Cost of QualityPreventative Costs: • Marketing research • Customer surveys • Field trials • Supplier quality planning • SPC • Process Control • Training • Quality Planning • Design Review • Quality system audits • Continuous improvement • Technical data review • Process validation

13. Output Continue or Improve Inspect with SPC Analyze The Prevention Model • The use of statistical signals to maintain or improve the process Process Shipment Figure 1.3

14. Output Inspect with SPC Improve Analyze Exercise: Prevention Model Let’s build a prevention model for taking a college course. _________ _________ _________ _________ _______ _______ _______ _______ _______ _______

15. The Cost of Poor QualityAppraisal Costs: • Inspection and Test Materials • Set-up Inspections and Tests • Depreciation Allowances • Measurement Equipment Expense • Maintenance and Calibration Labor • Outside Endorsements and Certifications • External Appraisal Costs • Field Performance Evaluations • Special Product Evaluations • Evaluations of Field Stock and Spare Parts • Process Control Measurement • Purchasing Appraisal Costs • Receiving/Incoming Inspection and Test • Measurement Equipment • Qualification of Supplier Product • Source Inspection and Control Programs • Manufacturing Appraisal Costs • Planned Inspections, Tests, Audits • Checking Labor • Product or Service Quality Audits • Review of Test and Inspection data • Other Quality Evaluations • Laboratory Support

16. The Detection Model • An attempt to achieve quality by inspecting the quality into the product through 100% inspection Repair/ Rework Process Inspection Shipment Scrap or waste Figure 1.2 What happens when your process improves?

17. Exercise: Detection Model Let’s build a detection model for taking a college course. ______________ ______________ ______________ ______________ ______________

18. Appraisal exercise The defects in this story appear as the letter “S.” How many defects or S’s (capital or lower case) can you detect in the story below? Bubba-Gump Shrimp Company It was a simple test to sample the number of shrimp secured by each of the fishing vessels after passing the second level of serious FDA inspection techniques. Susan was the first of several student inspectors to have the occasion to assay the new sampling system. Susan first separated seventy random sized shrimp from the sample received from the FDA. Those seventy were then weighed on a special scale. Susan then posted this weight on a job log. The sample of seventy shrimp is then returned to the same FDA sample. The weight of the seventy shrimp is then sent to the large sample scale were the first sample of shrimp has been assembled. The sample from the fishing vessel is then weighed and a series of automatic calculations determines the best number of shrimp in the sample from the comparison of Susan’s seventy shrimp sample.

19. Reliability on visual inspection methods? 1. How many S's did you find in the story after reading it only once? ___________________________________________________ 2. How many S's did you find in the story after reading it through the second time? ___________________________________________________ 3. What was your range from your first to second reading? ___________________________________________________ 4. How is this similar to “real life” inspection systems? ___________________________________________________ ___________________________________________________ 5. What is the most cost-effective way to not have non-conformances pass through the system? __________________________________________________ ___________________________________________________ See example 1.1, page 6, in the text book

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21. The Cost of Poor QualityFailure Costs (internal or external): • Scrap • Rework • Customer Complaint Investigation • Returned Goods • Retrofit Costs • Recall Costs • Warranty Claims • Liability Costs • Penalties • Customer/User Goodwill • Other Failure Costs

22. Goals of SPC • Minimize cost • Attain a consistent process • Allow everyone to contribute to process improvement • Help to make economical decisions

23. How? • Use the seven basic tools • Flowchart • Pareto chart • Checksheet • Cause-and-effect diagram • Histogram • Control Chart • Scatterplot

24. How to apply SPC to a process • Analyze where SPC should be done • Work on decreasing any obvious variability • Gage R&R • Make sampling plan • Create control chart – allow only common cause variability • Run the process • Calculate process capability • Improve if necessary or control process • Pre-control • Continue to improve

25. What is DOE? • DOE – Design of Experiments or referred as Designed Experiments • A systematic change to process variables to find the best combination to produce a quality product Output Input Process

26. Why use DOE ? Will help to gain knowledge in: • Improving process performance • Reducing costs • Understanding relationships between variables • Understanding how to optimize processes

27. Let’s start with an example:

28. Histogram of MPG: What causes variation in fuel economy? DOE is about discovering and quantifying the magnitude of cause and effect relationships. We need DOE because intuition can be misleading.

29. What about other factors - and noise?

30. Let’s talk about regression • To explain how we can model data experimentally, let’s take another look at the mileage data and see if there’s a factor that might explain some of the variation. • Draw a scatter diagram for the following data:

31. Mileage data with vehicle weight: The variable called ‘weight’ is known as a ‘factor’ and is plotted on the x-axis. The variable called ‘mileage’ is known as the response, and is plotted on the y-axis. It’s sometimes called ‘Y’.

32. Scatter Diagram Form:

33. Yours might look something like:

34. Regression analysis • If you draw a best fit line and figure out an equation for that line, you would have a ‘model’ that represents the data.

35. Looking at correlation from a Scatter diagram: ‘Correlation’ is a fancy word for how well the model predicts the response from the factors.

36. Is there really an effect?

37. Understanding a system There are basically two ways to understand a process you are working on. • One factor at a time (OFAT) • DOE Each have their advantages and disadvantages. We’ll talk about each.

38. Why DOE an OFAT example • To illustrate the need for experimental design, let’s consider how two known (based on years of experience) factors affect gas mileage, tire size (T) and fuel type (F).

39. One–at–a-time design Step 1: Select two levels of tire size and two kinds of fuels. Step 2: Holding fuel type constant, test the car at both tire sizes.

40. One–at–a-time design Since we want to maximize mpg the more desirable response happened with T2. Step 3: Holding tire size at T2, test the car at both fuel types.

41. One–at–a-time design At first glance the ideal setting looks like F2 and T2 at 40mpg. However this experimental method did not test the interaction effect of tire size and fuel type.

42. One–at–a-time design Suppose that the untested combination F2T1 would produce the results below. There is a different slope so there appears to be an interaction. A more appropriate design would be to test all four combinations.

43. What about the other factors - and noise?

44. What we need to answer • We need a way to investigate the relationship(s) between variables • We need to distinguish the effects of variables from each other (and maybe tell if they interact with each other) • We need to be able to quantify the effects... ...so we can predict, control, and optimize processes.

45. Obtain the maximum amount of information using a minimum amount of resources Determine which factors shift the average response, which shift the variability and which have no effect Build empirical models relating the response of interest to the input factors Objectives of an Experimental Design

46. So how do we do it? PLANNING DESIGN ANALYSIS CONFIRMATION

47. DOE to the rescue!! DOE uses purposeful changes of the inputs (factors) in order to observe corresponding changes in the output (response). Use an IPO – they are real important here.

48. Here’s what one looks like:

49. (-,+) (+,+) High (+) Factor B Settings In tabular form, it would look like: Low (-) Run A B (-,-) (+,-) 1 - - 2 - + 3 + - Low (-) High (+) 4 + + Factor A Settings The basics • To ‘design’ an experiment, means to pick the points that you’ll use for a scatter diagram.

50. Average Y when A was set ‘high’ Average Y when A was set ‘low’ Measuring an “Effect” Okay - a little math must be done. But a computer helps to keep it simple. The difference in the average Y when A was ‘high’ from the average Y when A was ‘low’ is the ‘factor effect’ The differences are calculated for every factor in the experiment.