s Theories of Knowledge and Variation

1. �sTheories of Knowledge and Variation Presented By: Group 6 M. ELLIOT CARTE DANIEL AUSTIN DE BETHUNE MICHAEL D. GOOLSBY AMY GRAY MAIRA PARVEEN MARK RICHARD WILKINSON

2. System of Profound Knowledge Deming described his System of Profound Knowledge in terms of four main concepts Appreciation for a system Knowledge about variation Theory of knowledge Psychology In particular, we will be talking today about Theory of Knowledge Theory of Variation

3. Theory of Knowledge Deming taught that in order for a statement to truly convey knowledge, it must do two things Predict future outcomes Not violate or contradict past observations Along with prediction comes the risk of being inaccurate, usually at a cost

4. A Few Words on Theory Without theory, observations, experience and predictions are useless It is only through theory that we are able to make accurate predictions It is our theories (our understandings and beliefs) that we modify upon analysis of our experiences and other information Deming also stressed the importance of theoryDeming also stressed the importance of theory

5. Theory of Knowledge (cont.) Knowledge can be thought of as a product of several factors our predictions of future experiences or outcomes our experiences or information our observations and analysis of outcomes and how they differ from our predictions how our observations shape our theories and beliefs how our beliefs and theories shape our predictions of future outcomes

7. Truth of Data Statisticians must not be asked to �massage� data sets Applying statistics to a data set does not necessarily make it a statistical analysis Good statistical analysis applied to bad data is useless Statistical analysis must be conducted in a way appropriate for the set of data being analyzed we must know what the data is supposed to measure we must know how the data was gathered

8. Truth of Data (cont.) It is not enough to hand everyone a copy of pages and pages of data and allow everyone to start making their own analysis of what they perceive to be the variations in the data The data must be proven to be true, accurate and taken in context

9. Truth of Data (cont.) Data must be interpreted in a way that is independent of what one perceives to be the crisis at the time Interpretation must be made based first on a theory Only after a theory is in place, may we plan and execute appropriate data gathering Then the data can be analyzed in order to discover variation in the process

10. Truth of Data (cont.) Then, statistical theory can be used to interpret the variation that is revealed by analysis of the data Notice that the proposed means of analysis is well defined and in place before any data is gathered Truth of the data depends not only on this pre-data gathering planning of analysis, but also on the adequate definition of the data set Data that is gathered or analyzed without proper analysis planning or data definition can be said to lack truth

11. A closer Look at Looking at Data When looking at data, we must remember that nothing really has a true value We have is a process for evaluating a situation and a measurement process that helps us estimate data from situational outputs Thus, no way of data calculations can be all right all the time

12. Superstitious Learning Superstitious learning is when Event A occurs Event B occurs It is assumed that event A caused event B Worse yet, is when one then assumes that repeating event A will cause repeated occurrences of event B

13. Superstitious Learning (cont.) Haphazard way of shaping beliefs Could be coincidence Could exhibit correlation without causation It�s very dangerous to blindly act on or modify a process based on, an event that has shown no correlation to a desired outcome It could also be that there is clear correlation Correlation does not imply causation!

14. Theory of Variation In understanding variance we must consider difference between common cause variation and special cause variation causes of variation variation should be considered dependent on the processes of the system, not the workers or other people involved in the system Variation will always exist We should try to reduce, not eliminate variation

15. Understanding Variation Calculating Variance Sigma represents one standard deviation Sigma squared is the variance Mu is the mean x is one data item, or an instance N is the number of items (instances)

16. Variance Approximately 99% - 100% of the values will fall within 3 standard deviations of the mean Approximately 90% - 98% of the values will fall within 2 standard deviations of the mean Approximately 60% - 78% of the values will fall within 1 standard deviation of the mean

17. Variance

18. Variance (an example) To simplify, we�ll use a data range of 1-10 inclusive. Given the following data set, we�ll calculate the variance: 1, 5, 9, 7, 3, 2, 8, 6, 4, 4, 4, 7, 7, 6, 4, 2, 3, 8, 9, 9, 7, 3, 3, 2, 7, 5

19. Variance (an example) 1, 5, 9, 7, 3, 2, 8, 6, 4, 4, 4, 7, 7, 6, 4, 2, 3, 8, 9, 9, 7, 3, 3, 2, 7, 5 First we find the mean by adding all values and then dividing by the number of values. The sum of the values is 135. The number of values is 26 This gives us a mean of approximately 5.1923077

20. Variance (an example) Next, we recall from our formula that we need to take the difference of each value and the mean, square the resulting value and then sum the squares. In other words, we need the sum of the squares of the difference of each value and the mean. See highlighted area of formula.

21. Variance (an example)

22. Variance (an example)

23. Variance (an example)

24. Variance (an example) Now, lets see how many of our data items actually fall within one standard deviation. We recall that the mean was about 5.2 and one standard deviation was about 2.4, so the range that would include all values within one standard deviation of the mean is about 2.8 � 7.6 How many of our values actually fall in this range?

25. Variance (an example) 1, 5, 9, 7, 3, 2, 8, 6, 4, 4, 4, 7, 7, 6, 4, 2, 3, 8, 9, 9, 7, 3, 3, 2, 7, 5 17 of our 26 values fall within one standard deviation. That�s about 65% Recall that we would expect 60% - 78% to fall within one standard deviation.

26. Variance (an example) Typically, the upper and lower control limits of a system�s output are set according to the standard deviation of a sample data set. Usually, the upper control limit is set to the mean plus one standard deviation and the lower control limit is set to the mean minus one standard deviation. This may vary (no pun intended) depending on the system.

27. Variance (an example) Values falling within the control limits usually signify common cause variation.

28. Variance (an example)

29. Where System Variation Occurs System Development System Implementation System Operation System Maintenance

30. Why Variation Occurs (in data) Per in-class discussion Hardware failure Natural disaster Lack of data input control Lack of standards Multiple sources of the same data Ineffective security Unrestricted access to data Inaccurate code

31. Other Ways to Control Variation (in data) Per in-class discussion Disaster recovery Data validation and pre-validation filtering Standardization Eliminate multiple sources of same data Careful coding Tight restrictions on data access Effective system security Restrict use / analysis of data as well as source of data

32. Common Cause VS Special Cause Variation that is inherent in the process. Produced by the interactions �among the variables of the process Collection of variables and their interaction is called the system of common causes There will always be some Common Cause Variation in the system Management may assign teams consisting of internal suppliers, the owner of the process, customers, and others who can contribute knowledge about the common cause system to improve the process

33. Common Cause VS Special Cause Variation in the process that is assignable to a specific cause or causes This variation arises because of special circumstances Management must find the cause for this variation immediately and fix it

34. How Variation Might Occur Common Cause Variation could occur because of a certain characteristic of the system such as: equipment, material, method, environment, or factor related to the workers Special Cause Variation could occur anytime an unexpected situation arises such as a broken machine producing faulty products

35. Reducing Common Cause Variation Understand that the process has an inherent capability which will not change unless the process is changed Identify aspects of the process that contribute to the common cause system Determine which aspect of the process to change Plan the process change

36. Reducing Special Cause Variation Immediately try to understand when a special cause has occurred Determine what was different when the special cause occurred Identify ways to prevent the special cause from recurring

37. Actions to Avoid Doing nothing at all Tampering or responding to change in a process and taking action without understanding the nature of the variation in the process Making fundamental changes in the process