1. © 2007 Pearson Education 8- 1 Managing Quality Integrating the Supply Chain S. Thomas Foster Chapter 8 Data Analyses Using Pivot Tables 10/11 – 5:30PM

2. © 2007 Pearson Education 8- 2 Data Analysis Using Pivot Tables Chapter 8 How do you perform pivot table analyses? PO/OMD/A80-20DC/DCO/SSCQ/S1/SW4H2 1. Identify a problem or opportunity for which you want to specify the scope (i.e., Who? What? When? Where? How? How much?). 2. Identify the relevant organizational categorical variables. These are variables which you cannot manipulate with mathematical operators. These variables organize the context. Categorical variables are organizational entities and are not measurements.

3. © 2007 Pearson Education 8- 3 Data Analysis Using Pivot Tables Chapter 8 3. Identify the relevant measurement variables. These are variables which you can manipulate with mathematical operators. These variables measure the organizational categorical variables. They are the dimensions of the categorical variables.

4. © 2007 Pearson Education 8- 4 Data Analysis Using Pivot Tables Chapter 8 4. Identify the relevant data. You extract just the values for the organizational categorical and measurement variables which are relevant to your research question about the context. In other words, if you have 15 departments and three of those departments are causing 80% of your problems, you must extract and analyze the data for each department separately. The departments may have similar symptoms but they will not necessarily have similar causes and solutions. Be careful about treating different things the same.

5. © 2007 Pearson Education 8- 5 Data Analysis Using Pivot Tables Chapter 8 5. Repeatedly do aggregate analyses on the organizational categorical variables until you get down to the lowest level, the level at which you can physically correct or improve a process. You are trying to identify the 20% of the organization which is causing 80% of the problem. 6. Calculate the distribution percentage and the cumulative percentage to help identify the 20% of the organizational categorical variables causing 80% of the problem or opportunity.

6. © 2007 Pearson Education 8- 6 Data Analysis Using Pivot Tables Chapter 8 7. Then do an aggregate analysis on each of the operational categorical variables using the data for the operational categorical variable at the lowest level. For example, employee #, part #, line #, machine #, shift #, day of the week, hour of the day, and work station # are examples of operational categorical variables. You want to retain for further analyses any of the operational categorical variables which show an 80/20 relationship.

7. © 2007 Pearson Education 8- 7 Data Analysis Using Pivot Tables Chapter 8 8. Calculate the distribution percentage and the cumulative percentage for these operational categorical variables to help identify the 20% causing the 80% of the problem or opportunity. 9. Using the data for the operational categorical variables showing 80/20 relationships, do segmentation and stratification analyses across every combination of paired operational categorical variables, which have an 80/20 aggregate analysis, to identify patterns, clusters, between the operational categorical variables.

8. © 2007 Pearson Education 8- 8 Data Analysis Using Pivot Tables Chapter 8 10. Use Excel’s conditional formatting to mark values with red or green to show the bad or good values, respectively, within either the first or fourth quartile. You are trying to identify which of the operational categorical variables, with an 80/20 analysis, relate to each other, as evidenced by patterns. If your red or green cells cluster together, then you have a pattern which means that you have at least a coincidental relationship between these two categorical operational variables. If your red or green cells do not cluster together, then the operational variables do not have a relationship.

9. © 2007 Pearson Education 8- 9 Data Analysis Using Pivot Tables Chapter 8 11. Summarize your results with a single conclusion. The best way to make a point is to make just one point, not a thousand points. This conclusion specifies the scope of the problem (i.e., who, what, when, where, how, and how much) when you go to the workplace to find out why this problem is occurring. If you do not define the scope of the problem with the above analysis, when you go to the workplace to observe the problem and ask questions, you can easily be distracted and confused with the resident experts’ assumptions, anecdotes, heuristics, hearsay, opinions, and pseudo facts.

10. © 2007 Pearson Education 8- 10 Data Analysis Using Pivot Tables Chapter 8 What kind of patterns can we expect to find in our segmentation and stratification analyses of the variables which showed an 80/20 concentration in our aggregate analyses? We examine all combinations of paired categorical variables, which show an 80/20 concentration in our aggregate analyses, in our segmentation and stratification analyses. In the matrix, we want to see if the top 20% or the bottom 20%, whichever is our concern, cluster together or if they are randomly distributed throughout the matrix. If they cluster together, then the scope of the problem is narrowed with regard to who, what, when, where, how, and how much.

11. © 2007 Pearson Education 8- 11 Data Analysis Using Pivot Tables Chapter 8 What do we do if we find patterns, clusters in our segmentation and stratification analyses? When you find patterns, clusters of high or low values, you will have specified the scope of the problem as to who, what, when, where, how, and how much with regard to the operational categorical variables. Next, you need to go observe the process and ask questions of the people performing the process to understand why these patterns are occurring.

12. © 2007 Pearson Education 8- 12 Data Analysis Using Pivot Tables Chapter 8 In other words, the reason why the problem is occurring is related to who, what, when, where, how, and how much with regard to the operational categorical variables. But your data analyses of reports, interviews, surveys, data, and statistics are not going to show you why the problem is occurring. Your data analyses just provides you with the evidence you need to support your focus on a few operational categorical variables at a specific point in a specific process to save you time when you go see the process. You will use the PDCA method to identify the specific cause of the problem or the specific process improvement needed.