1. Project Sales Or Production Levels Using The Rolling Average © Dale R. Geiger 20111

2. What if? You planned for 10 but… © Dale R. Geiger 2011

3. Terminal Learning Objective Task: Project Sales Or Production Levels Using The Rolling Average Condition: You are a cost advisor technician with access to all regulations/course handouts, and awareness of Operational Environment (OE)/Contemporary Operational Environment (COE) variables and actors Standard: with at least 80% accuracy Demonstrate understanding of Trend Projection concepts © Dale R. Geiger 20113

4. Importance of Demand We have seen how demand drives cost Flexible forecasting Assumptions about probabilities may not yield useful information “Precisely wrong” Examining trends gives another perspective on demand © Dale R. Geiger 20114

5. Predicting the Future © Dale R. Geiger 20115

6. What is Trend Projection? Uses historical data about past demand to make estimates of future demand Relies on systematic methodologies and assumptions Cannot predict the future or anticipate catastrophic events © Dale R. Geiger 20116

7. Three Methods Regression Represents a straight line with the least squared error from actual Rolling average Uses average of prior period demand to predict future period demand Planning factors Assumes a relationship between a current value and future demand © Dale R. Geiger 20117

8. Regression Analysis Plots a linear relationship between multiple data points Minimizes the “squared errors” Square difference between mean and actual to eliminate negative values Uses the format y = mx + b where: © Dale R. Geiger 2011 8

9. Regression Results Very predictable The ascending series is y = x + 4 and we can predict that the 7 th period would need 11 burgers The descending series is y = -x + 17 and we can predict that the 7 th period would need 10 © Dale R. Geiger 20119

10. Regression Exercise Use spreadsheet to predict the 8 th, 9 th, and 10 th event burger demand if the first six demands were: 8 10 9 12 13 15 © Dale R. Geiger 201110

11. Spreadsheet Exercise © Dale R. Geiger 201111 The spreadsheet returns the equation: y = 1.3429x + 6.4667 The spreadsheet returns the equation: y = 1.3429x + 6.4667 Enter the data as shown Enter the values in the spreadsheet to predict demand Per. 8 demand = 17 Enter the values in the spreadsheet to predict demand Per. 8 demand = 17

12. Regression Analysis © Dale R. Geiger 201112

13. Example: Using Regression to Estimate Fixed and Variable Costs Consider four quarters of data Regression returns y = 2.2x +13.7 13 Q1Q2Q3Q4 Units5678 Total Cost25272832 Fixed cost is 13.7 Variable cost is 2.2 per unit Total cost is 13.7 + 2.2*units Fixed cost is 13.7 Variable cost is 2.2 per unit Total cost is 13.7 + 2.2*units

14. Regression Analysis © Dale R. Geiger 201114 Notice that four very different sets of data all have very similar regression lines The x-axis in these graphs represents time periods in series

15. Regression Strengths and Weaknesses Can be calculated very precisely But cumbersome to do by hand(use spreadsheet!) May be precisely wrong Can be used to identify trends But by definition cannot predict downturns or upturns Assumes relationship is linear and will remain linear © Dale R. Geiger 201115

16. Check on Learning In the context of trend projection, what does the regression line represent? What is the main weakness of regression in trend projection? © Dale R. Geiger 201116

17. Rolling Average Uses average of prior periods to predict future periods Evens out highs and lows by using a number of periods Key assumption for predictions: Assumes that the average will be maintained Example: Average of Periods 2, 3 & 4 will equal average of periods 1, 2 & 3 © Dale R. Geiger 201117

18. Rolling Average Calculation The demand for our last twelve periods has been: Task: Calculate the 3-month rolling average for periods 3-12 © Dale R. Geiger 201118

19. Rolling Average Calculation The 3-month rolling average is the average value for the most recent 3 months Per1 + Per2 + Per3 3 Add the most recent period to the calculation and drop the oldest Per2 + Per3 + Per4 3 © Dale R. Geiger 201119

20. Rolling Average Calculation © Dale R. Geiger 201120 Period 1not enough data 2 not enough data 3(6 + 8 + 4)/3 = 6.0 4(8 + 4 + 3)/3 = 5.0 5(4 + 3 + 7)/3 = 4.7 6(3 + 7 + 5)/3 = 5.0 Period 7(7 + 5 + 6)/3 = 6.0 8 (5 + 6 + 8)/3 = 6.3 9(6 + 8 + 3)/3 = 5.7 10(8 + 3 + 6)/3 = 5.7 11(3 + 6 + 4)/3 = 4.3 12(6 + 4 + 5)/3 = 5.0

21. Rolling Average Calculation © Dale R. Geiger 201121 Period 7(7 + 5 + 6)/3 = 6.0 8 (5 + 6 + 8)/3 = 6.3 9(6 + 8 + 3)/3 = 5.7 10(8 + 3 + 6)/3 = 5.7 11(3 + 6 + 4)/3 = 4.3 12(6 + 4 + 5)/3 = 5.0

22. Rolling Average Calculation © Dale R. Geiger 201122 Period 7(7 + 5 + 6)/3 = 6.0 8 (5 + 6 + 8)/3 = 6.3 9(6 + 8 + 3)/3 = 5.7 10(8 + 3 + 6)/3 = 5.7 11(3 + 6 + 4)/3 = 4.3 12(6 + 4 + 5)/3 = 5.0

23. Rolling Average Calculation © Dale R. Geiger 201123 Period 7(7 + 5 + 6)/3 = 6.0 8 (5 + 6 + 8)/3 = 6.3 9(6 + 8 + 3)/3 = 5.7 10(8 + 3 + 6)/3 = 5.7 11(3 + 6 + 4)/3 = 4.3 12(6 + 4 + 5)/3 = 5.0

24. Rolling Average Calculation © Dale R. Geiger 201124 Period 1not enough data 2 not enough data 3(6 + 8 + 4)/3 = 6.0 4(8 + 4 + 3)/3 = 5.0 5(4 + 3 + 7)/3 = 4.7 6(3 + 7 + 5)/3 = 5.0 Period 7(7 + 5 + 6)/3 = 6.0 8 (5 + 6 + 8)/3 = 6.3 9(6 + 8 + 3)/3 = 5.7 10(8 + 3 + 6)/3 = 5.7 11(3 + 6 + 4)/3 = 4.3 12(6 + 4 + 5)/3 = 5.0

25. Graph of Rolling Average © Dale R. Geiger 201125 This is a time series. X-axis represents sequential time periods

26. Graph of Rolling Average © Dale R. Geiger 201126 This is a time series. X-axis represents sequential time periods

27. Rolling Average vs. Regression © Dale R. Geiger 201127 This is a time series. X-axis represents sequential time periods

28. Using Rolling Average to Project Future Demand Assume that the previous rolling average will be maintained Our forecast for period 13 will assume a rolling average of 5, same as period 12 (Per11 + Per12 + Per13)/3 = 5 © Dale R. Geiger 201128

29. Using Rolling Average to Project Future Demand Plug in the known values and solve the equation: (Per11 + Per12 + Per13)/3 = 5 (4 + 5 + Per13)/3 = 5 3 * (4 + 5 + Per13)/3 = 5 * 3 9 + Per13 = 15 Per13 = 6 © Dale R. Geiger 201129

30. Using Rolling Average to Project Future Demand Plug in the known values and solve the equation: (Per11 + Per12 + Per13)/3 = 5 (4 + 5 + Per13)/3 = 5 3 * (4 + 5 + Per13)/3 = 5 * 3 9 + Per13 = 15 Per13 = 6 © Dale R. Geiger 201130 What would regression analysis project? Which is “right”? What would regression analysis project? Which is “right”?

31. Rolling Average vs. Regression © Dale R. Geiger 201131 This is a time series. X-axis represents sequential time periods 13 3 month rolling average suggests an inflection point has changed the trend Regression picks up the long term downward trend, predicting another decrease

32. Rolling Average Strengths and Weaknesses Can be calculated very precisely But may be precisely wrong Simple to calculate The main strength of rolling averages is that they dampen the effect of short term changes This helps decision makers avoid knee jerk responses to changes in demand that may not be significant Decision makers are often looking for inflection points An inflection point in a six month rolling average carries a lot of weight © Dale R. Geiger 201132

33. Check on Learning What would be the equation for a six-month rolling average calculation? What is the primary assumption when using rolling average to project future demand? © Dale R. Geiger 201133

34. Planning Factors Assume some cause and effect relationship If we suspect that demand for education counseling decreases when a unit deploys We could study the history of that relationship and determine a planning factor (or ratio) of sessions per soldier as “a” We could then use that factor to plan for the drop in session demand when X soldiers deploy as New demand = a*X © Dale R. Geiger 201134

35. Planning Factor Example Given the recent history determine the planning factor relating sessions and soldiers Use that factor to predict sessions as population goes to 8000 7000 6000 © Dale R. Geiger 201135

36. Planning Factor Example Given the recent history determine the planning factor relating sessions and soldiers Use that factor to predict sessions as population goes to 8000 *.032 = 256 7000 *.032 = 224 6000 *.032 = 192 © Dale R. Geiger 201136 Total = 1994 62365 1994/62365 =.032 or 3.2%

37. Leading Indicators Leading indicators are similar to planning factors with a couple differences Leading indicators often have a weaker cause and effect relationship Changes in consumer confidence index may foreshadow an increase in sales at the post exchange There is a period of time before the effect is seen (i.e. that’s why they are called leading indicators) © Dale R. Geiger 201137

38. Check on Learning What are planning factors? How are planning factors generally expressed? © Dale R. Geiger 201138

39. Practical Exercise © Dale R. Geiger 201139