Slides 5 3 project sales or production levels using the roll

Project Sales or Production Levels Using the Rolling Average © 2011 What if? You planned for 10 but… © 2011 Terminal Learning Objective • Task: Project Sales or Production Levels Using the Rolling Average • Condition: You are training to become an ACE with access to ICAM course handouts, readings, and spreadsheet tools 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 © 2011 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 © 2011 Predicting the Future © 2011 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 © 2011 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 © 2011 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: © 2011 Regression Results • Very predictable • The ascending series is y = x + and we can predict that the 7th period would need 11 burgers • The descending series is y = -x + 17 and we can predict that the 7th period would need 10 © 2011 Regression Exercise • Use spreadsheet to predict the 8th, 9th, and 10th event burger demand if the first six demands were: 10 12 13 15 â 2011 10 Graph of Rolling Average This is a time series X-axis represents sequential time periods © 2011 25 Graph of Rolling Average This is a time series X-axis represents sequential time periods © 2011 26 Rolling Average vs Regression This is a time series X-axis represents sequential time periods © 2011 27 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 = © 2011 28 Using Rolling Average to Project Future Demand • Plug in the known values and solve the equation: (Per11 + Per12 + Per13)/3 = (4 + + Per13)/3 = * (4 + + Per13)/3 = * + Per13 = 15 Per13 = © 2011 29 Using Rolling Average to Project Future Demand • Plug in the known values and solve the equation: (Per11 + Per12 + Per13)/3 = (4 + + Per13)/3 = * (4 + + Per13)/3 = * What would regression + Per13 = 15 analysis project? Per13 = Which is “right”? © 2011 30 Rolling Average vs Regression month rolling average suggests an inflection point has changed the trend Regression picks up the long term downward trend, predicting another decrease 13 This is a time series X-axis represents sequential time periods © 2011 31 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 â 2011 32 Learning Check 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? â 2011 33 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 â 2011 34 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 © 2011 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 * 032 = 256 • 7000 * 032 = 224 • 6000 * 032 = 192 Total = 1994 62365 1994/62365 = 032 or 3.2% © 2011 36 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) â 2011 37 Learning Check What are planning factors? • How are planning factors generally expressed? © 2011 38 Practical Exercise © 2011 39 ... + 3) /3 = 5. 7 (8 + + 6) /3 = 5. 7 (3 + + 4) /3 = 4 .3 (6 + + 5) /3 = 5. 0 22 Rolling Average Calculation Period 10 11 12 © 2011 (7 + + 6) /3 = 6.0 (5 + + 8) /3 = 6 .3 (6 + + 3) /3 = 5. 7 (8 + + 6) /3 = 5. 7... 6) /3 = 6.0 (5 + + 8) /3 = 6 .3 (6 + + 3) /3 = 5. 7 (8 + + 6) /3 = 5. 7 (3 + + 4) /3 = 4 .3 (6 + + 5) /3 = 5. 0 21 Rolling Average Calculation Period 10 11 12 © 2011 (7 + + 6) /3 = 6.0 (5 + + 8) /3 = 6 .3 (6... (4 + + 7) /3 = 4.7 (3 + + 5) /3 = 5. 0 Period 10 11 12 © 2011 (7 + + 6) /3 = 6.0 (5 + + 8) /3 = 6 .3 (6 + + 3) /3 = 5. 7 (8 + + 6) /3 = 5. 7 (3 + + 4) /3 = 4 .3 (6 + + 5) /3 = 5. 0 20 Rolling Average Calculation