1. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 1 Demand Forecasting

2. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 2 Objectives Understand the role of forecasting Understand the issues Understand basic tools and techniques

3. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 3 Forecasting Developing predictions or estimates of future values –Demand volume –Price levels –Lead times –Resource availability –...

4. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 4 The Role of Forecasting Necessary Input to all Planning Decisions –Operations: Inventory, Production Planning & Scheduling –Finance: Plant Investment & Budgeting –Marketing: Sales-Force Allocation, Pricing Promotions –Human Resources: Workforce Planning

5. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 5 Demand Forecasting For manufactured items and conventional goods, forecasts are used to determine Replenishment levels and safety stocks Set production plans Determine procurement schedules Capacity planning, financial planning, & workforce planning

6. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 6 Demand Forecasting For services, demand forecasts are used for Capacity planning, workforce scheduling, procurement & budgeting. Because services cannot be stored, demand forecasting for services is often concerned with forecasting the peak demand, rather than the average demand and its range.

7. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 7 Characteristics of Forecasts Forecast are always wrong. A good forecast is more than a single value. Forecast accuracy decreases with the forecast horizon. Aggregate forecasts are more accurate than disaggregated forecasts.

8. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 8 Independent vs. Dependent Demand Independent –Exogenously controlled –Subject to random or unpredictable changes –What we forecast Dependent or Derived –Calculated or derived from other sources –Do not forecast

9. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 9 Forecasting Methods Qualitative or Judgmental –Ask people who ought to know Historical Projection or Extrapolation –Time Series Models Moving Averages Exponential Smoothing –Regression based methods

10. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 10 Basic Approach to Demand Forecasting Identify the Objective of the Forecast Integrate Forecasting with Planning Identify the Factors that Influence the Demand Forecast Identify the Appropriate Forecasting Model Monitor the Forecast (Measure Errors)

11. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 11 Time Series Methods Appropriate when future demand is expected to follow past demand patterns. Future demand is assumed to be influenced by the current demand, as well as historical growth and seasonal patterns.

12. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 12 Time Series Models With time series models observed demand can be broken down into two components: systematic and random. Observed Demand = Systematic Component + Random Component

13. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 13 Time Series Methods The systematic component is the expected demand value. It is comprised of the underlying average demand, the trend in demand, and the seasonal fluctuations (seasonality) in demand.

14. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 14 Idea Behind Time Series Models Distinguish between random fluctuations and true changes in underlying demand patterns.

15. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 15 Time Series Components of Demand Time Demand Random component

16. Monthly chart of the DJIA's changes from month to month along with a 3 period simple moving average. Fall, 2012 16EMBA 512 Demand Forecasting Boise State University

17. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 17 Time Series Methods The random component cannot be predicted. However, its size and variability can be estimated to provide a measure of forecast error. The objective of forecasting is to filter the random component and model (estimate) the systematic component.

18. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 18 Moving Averages Simple, widely used Reduce random noise One Extreme –Prediction next period = Demand this period Another Extreme –Prediction next period = Long run average Intermediate View –Prediction next period = Average of last n periods

19. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 19 Moving Average Models PeriodDemand 112 215 311 4 9 510 6 8 714 812 3-period moving average forecast for Period 8: =(14 + 8 + 10) / 3 =10.67 

20. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 20 Weighted Moving Averages Forecast for Period 8 =[(0.5  14) + (0.3  8) + (0.2  10)] / (0.5 + 0.3 + 0.2) =11.4 What are the advantages? What do the weights add up to? Could we use different weights? Compare with a simple 3-period moving average.

21. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 21 Table of Forecasts and Demand Values... Period Actual Demand Two-Period Moving Average Forecast Three-Period Weighted Moving Average Forecast Weights = 0.5, 0.3, 0.2 112 215 31113.5 491312.4 510 10.8 689.59.9 71498.8 8121111.4 9 1311.8

22. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 22... and Resulting Graph Note how the forecasts smooth out variations

23. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 23 Simple Exponential Smoothing Sophisticated weighted averaging model Needs only three numbers: F t = Forecast for the current period t D t = Actual demand for the current period t  = Weight between 0 and 1

24. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 24 Exponential Smoothing Moving Averages –Equal weight to older observations Exponential Smoothing –More weight to more recent observations Forecast for next period is a weighted average of –Observation for this period –Forecast for this period

25. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 25 Simple Exponential Smoothing Formula F t+1 = F t +  (D t – F t ) =  ×  D t + (1 –  × F t Where did the current forecast come from? What happens as  gets closer to 0 or 1? Where does the very first forecast come from?

26. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 26 Exponential Smoothing Forecast with  = 0.3 F 2 = 0.3×12 + 0.7×11 = 3.6 + 7.7 = 11.3 F 3 = 0.3×15 + 0.7×11.3 = 12.41 Period Actual Demand Exponential Smoothing Forecast 11211.00 (given) 21511.30 31112.41 4911.99 51011.09 6810.76 7149.93 81211.15 9 11.41

27. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 27 Resulting Graph

28. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 28 Time Series with Time Demand random and trend components

29. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 29 Linear Trend

30. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 30 Exponential Trend

31. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 31 Trends What do you think will happen to a moving average or exponential smoothing model when there is a trend in the data?

32. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 32 Simple Exponential Smoothing Always Lags A Trend Because the model is based on historical demand, it always lags the obvious upward trend Period Actual Demand Exponential Smoothing Forecast 11111.00 21211.00 31311.30 41411.81 51512.47 61613.23 71714.06 81814.94 9 15.86

33. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 33 Simple Linear Regression Time Series –Find best fit of proposed model to past data –Project that fit forward Assumes a linear relationship: y = a + b(x) y x

34. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 34 Definitions Y = a + b(X) Y = predicted variable (i.e., demand) X = predictor variable “X” is the time period for linear trend models.

35. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 35 Example: Regression Used to Estimate A Linear Trend Line Period (X) Demand (Y) 1110 2190 3320 4410 5490

36. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 36 Resulting Regression Model: Forecast = 10 + 98×Period

37. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 37 Time series with Demand random, trend and seasonal components June

38. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 38 Trend & Seasonality

39. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 39 Seasonality

40. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 40 Modeling Trend & Seasonal Components Quarter PeriodDemand Winter 071 80 Spring2 240 Summer3 300 Fall4 440 Winter 085 400 Spring6 720 Summer7 700 Fall8 880

41. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 41 What Do You Notice? Forecasted Demand = –18.57 + 108.57 x Period Period Actual Demand Regression Forecast Forecast Error Winter 0718090-10 Spring2240198.641.4 Summer3300307.1-7.1 Fall4440415.724.3 Winter 085400524.3-124.3 Spring6720632.987.2 Summer7700741.4-41.4 Fall888085030

42. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 42 Regression picks up trend, but not the seasonality effect

43. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 43 Calculating Seasonal Index: Winter Quarter (Actual / Forecast) for Winter Quarters: Winter ‘07:(80 / 90) = 0.89 Winter ‘08:(400 / 524.3) = 0.76 Average of these two = 0.83 Interpret!

44. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 44 Seasonally Adjusted Forecast Model For Winter Quarter [ –18.57 + 108.57×Period ] × 0.83 Or more generally: [ –18.57 + 108.57 × Period ] × Seasonal Index

45. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 45 Seasonally Adjusted Forecasts Forecasted Demand = –18.57 + 108.57 x Period Period Actual Demand Regression Forecast Demand/ Forecast Seasonal Index Seasonally Adjusted Forecast Forecast Error Winter 07180900.890.8374.335.67 Spring2240198.61.211.17232.977.03 Summer3300307.10.980.96294.985.02 Fall4440415.71.061.05435.194.81 Winter 085400524.30.760.83433.02-33.02 Spring6720632.91.141.17742.42-22.42 Summer7700741.40.940.96712.13-12.13 Fall88808501.041.05889.84-9.84

46. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 46 Would You Expect the Forecast Model to Perform This Well With Future Data?

47. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 47 The Perfect (Imaginary) Forecast

48. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 48 A More Realistic Forecast

49. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 49 Forecast Error Building a Forecast –Fit to historical data –Project future data Forecast Error –How well does model fit historical data –Do we need to tune or refine the model –Can we offer confidence intervals about our predictions

50. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 50 Forecast Error The forecast error measures the difference between the actual demand and the forecast of demand. The forecast is based on the systematic component and the random component is estimated based on the forecast error. Forecast Error = Actual – Forecast

51. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 51 Measures of Forecast Accuracy Forecast Error t (E t )= Demand t -Forecast t Mean Squared Error (MSE) Mean Absolute Deviation (MAD) Bias Tracking Signal Relative Forecast Errors

52. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 52 Mean Squared Error (MSE) The MSE estimates the variance of the forecast error.

53. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 53 Mean Absolute Deviation (MAD) The MAD can be used to estimate the standard deviation of the random component, assuming the random component is normally distributed: σ = 1.25MAD

54. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 54 Bias To determine whether a forecasting method consistently over-or- underestimates demand, calculate the sum of the forecast errors:

55. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 55 Tracking Signal The tracking signal (TS) is the ratio of the bias to the MAD. Tracking signals outside the range + 6 indicates that the forecast is biased and either under predicting (negative) or over predicting (positive) demand.

56. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 56 Forecast Accuracy & Demand Variability (Normally Distributed Demand) Coefficient of Variation Probability Demand is Within 25% of the Forecast 0.10 98.76% 0.2568.27% 0.5038.29% 0.7526.11% 1.0019.74% 1.5013.24% 2.009.95% 3.006.64%

57. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 57 Forecasting is a necessary evil, try to reduce the need for it. Complexity costs money, does it provide better forecasts? Aggregation provides accuracy, but precludes local information Forecast the right thing Issues

58. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 58 Forecasting Success Story Taco Bell

59. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 59 Taco Bell Labor is 30% of revenue Make to order environment Significant “seasonality” –52% of days sales during lunch –25% of days sales during busiest hour Balance staff with demand Feed the dog

60. Fall, 2012 EMBA 512 Demand Forecasting Boise State University 60 Value Meals Drove demand Forecasting system in each store –forecasts arrivals within 15 minute intervals Simulation system –“predicts” congestion and lost sales Optimization system –Finds the minimum cost allocation of workers

61. Fall, 2011 EMBA 512 Demand Forecasting Boise State University 61 Forecasting System Customer arrivals by 15-minute interval of day (e.g., 11:15-11:30 am Friday) Fed by in-store computer system 6-week moving average Estimated savings: Over $40 Million in 3 years.