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Forecasting

Forecasting. Operation research. Course Structure. Introduction. Operations Strategy & Competitiveness. Quality Management. Strategic Decisions (some). Design of Products and Services. Process Selection and Design. Capacity and Facility Decisions. Forecasting.

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Forecasting

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  1. Forecasting Operation research

  2. CourseStructure Introduction Operations Strategy & Competitiveness Quality Management Strategic Decisions (some) Design of Products and Services Process Selection and Design • Capacity and • Facility Decisions • Forecasting • Tactical & Operational Decisions

  3. Forecasting • Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, ? b) 2.5, 4.5, 6.5, 8.5, 10.5, ? c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, ?

  4. Forecasting • Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, b) 2.5, 4.5, 6.5, 8.5, 10.5, c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, 3.7 12.5 9.0

  5. Outline • What is forecasting? • Types of forecasts • Time-Series forecasting • Naïve • Moving Average • Exponential Smoothing • Regression • Good forecasts

  6. What is Forecasting? • Process of predicting a future event based on historical data • Educated Guessing • Underlying basis of all business decisions • Production • Inventory • Personnel • Facilities

  7. Why do we need to forecast? In general, forecasts are almost always wrong. So, Throughout the day we forecast very different things such as weather, traffic, stock market, state of our company from different perspectives. Virtually every business attempt is based on forecasting. Not all of them are derived from sophisticated methods. However, “Best" educated guesses about future are more valuable for purpose of Planning than no forecasts and hence no planning.

  8. Importance of Forecasting in OM Departments throughout the organization depend on forecasts to formulate and execute their plans. Finance needs forecasts to project cash flows and capital requirements. Human resources need forecasts to anticipate hiring needs. Production needs forecasts to plan production levels, workforce, material requirements, inventories, etc.

  9. Importance of Forecasting in OM Demand is not the only variable of interest to forecasters. Manufacturers also forecast worker absenteeism, machine availability, material costs, transportation and production lead times, etc. Besides demand, service providers are also interested in forecasts of population, of other demographic variables, of weather, etc.

  10. Types of Forecasts by Time Horizon Quantitative methods • Short-range forecast • Usually < 3 months • Job scheduling, worker assignments • Medium-range forecast • 3 months to 2 years • Sales/production planning • Long-range forecast • > 2 years • New product planning Detailed use of system Design of system Qualitative Methods

  11. Forecasting During the Life Cycle Introduction Growth Maturity Decline Quantitative models Qualitative models • - Executive judgment • - Market research • Survey of sales force • Delphi method - Time series analysis - Regression analysis Sales Time

  12. Qualitative Forecasting Methods Qualitative Forecasting Models Sales Force Composite Delphi Method Executive Judgement Market Research/ Survey Smoothing

  13. Qualitative Methods • Briefly, the qualitative methods are: • Executive Judgment: Opinion of a group of high level experts or managers is pooled • Sales Force Composite: Each regional salesperson provides his/her sales estimates. Those forecasts are then reviewed to make sure they are realistic. All regional forecasts are then pooled at the district and national levels to obtain an overall forecast. • Market Research/Survey: Solicits input from customers pertaining to their future purchasing plans. It involves the use of questionnaires, consumer panels and tests of new products and services. .

  14. Qualitative Methods • Delphi Method: As opposed to regular panels where the individuals involved are in direct communication, this method eliminates the effects of group potential dominance of the most vocal members. The group involves individuals from inside as well as outside the organization. • Typically, the procedure consists of the following steps: • Each expert in the group makes his/her own forecasts in form of statements • The coordinator collects all group statements and summarizes them • The coordinator provides this summary and gives another set of questions to each group member including feedback as to the input of other experts. • The above steps are repeated until a consensus is reached. .

  15. Quantitative Forecasting Methods Quantitative Forecasting Regression Time Series Models Models 2. Moving 3. Exponential 1. Naive Average Smoothing a) simple b) weighted a) level b) trend c) seasonality

  16. Quantitative Forecasting Methods Quantitative Forecasting Regression Time Series Models Models 2. Moving 3. Exponential 1. Naive Average Smoothing a) simple b) weighted a) level b) trend c) seasonality

  17. Time Series Models • Try to predict the future based on past data • Assume that factors influencing the past will continue to influence the future

  18. Time Series Models: Components Trend Random Seasonal Composite

  19. Product Demand over Time Demand for product or service Year 2 Year 3 Year 4 Year 1

  20. Product Demand over Time Trend component Seasonal peaks Demand for product or service Actual demand line Random variation Year 2 Year 3 Year 4 Year 1 Now let’s look at some time series approaches to forecasting… Borrowed from Heizer/Render - Principles of Operations Management, 5e, and Operations Management, 7e

  21. Quantitative Forecasting Methods Quantitative Time Series Models Models 2. Moving 3. Exponential 1. Naive Average Smoothing a) simple b) weighted a) level b) trend c) seasonality

  22. 1. Naive Approach • Demand in nextperiod is the same as demand in most recentperiod • May sales = 48 → • Usually not good June forecast = 48

  23. 2a. Simple Moving Average • Assumes an average is a good estimator of future behavior • Used if little or no trend • Used for smoothing Ft+1 = Forecast for the upcoming period, t+1 n = Number of periods to be averaged A t = Actual occurrence in period t

  24. 2a. Simple Moving Average You’re manager in Amazon’s electronics department. You want to forecast ipod sales for months 4-6 using a 3-period moving average. Sales (000) Month 1 4 2 6 3 5 4 ? 5 ? 6 ?

  25. 2a. Simple Moving Average You’re manager in Amazon’s electronics department. You want to forecast ipod sales for months 4-6 using a 3-period moving average. Sales (000) Moving Average Month (n=3) NA 1 4 NA 2 6 NA 3 5 (4+6+5)/3=5 4 ? 5 ? 6 ?

  26. What if ipod sales were actually 3 in month 4 2a. Simple Moving Average Sales (000) Moving Average Month (n=3) NA 1 4 NA 2 6 NA 3 5 5 ? 4 3 5 ? 6 ?

  27. Forecast for Month 5? 2a. Simple Moving Average Sales (000) Moving Average Month (n=3) NA 1 4 NA 2 6 NA 3 5 5 4 3 (6+5+3)/3=4.667 5 ? 6 ?

  28. Actual Demand for Month 5 = 7 2a. Simple Moving Average Sales (000) Moving Average Month (n=3) NA 1 4 NA 2 6 NA 3 5 5 4 3 4.667 ? 5 7 6 ?

  29. Forecast for Month 6? 2a. Simple Moving Average Sales (000) Moving Average Month (n=3) NA 1 4 NA 2 6 NA 3 5 5 4 3 4.667 5 7 (5+3+7)/3=5 6 ?

  30. 2b. Weighted Moving Average • Gives more emphasis to recent data • Weights • decrease for older data • sum to 1.0 Simple moving average models weight all previous periods equally

  31. 2b. Weighted Moving Average: 3/6, 2/6, 1/6 Weighted Moving Average Month Sales (000) NA 1 4 NA 2 6 NA 3 5 31/6 = 5.167 4 ? 5 ? 6 ?

  32. 2b. Weighted Moving Average: 3/6, 2/6, 1/6 Weighted Moving Average Month Sales (000) NA 1 4 NA 2 6 NA 3 5 31/6 = 5.167 4 3 25/6 = 4.167 5 7 6 32/6 = 5.333

  33. 3a. Exponential Smoothing • Assumes the most recent observations have the highest predictive value • gives more weight to recent time periods Ft+1 = Ft + a(At - Ft) et Need initial forecast Ft to start. Ft+1 = Forecast value for time t+1 At = Actual value at time t  = Smoothing constant α value ranging between 0 and 1. α coefficient value is determined by trial and error. Where it is appropriate to reach a value of α by selecting two or three values for α. Then measure the prediction accuracy.

  34. 3a. Exponential Smoothing – Example 1 Ft+1 = Ft + a(At - Ft) Given the weekly demand data what are the exponential smoothing forecasts for periods 2-10 using a=0.10? Assume F1=D1 i Ai

  35. 3a. Exponential Smoothing – Example 1 Ft+1 = Ft + a(At - Ft) i Ai Fi a= F2 = F1+ a(A1–F1) =820+.1(820–820) =820

  36. 3a. Exponential Smoothing – Example 1 Ft+1 = Ft + a(At - Ft) i Ai Fi a= F3 = F2+ a(A2–F2) =820+.1(775–820) =815.5

  37. 3a. Exponential Smoothing – Example 1 Ft+1 = Ft + a(At - Ft) i Ai Fi a= This process continues through week 10

  38. 3a. Exponential Smoothing – Example 1 Ft+1 = Ft + a(At - Ft) i Ai Fi a= a= What if the a constant equals 0.6

  39. 3a. Exponential Smoothing – Example 2 Ft+1 = Ft + a(At - Ft) i Ai Fi a= a= What if the a constant equals 0.6

  40. 3a. Exponential Smoothing – Example 3 Company A, a personal computer producer purchases generic parts and assembles them to final product. Even though most of the orders require customization, they have many common components. Thus, managers of Company A need a good forecast of demand so that they can purchase computer parts accordingly to minimize inventory cost while meeting acceptable service level. Demand data for its computers for the past 5 months is given in the following table.

  41. 3a. Exponential Smoothing – Example 3 Ft+1 = Ft + a(At - Ft) i Ai Fi a= a= What if the a constant equals 0.5

  42. 3a. Exponential Smoothing • How to choose α • depends on the emphasis you want to place on the most recent data • Increasing α makes forecast more sensitive to recent data

  43. Forecast Effects ofSmoothing Constant  Weights = Prior Period  2 periods ago (1 - ) 3 periods ago (1 - )2 = 0.10 = 0.90 Ft+1 = Ft +  (At - Ft) or Ft+1 =  At + (1- ) At - 1 + (1- )2At - 2 + ... w1 w2 w3 8.1% 9% 10% 90% 9% 0.9%

  44. To Use a Forecasting Method • Collect historical data • Select a model • Moving average methods • Selectn (number of periods) • For weighted moving average: selectweights • Exponential smoothing • Select • Selections should produce a good forecast …but what is a good forecast?

  45. A Good Forecast • Has a small error • Error = Demand - Forecast

  46. Measures of Forecast Error et MAD = Mean Absolute Deviation • MSE = Mean Squared Error RMSE = Root Mean Squared Error • Ideal values =0 (i.e., no forecasting error)

  47. MAD Example = 40 4 =10 What is the MAD value given the forecast values in the table below? At Ft |At – Ft| Month Sales Forecast 1 220 n/a 5 2 250 255 5 3 210 205 20 4 300 320 5 325 315 10 = 40

  48. MSE/RMSE Example = 550 4 =137.5 What is the MSE value? RMSE = √137.5 =11.73 At Ft |At – Ft| (At – Ft)2 Month Sales Forecast 1 220 n/a 5 25 2 250 255 5 25 3 210 205 20 400 4 300 320 5 325 315 10 100 = 550

  49. Measures of Error 1. Mean Absolute Deviation (MAD) 84 = 14 6 -16 16 2a. Mean Squared Error (MSE) 1 1 -1 100 -10 10 1,446 = 241 17 17 289 6 -20 20 400 2b. Root Mean Squared Error (RMSE) -10 84 1,446 An accurate forecasting system will have small MAD, MSE and RMSE; ideally equal to zero. A large error may indicate that either the forecasting method used or the parameters such as α used in the method are wrong. Note: In the above, n is the number of periods, which is 6 in our example = SQRT(241) =15.52

  50. Forecast Bias • Forecast Bias occurs when there are consistent differences between actual outcomes and previously generated forecasts of those quantities; that is: forecasts may have a general tendency to be too high or too low. • How can we tell if a forecast has a positive or negative bias? • TS = Tracking Signal • Good tracking signal has low values MAD 30

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