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Forecasting

Forecasting. K.Prasanthi. 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, ?. Forecasting.

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Forecasting

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  1. Forecasting K.Prasanthi

  2. 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. 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, Process of predicting a future event based on historical data 3.7 12.5 9.0

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

  5. Importance of Forecasting 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.

  6. Period of forecasting • 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

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

  8. Qualitative Methods Briefly, the qualitative methods are: Executive Judgment: Opinion of a group of high level experts or managers 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. • .

  9. 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. • .

  10. 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

  11. 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

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

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

  14. 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

  15. 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 ?

  16. 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 ?

  17. 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 ?

  18. 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 ?

  19. 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 ?

  20. 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 ?

  21. 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

  22. 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 ?

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 3a. Exponential Smoothing – Example 1 Ft+1 = Ft + a(At - Ft) i Ai Fi a= This process continues through week 10

  29. 3a. Exponential Smoothing – Example 1 Ft+1 = Ft + a(At - Ft) i Ai Fi a= a= What if the a constant equals 0.6

  30. 3a. Exponential Smoothing – Example 2 Ft+1 = Ft + a(At - Ft) i Ai Fi a= a= What if the a constant equals 0.6

  31. 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.

  32. 3a. Exponential Smoothing – Example 3 Ft+1 = Ft + a(At - Ft) i Ai Fi a= a= What if the a constant equals 0.5

  33. 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

  34. 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%

  35. 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?

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

  37. 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)

  38. 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

  39. 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

  40. 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

  41. Forecast Bias • How can we tell if a forecast has a positive or negative bias? • TS = Tracking Signal • Good tracking signal has low values MAD 30

  42. 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

  43. Exponential Smoothing (continued) • We looked at using exponential smoothing to forecast demand with only random variations Ft+1 = Ft + a (At - Ft) Ft+1 = Ft + a At – a Ft Ft+1 = a At + (1-a) Ft

  44. Exponential Smoothing (continued) • We looked at using exponential smoothing to forecast demand with only random variations • What if demand varies due to randomness and trend? • What if we have trend and seasonality in the data?

  45. Regression Analysis as a Method for Forecasting Regression analysis takes advantage of the relationship between two variables. Demand is then forecasted based on the knowledge of this relationship and for the given value of the related variable. Ex: Sale of Tires (Y), Sale of Autos (X) are obviously related If we analyze the past data of these two variables and establish a relationship between them, we may use that relationship to forecast the sales of tires given the sales of automobiles. The simplest form of the relationship is, of course, linear, hence it is referred to as a regression line.

  46. Formulas y = a + b x where,

  47. Regression – Example y = a+ b X

  48. General Guiding Principles for Forecasting 1. Forecasts are more accurate for larger groups of items. 2. Forecasts are more accurate for shorter periods of time. 3. Every forecast should include an estimate of error. • Before applying any forecasting method, the total system should be understood. • Before applying any forecasting method, the method should be tested and evaluated. 6. Be aware of people; they can prove you wrong very easily in forecasting

  49. FOR JULY 2nd MONDAY • READ THE CHAPTERS ON • Forecasting • Product and service design

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