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Forecasting the Demand Those who do not remember the past are condemned to repeat it

Forecasting the Demand Those who do not remember the past are condemned to repeat it George Santayana (1863-1952) a Spanish philosopher, essayist, poet and novelist. Goals and Aims.

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Forecasting the Demand Those who do not remember the past are condemned to repeat it

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  1. Forecasting the Demand Those who do not remember the past are condemned to repeat it George Santayana (1863-1952) a Spanish philosopher, essayist, poet and novelist

  2. Goals and Aims Operations managers need forecasting for their Design tasks as well as their Operational tasks. We will learn • Why do we need forecasting? • What is forecasting? • Qualitative (judgmental) forecasting. • Quantitative forecasting. • Time series analysis • Naïve method • Moving average • Exponential smoothing • Regression analysis • How to measure forecasting errors?

  3. Uses of Forecast in Planning, Scheduling, and Decision Making

  4. Easy to use Written Meaningful unit Elements of a good forecast To have enough time to make the required decisions based on forecasts Timely Close to reality Close to actual data Accuracy over time Consistency in forecasts Accurate Reliable

  5. Common features of forecasts • Forecasts rarely perfect because of randomness • Forecast should be accompanied by a measure of forecast error • Forecasts are more accurate forgroups of items vs. individual items • Forecast accuracy decreases as time horizon increases I see that you willget an A this semester.

  6. Types of Forecasts • Judgmental- uses subjective inputs. Qualitative • Time series - uses historical data to develop a function to forecast demand of our products over time. A relationship between demand and time. • Regression - Create a relationship between demand of our products and value of one or more variables

  7. Judgmental forecasts • Executive opinions • Sales force composite • Consumer surveys • Outside opinion • Delphi technique

  8. Delphi technique • A questionnaire is send to individuals who have knowledge and ability in the area • Responses are kept anonymous • A new questionnaire is developed based on the information extracted from the previous questionnaire. • The process starts over • The process stops when they reach an consensus forecast

  9. Delphi technique One reason for using the Delphi method in forecasting is to avoid premature consensus (bandwagon effect)

  10. Time series analysis Find a relationship between demand and time Demand Time

  11. Irregularvariation Trend- long-term movement in data Randomvariation Cycles - Long-term variations (5 to 10 yrs) Seasonality - short-term cycles (seasons, weeks, days, hours) forecast variations Irregular variations- caused by unusual circumstances Random variations- caused by chance

  12. Techniques for time series forecasting • Naïve forecasts • Moving Averages • Exponential Smoothing

  13. Naive forecasts We sold 250 wheels last week.... Now, next week we should sell.… 250 wheels F(t+1) = At At : Actual demand in period t F(t+1) : Forecast of demand for period t+1 The naive forecast can also serve as an accuracy standard for other techniques

  14. Moving Average Three period moving average in period say 7 is the average of MA73 = (A7+ A6+ A5)/3 Three period moving average in period t is the average of MAt3 = (At+ At-1+ At-2)/3 Ten period moving average in period t is the average of MAt10 = (At+ At-1+ At-2 +At-3+ ….+ At-9)/10

  15. Forecast Using Moving Average n period moving average in period t is the average of MAtn = (At+ At-1+ At-2 +At-3+ ….+ At-n+1)/n Forecast for period t+1 is equal to moving average for period t Ft+1 =MAtn Ft+1 =MAtn = (At+ At-1+ At-2 +At-3+ ….+ At-n+1)/n

  16. Actual data; taking into account Which one take into account more elements of the actual data,? a 4-period moving average or a 7-period moving average? MAt4 = (At+ At-1 + At-2+ At-3)/4 MAt7 = (At+ At-1+ At-2 +At-3+ ….+ At-6)/7

  17. Moving Average, t to t+1 Suppose we are in period 20, and we have already computed 4-period and 7-period moving averages. The actual demand for period 21 is 800 unit. From now on, which one needs to write more data in a file or store more data on computer, 4-periods or 7-period MA?

  18. 4 period moving average at period 20, and 21 The Actual Demand for periods 17-20 are The Actual Demand for period 21 is 800 MA420 = (A20+A19+A18+A17)/4 MA421 = (A21+A20+A19+A18)/4 MA420 = (658+864+1110+634)/4 = 816.5 MA421 = (800+658+864+1110)/4=858

  19. 4 period moving average at period 20, and 21 MA420 = (658+864+1110+634)/4 = 816.5 MA421 = (800+658+864+1110)/4=858 MA420 = (658+864+1110) /4 +634/4 = 816.5 MA421 = 800/4 + (658+864+1110)/4 = 858 MA421 = 816.5 +(800- 634) /4=858 MA421 = MA420 +(A21- A17)/4

  20. 7 period moving average at period 20, and 21 Actual Demand for period 21 is 800 MA720 = (658+864+1110+634+855+738+910)/7 = 824.14 MA721 = (800+658+864+1110+634+855+738)/7=808.43

  21. 7 period moving average at period 20, and 21 MA720 = (658+864+1110+634+855+738+910)/7 = 824.14 MA721 = (800+658+864+1110+634+855+738)/7=808.43 MA720 = (658+864+1110+634+855+738 )/7+ (910)/7 = 824.14 MA721 = (800)/7+ (658+864+1110+634+855+738)/7 = 808.43 MA721 = 824. 14 +(800- 910)/7=808.43 MA721 = MA720 +(A21- A14)/7

  22. Which One MA721 = MA720 +(A21- A14)/7 MA721 = 824. 14 +(800- 910) /7=808.43 MA421 = MA420 +(A21- A17)/4 MA421 = 816.5 +(800- 634) /4=858 Same Computations

  23. Actual data; storing Which one needs to write more data in a file or store more data in Computer, 4 periods or 7 period MA? MAt = MAt-1 +(At - At-n)/n MAt4 = MA4t-1 +(At - At-4 )/4 MAt7 = MA7t-1 +(At - At-7)/7

  24. Moving average The relationship between MAt and MAt-1 can be easily computed for any moving average MAt = (At+ At-1+…………+ At-n+2+ At-n+1)/n MAt-1 = ( At-1 + At-2…......…... At-n+1+ At-n)/n

  25. Moving average MAt = (At+ At-1+…………+ At-n+2+ At-n+1)/n MAt-1 = ( At-1 + At-2…......…... At-n+1+At-n)/n MA(t+1) = MAt + (At+1 - At-n)/n Moving average is then used to forecast for the next period. In moving average forecasting F(t+1) = MAt F22= MA21

  26. Comparison If we are in period 20, and 4-period moving average in period 20 is MA420 = 816.5. Then our forecast for period 21 is F21 = MA420 = 816.5 Our forecast for period 22 is F22 = MA21 If the actual demand in period 21 is 800, and the actual demand in period 17 was 634 MA22 = 816.5+(800-634)/4 = 858. F22 = MA21 = 858

  27. 4 and 7 period moving average Which one create a more smooth forecasting curve? A 4 period or a 7 period moving average. Increase the number of periods; increase the smoothness of a forecast Decrease the number of periods; increase the responsiveness of a forecast

  28. Micro $oft Stock

  29. Exponential smoothing

  30. Exponential smoothing

  31. Practice The president of State University wants to forecast student enrollments for this academic year based on the following historical data: 5 years ago 15,000 4 years ago 16,000 3 years ago 18,000 2 years ago 20,000 Last year 21,000 What is the forecast for this year using exponential smoothing with alpha = 0.4, if the forecast for two years ago was 16,000?

  32. Practice t 1 2 3 4 5 At 15000 16000 18000 20000 21000 17600 Ft 16000 Forecast for last year F5 = (1-α)F4+α(A4) F5 = .6(16000)+.4(20000)=17600 Forecast for this year F6 = (1-α)F5+α(A5) F6 = .6(17600)+.4(21000)=18960

  33. Smoothing constant .2 .05

  34. Exponential smoothing and Moving Average Which one take into account more elements of the actual data,? a 100-period moving average, or an exponential smoothing?

  35. How many piece of data are involved in ES

  36. Exponential Smoothing α=.2 1 100 F1 t At Ft Since I have no information for F1, I just enter A1 which is 100 A1  F1

  37. Exponential Smoothing α=.2 1 100 100 2 100 t At Ft F2 =(1- α)F1 + α A1 F2 =(1- .2)100 + .2(100) F2 =80 + 20 = 100 A1 & F1  F2 A1  F1 A1  F2

  38. Exponential Smoothing α=.2 1 100 100 2 100 3 110 t At Ft 150 F3 =(1- α)F2 + α A2 F3 =.8(100) + .2(150) F3 =80 + 30 = 110 F2 & A2  F3 A1  F2 A1 & A2  F3

  39. Exponential Smoothing α=.2 3 110 1 100 100 2 150 100 4 112 t At Ft 120 F4 =(1- α)F3 + α A3 F4 =.8(110) + .2(120) F4 =88 + 24 = 112 A3 & F3  F4 A1 & A2  F3 A1& A2 & A3  F4

  40. Exponential Smoothing F2 =A1 F3 =(1- α)F2 + α A2 F3 =(1- α)A1 + α A2 F4 =(1- α)F3 + α A3 F4 =(1- α)[(1- α)A1 + α A2] + α A3 F4 =(1- α)2 A1 + α(1- α) A2 + α A3 F4 =(.8)2 A1 + .2(.8) A2 + .2 A3 F4 =(.64) A1 + (.16) A2 + .2 A3 F4 =(.64) 100 + (.16) 150 + .2(120) F4 =64 + 24 + 24= 112

  41. Forecast accuracy • Error - difference between actual value and predicted value • Mean absolute deviation (MAD) • Tracking Signal (TS)

  42. Mean Absolute Deviation (MAD)   Actual Forecast MAD = n

  43. (Actual - forecast) Tracking signal = MAD  (Actual - forecast) Tracking signal =  Actual - forecast n Ratio of cumulative error and MAD Tracking signal  (Actual - forecast) Tracking signal = n  Actual - forecast

  44. Tracking signal Detecting non-randomness in errors can be done using Control Charts (UCL and LCL) Tracking Signal UCL Time LCL

  45. Tracking signal Tracking Signal UCL Time LCL

  46. Associative (Causal) Forecasting The primary method for associative forecasting is Regression Analysis. The term most closely relates to associative forecasting techniques is predictor variables The predictor variable in simple linear regression is the independent variable

  47. Regression method Computedrelationship Least squares line minimizes sum of squared deviations around the line

  48. Regression method

  49. Regression: Tools / Data Analysis / Regression

  50. Regression: X and Y ranges

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