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Forecasting Supply Chain Requirements

Forecasting Supply Chain Requirements. I hope you'll keep in mind that economic forecasting is far from a perfect science. If recent history's any guide, the experts have some explaining to do about what they told us had to happen but never did. Ronald Reagan, 1984. Chapter 8.

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Forecasting Supply Chain Requirements

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  1. Forecasting Supply Chain Requirements I hope you'll keep in mind that economic forecasting is far from a perfect science. If recent history's any guide, the experts have some explaining to do about what they told us had to happen but never did. Ronald Reagan, 1984 Chapter 8 CR (2004) Prentice Hall, Inc.

  2. Inventory Strategy Inventory Strategy Forecasting Forecasting • • Transport Strategy Transport Strategy • • Inventory decisions Inventory decisions • • Transport fundamentals Transport fundamentals • • Purchasing and supply Purchasing and supply • • Transport decisions Transport decisions Customer Customer scheduling decisions scheduling decisions service goals service goals • • Storage fundamentals Storage fundamentals • • The product The product • • Storage decisions Storage decisions ORGANIZING ORGANIZING PLANNING PLANNING CONTROLLING CONTROLLING • • Logistics service Logistics service • • Ord Ord . proc. & info. sys. . proc. & info. sys. Location Strategy Location Strategy • • Location decisions Location decisions • • The network planning process The network planning process Forecasting in Inventory Strategy CR (2004) Prentice Hall, Inc.

  3. What’s Forecasted in the Supply Chain? • Demand, sales or requirements • Purchase prices • Replenishment and delivery times CR (2004) Prentice Hall, Inc.

  4. Some Forecasting Method Choices • Historical projection • Moving average • Exponential smoothing • Causal or associative • Regression analysis • Qualitative • Surveys • Expert systems or rule-based • Collaborative CR (2004) Prentice Hall, Inc.

  5. Typical Time Series Patterns: Random CR (2004) Prentice Hall, Inc.

  6. 250 200 Sales 150 100 Actual sales Average sales 50 0 0 5 10 15 20 25 Time Typical Time Series Patterns: Random with Trend CR (2004) Prentice Hall, Inc.

  7. Typical Time Series Patterns: Random with Trend & Seasonal CR (2004) Prentice Hall, Inc.

  8. Sales Time Typical Time Series Patterns: Lumpy CR (2004) Prentice Hall, Inc.

  9. Is Time Series Pattern Forecastable? Whether a time series can be reasonably forecasted often depends on the time series’ degree of variability. Forecast a regular time series, but use other techniques for lumpy ones. How to tell the difference: Rule A time series is lumpy if where regular, otherwise. CR (2004) Prentice Hall, Inc.

  10. Moving Average Basic formula where i = time period t = current time period n = length of moving average in periods Ai = demand in period i CR (2004) Prentice Hall, Inc.

  11. Example 3-Month Moving Average Forecasting Total demand 3-month Demand for during past 3 moving Month, i month, i months average . . . . . . . . . . . . 20 120 . . 21 130 360/3 120 22 110 380/3 126.67 23 140 360/3 120 24 110 380/3 126.67 25 130 26 ? CR (2004) Prentice Hall, Inc.

  12. = + + + MA w A w A ... w A 1 1 2 2 n n n å = where w 1 i = 1 i If weights ( w ) are exponential in form, then = a + a - a 1 MA A ( 1 ) A - 1 t t + a - a + a - a 2 3 ( 1 ) A ( 1 ) A - - 2 3 t t + + a - a n ... ( 1 ) A - Weighted Moving Average t n which reduces to the basic, level only, exponential smoothing formula = = a + - a MA F A ( 1 ) F + 1 t t t where a = smoothing constant usually 0.01 to 0.30 = F forecast for next period + 1 t = A actual demand in current period t = F forecast in current period t

  13. IV. Forecast error I. Level only N   - | A F | F = A + (1- )F å t+1 t t t t = 1 t MAD = N II. Level and trend or N 2 a a - (A F ) S = A + (1- )(S + T ) å t t t t t-1 t-1 = t 1 = S F N T = ß(S - S ) + (1-ß)T t t t-1 t-1 @ and S 1.25MAD. F F = S + T t+1 t t III. Level, trend, and seasonality a a S = (A /I ) + (1- )(S + T ) t t t-L t-1 t-1 g g I = (A /S ) + (1- )I t t t t-L T = ß(S - S ) + (1-ß)T t t t-1 t-1 F = (S + T )I t+1 t t t-L+1 where L is the time period of one full seasonal cycle. Exponential Smoothing Formulas CR (2004) Prentice Hall, Inc.

  14. Example Exponential Smoothing Forecasting Time series data Quarter 1 2 3 4 Last year 1200 700 900 1100 This year 1400 1000 ? Getting started Assume  = 0.2. Average first 4 quarters of data and use for previous forecast, say Fo CR (2004) Prentice Hall, Inc.

  15. Example (Cont’d) Begin forecasting First quarter of 2nd year Second quarter of 2nd year CR (2004) Prentice Hall, Inc.

  16. Example (Cont’d) Third quarter of 2nd year Summarizing Quarter 1 2 3 4 Last year 1200 700 900 1100 This year 1400 1000 ? Fore- cast 1000 1080 1064 CR (2004) Prentice Hall, Inc.

  17. Example (Cont’d) Measuring forecast error as MAD or RMSE (std. error of forecast) 1 degree of freedom lost in level-only model, but 2 in level-trend and 3 in level-trend-seasonal models CR (2004) Prentice Hall, Inc.

  18. Example (Cont’d) Using SF and assuming n=2 NoteTo compute a reasonable average for SF, n should range over at least one seasonal cycle in most cases. CR (2004) Prentice Hall, Inc.

  19. Example (Cont’d) Range of the forecast If forecast errors are normally distributed and the forecast is at the mean of the distribution, i.e., , a forecast confidence band can be computed. The error distribution for the level-only model results is: Bias should be 0 or close to it in a model of good fit Range S = 408 F F3=1064 8-19 CR (2004) Prentice Hall, Inc.

  20. = ± Y F z ( S ) 3 F = ± 1064 1 . 96 ( 408 ) = ± 1064 800 Example (Cont’d) From a normal distribution table, z@95%=1.96. The actual time series value Y for quarter 3 is expected to range between: or 264  Y  1864 CR (2004) Prentice Hall, Inc.

  21. Correcting for Trend in ES The trend-corrected model is St =At  (1 – )(St-1  Tt-1) Tt = (St – St-1)  (1 – )Tt-1 Ft+1 = St Tt where S is the forecast without trend correction. Assuming  = 0.2,  = 0.3, S-1= 975, and T-1= 0 Forecast for quarter 1 of this year S0= 0.2(1100)  0.8(975 + 0) = 1000 T0= 0.3(1000 – 975)  0.7(0) = 8 F1= 1000  8 = 1008 CR (2004) Prentice Hall, Inc.

  22. Correcting for Trend in ES (Cont’d) Forecast for quarter 2 of this year S0T0 S1 = 0.2(1400)  0.8(1000  8) = 1086.4 T1 = 0.3(1086.4 – 1000)  0.7(8) = 31.5 F2 = 1086.4  31.5 = 1117.9 Forecast for quarter 3 of this year S2 = 0.2(1000)  0.8(1086.4  31.5) = 1094.3 T2 = 0.3(1094.3 – 1086.4)  0.7(31.5) = 24.4 F3 = 1094.3  24.4 = 1118.7, or 1119 CR (2004) Prentice Hall, Inc.

  23. Correcting for Trend in ES (Cont’d) Summarizing with trend correction Quarter 1 2 3 4 Last year 1200 700 900 1100 This year 1400 1000 ? Fore- cast 1008 1118 1119 CR (2004) Prentice Hall, Inc.

  24. Fore- cast error a 0 1 Optimizing  for ES Minimize average forecast error 8-24 CR (2004) Prentice Hall, Inc.

  25. Controlling Model Fit in ES Tracking signal monitors the fit of the model to detect when the model no longer accurately represents the data where the Mean Squared Error (MSE) is n is a reasonable number of past periods depending on the application If tracking signal exceeds a specified value (control limit), revise smoothing constant(s). 8-25 CR (2004) Prentice Hall, Inc.

  26. Classic Time Series Decomposition Model Basic formulation F = TSCR where F = forecast T = trend S = seasonal index C = cyclical index (usually 1) R = residual index (usually 1) Some time series data Quarter 1 2 3 4 Last year 1200 700 900 1100 This year 1400 1000 ? CR (2004) Prentice Hall, Inc.

  27. Classic Time Series Decomposition Model (Cont’d) Trend estimation Use simple regression analysis to find the trend equation of the form T = a  bt. Recall the basic formulas: and CR (2004) Prentice Hall, Inc.

  28. Classic Time Series Decomposition Model (Cont’d) Redisplaying the data for ease of computation. 2 t Y Yt t 1 1200 1200 1 2 700 1400 4 3 900 2700 9 4 1100 4400 16 5 1400 7000 25 6 1000 6000 36 2 t=21 Y=6300 Yt=22700 t =91 å å å å CR (2004) Prentice Hall, Inc.

  29. Classic Time Series Decomposition Model (Cont’d) Hence, and then T = 920.01  27.14t Forecast for 3rd quarter of this year is: T = 920.01  37.14(7) = 1179.99 CR (2004) Prentice Hall, Inc.

  30. Classic Time Series Decomposition Model (Cont’d) Compute seasonal indices The procedure is to form a ratio of actual demand to the estimated demand for a full seasonal cycle (4 quarters). One way is as follows. Seasonal t Y T Index, S t 1 1200 957.15* 1.25** 2 700 994.29 0.70 3 900 1031.43 0.87 4 1100 1068.57 1.03 *T=920.01  37.14(1)=957.15 **St=1200/957.15=1.25 CR (2004) Prentice Hall, Inc.

  31. Classic Time Series Decomposition Model (Cont’d) Compute seasonal indices Since C and R indexvalues are usually 1, the adjusted seasonal forecast for the 3rd quarter of this year would be: F7 = 1179.99 x 0.87 = 1026.59 Forecast range The standard error of the forecast is: A degree of freedom is lost for the a and b values in forecast equation CR (2004) Prentice Hall, Inc.

  32. Classic Time Series Decomposition Model (Cont’d) Tabled computations Qtr t Y T S F t t t t 1 1 1200 957.15 1.25 2 2 700 994.29 0.70 3 3 900 1031.43 0.87 4 4 1100 1068.57 1.03 1 5 1400 1105.71 1.27 1404.25* 2 6 1000 1142.85 0.88 1005.71** 3 7 1179.99 1026.59 *1105.71x1.27=1404.25 **1142.85x0.88=1005.71 CR (2004) Prentice Hall, Inc.

  33. Classic Time Series Decomposition Model (Cont’d) There is inadequate data to make a meaningful estimate of SF. However, we would proceed as follows: Normally, a larger sample size would be used giving a positive value for SF Then, Ftz(SF)  Y Ft  z(SF) CR (2004) Prentice Hall, Inc.

  34. Regression Analysis Basic formulation F = o  1X1  2X2 … nXn Example Bobbie Brooks, a manufacturer of teenage women’s clothes, was able to forecast seasonal sales from the following relationship F = constant 1(no. nonvendor accounts)  2(consumer debt ratio) CR (2004) Prentice Hall, Inc.

  35. Regression Forecasting Using Bobbie Brooks Sales Data (1) (2) (3) (4) (5) (6)= (2)/(5) Sales period Time Sales (Dt ) Trend value Seasonal Forecast  Dt t period, t ($000s) t2 (Tt ) index ($000s) Summer 1 $9,458 1 $12,053 0.78 9,458 Trans-season 2 11,542 23,084 4 12,539 0.92 Fall 3 14,489 43,467 9 13,025 1.11 Holiday 4 15,754 63,016 16 13,512 1.17 Spring 5 17,269 86,345 25 13,998 1.23 Summer 6 11,514 69,084 36 14,484 0.79 Trans-season 7 12,623 88,361 49 14,970 0.84 Fall 8 16,086 128,688 64 15,456 1.04 Holiday 9 18,098 162,882 81 15,942 1.14 Spring 10 21,030 210,300 100 16,428 1.28 Summer 11 12,788 140,668 121 16,915 0.76 Trans-season 12 16,072 192,864 144 17,401 0.92 * Fall 13 ? 17,887 $18,602 * Holiday 14 ? 18,373 20,945 Totals 78 176,723 1,218,217 650 å = = = = å 176 723 12 14 726 92 78 12 6 5 N = 12 Dt t = 1,218,217 t2 = 650 ( , / ) , . / . ´ Regression equation is: Tt= 11,567.08 + 486.13t *Forecasted values 8-35 CR (2004) Prentice Hall, Inc.

  36. (1) (2) (3)= (4)= 1.0/(2) (3)/48.09 Percent Inverse of Model Forecast of total error Model type error error proportion weights MJ 9.0 0.466 2.15 0.04 R 0.7 0.036 27.77 0.58 ES 1.2 0.063 15.87 0.33 1 ES 8.4 0.435 2.30 0.05 2 Total 19.3 1.000 48.09 1.00 Combined Model Forecasting Combines the results of several models to improve overall accuracy. Consider the seasonal forecasting problem of Bobbie Brooks. Four models were used. Three of them were two forms of exponential smoothing and a regression model. The fourth was managerial judgement used by a vice president of marketing using experience. Each forecast is then weighted according to its respective error as shown below. Calculation of forecast weights CR (2004) Prentice Hall, Inc.

  37. (1) (2) (3)= ´ (1) (2) Forecast Model Weighting Weighted type forecast factor proportion Regression model (R) $20,367,000 0.58 $11,813,000 Exponential Smoothing ES 20,400,000 0.33 6,732,000 1 Combined exponential smoothing-- regression model 17,660,000 0.05 883,000 (ES ) 2 Managerial judgment (MJ) 19,500,000 0.04 780,000 $20,208,000 Weighted average forecast Combined Model Forecasting (Cont’d) Weighted Average Fall Season Forecast Using Multiple Forecasting Techniques CR (2004) Prentice Hall, Inc.

  38. Multiple Model Errors 8-38 CR (2004) Prentice Hall, Inc.

  39. Actions When Forecasting is Not Appropriate • Seek information directly from customers • Collaborate with other channel members • Apply forecasting methods with caution (may work where forecast accuracy is not critical) • Delay supply response until demand becomes clear • Shift demand to other periods for better supply response • Develop quick response and flexible supply systems CR (2004) Prentice Hall, Inc.

  40. Collaborative Forecasting • Demand is lumpy or highly uncertain • Involves multiple participants each with a unique perspective—“two heads are better than one” • Goal is to reduce forecast error • The forecasting process is inherently unstable CR (2004) Prentice Hall, Inc.

  41. Collaborative Forecasting: Key Steps • Establish a process champion • Identify the needed Information and collection processes • Establish methods for processing information from multiple sources and the weights assigned to multiple forecasts • Create methods for translating forecast into form needed by each party • Establish process for revising and updating forecast in real time • Create methods for appraising the forecast • Show that the benefits of collaborative forecasting are obvious and real CR (2004) Prentice Hall, Inc.

  42. Managing Highly Uncertain Demand • Delay forecasting as long as possible • Prioritize supply by product’s degree of uncertainty (supply to the more certain products first) • Apply the principle of postponement to the most uncertain products (delay committing to a final product form until an order is received) • Create flexible supply to changing demand (alter capacity and output rates through subcontracting, computer technology, multi-purpose processes, etc.) • Be able to respond quickly to uncertain demand levels CR (2004) Prentice Hall, Inc.

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