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Supply Chain Management (SCM) Forecasting 1

Supply Chain Management (SCM) Forecasting 1. Dr. Husam Arman. Today’s Outline. Forecasting: What? Why? Characteristics of forecasts Classification of forecasting techniques Quantitative methods – Time series methods MA, WMA, ES and TES. Forecasting. What?

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Supply Chain Management (SCM) Forecasting 1

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  1. Supply Chain Management (SCM) Forecasting 1 Dr. Husam Arman

  2. Today’s Outline • Forecasting: What? Why? • Characteristics of forecasts • Classification of forecasting techniques • Quantitative methods – Time series methods • MA, WMA, ES and TES

  3. Forecasting • What? • The process of predicting the future • Why? • All the business planning is based to some extent on forecasting • Sales of new and existing products • Customer demand patterns • Needs and availability of raw materials, capacity requirements, workforce, resources… • Mostly used by Marketing and Production • What about finance, HR, MIS?

  4. Independent Demand: Finished Goods Dependent Demand: Raw Materials, Component parts, Sub-assemblies, etc. A C(2) B(4) D(2) E(1) D(3) F(2) Demand Management

  5. Independent Demand: What a firm can do to manage it. • Can take an active role to influence demand. • Can take a passive role and simply respond to demand.

  6. Quantity Time Patterns of Demand Figure 12.1

  7. Quantity Time (a) Horizontal: Data cluster about a horizontal line. Patterns of Demand Figure 12.1

  8. Quantity Time (b) Trend: Data consistently increase or decrease. Patterns of Demand Figure 12.1

  9. Year 1 Quantity | | | | | | | | | | | | J F M A M J J A S O N D Months (c) Seasonal: Data consistently show peaks and valleys. Patterns of Demand Figure 12.1

  10. Year 1 Quantity Year 2 | | | | | | | | | | | | J F M A M J J A S O N D Months Patterns of Demand Figure 12.1 (c) Seasonal: Data consistently show peaks and valleys.

  11. Quantity | | | | | | 1 2 3 4 5 6 Years (c) Cyclical: Data reveal gradual increases and decreases over extended periods. Patterns of Demand Figure 12.1

  12. Factors affecting Demand • External • Economic activities • Customer • Competitors • Internal ?

  13. Characteristics of Forecasting • They are usually wrong • More than a single number • Aggregate forecasts are more accurate • The longer the forecast horizon, the less accurate the forecast will be • Not to be used to the extension of known information

  14. Time Horizon Medium Term Long Term Short Term (3 months– (more than Application (0–3 months) 2 years) 2 years) Forecast quantity Decision area Forecasting technique Demand Forecast Applications Table 12.1

  15. Time Horizon Medium Term Long Term Short Term (3 months– (more than Application (0–3 months) 2 years) 2 years) Forecast quantity Individual products or services Decision area Inventory management Final assembly scheduling Workforce scheduling Master production scheduling Forecasting Time series technique Causal Judgment Demand Forecast Applications Table 12.1

  16. Time Horizon Medium Term Long Term Short Term (3 months– (more than Application (0–3 months) 2 years) 2 years) Forecast quantity Individual Total sales products or Groups or families services of products or services Decision area Inventory Staff planning management Production Final assembly planning scheduling Master production Workforce scheduling scheduling Purchasing Master production Distribution scheduling Forecasting Time series Causal technique Causal Judgment Judgment Demand Forecast Applications Table 12.1

  17. Time Horizon Medium Term Long Term Short Term (3 months– (more than Application (0–3 months) 2 years) 2 years) Forecast quantity Individual Total sales Total sales products or Groups or families services of products or services Decision area Inventory Staff planning Facility location management Production Capacity Final assembly planning planning scheduling Master production Process Workforce scheduling management scheduling Purchasing Master production Distribution scheduling Forecasting Time series Causal technique Causal Judgment Judgment Judgment Demand Forecast Applications Table 12.1

  18. Classification of forecasting techniques Forecasting Methods Qualitative Quantitative Causal models Economic Indicators Time series methods Scenario writing Moving Averages Market research Sales force composites Naive Smoothing Delphi Methods Historical analogy Seasonal Group forecasting Long term Hybrid methods Short term

  19. Time series methods • ‘Naive’  only info from the past • ‘Time series’  observations at discrete points in time • Key assumption: pattern of the past will continue in the future  isolate patterns • Patterns: • Trend • Seasonality • Cycle • Randomness

  20. Simple Moving Average Formula • The simple moving average model assumes an average is a good estimator of future behavior. • The formula for the simple moving average is: Ft = Forecast for the coming period N = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods

  21. 450 — 430 — 410 — 390 — 370 — Patient arrivals | | | | | | 0 5 10 15 20 25 30 Time-Series MethodsSimple Moving Averages Patient arrivals Week Example 12.2

  22. Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Time-Series MethodsSimple Moving Averages Patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Figure 12.5

  23. 450 — 430 — 410 — 390 — 370 — Patient arrivals Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsSimple Moving Averages Example 12.2

  24. 450 — 430 — 410 — 390 — 370 — Patient Week Arrivals 1 400 2 380 3 411 Patient arrivals Actual patient arrivals Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsSimple Moving Averages Example 12.2

  25. 450 — 430 — 410 — 390 — 370 — Patient Week Arrivals 1 400 2 380 3 411 Patient arrivals Actual patient arrivals Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsSimple Moving Averages Example 12.2

  26. 450 — 430 — 410 — 390 — 370 — Patient Week Arrivals 1 400 2 380 3 411 Patient arrivals 411 + 380 + 400 3 F4 = Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Example 12.2 Time-Series MethodsSimple Moving Averages

  27. 450 — 430 — 410 — 390 — 370 — Patient Week Arrivals 1 400 2 380 3 411 Patient arrivals F4 = 397.0 Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsSimple Moving Averages Example 12.2

  28. 450 — 430 — 410 — 390 — 370 — Patient Week Arrivals 1 400 2 380 3 411 Patient arrivals F4 = 397.0 Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsSimple Moving Averages Example 12.2

  29. 450 — 430 — 410 — 390 — 370 — Patient Week Arrivals 2 380 3 411 4 415 Patient arrivals 415 + 411 + 380 3 F5 = Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsSimple Moving Averages Example 12.2

  30. 450 — 430 — 410 — 390 — 370 — Patient Week Arrivals 2 380 3 411 4 415 Patient arrivals F5 = 402.0 Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsSimple Moving Averages Example 12.2

  31. 450 — 430 — 410 — 390 — 370 — Patient arrivals Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsSimple Moving Averages Example 12.2

  32. 450 — 430 — 410 — 390 — 370 — 3-week MA forecast Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsSimple Moving Averages Patient arrivals Figure 12.5

  33. 450 — 430 — 410 — 390 — 370 — 6-week MA forecast 3-week MA forecast Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Time-Series MethodsSimple Moving Averages Patient arrivals Week Figure 12.6

  34. Weighted Moving Average While the moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods. The formula for the moving average is: wt = weight given to time period “t” occurrence. (Weights must add to one.)

  35. 450 — 430 — 410 — 390 — 370 — 6-week MA forecast 3-week MA forecast Weighted Moving Average Assigned weights t 0.70 t-1 0.20 t-2 0.10 Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Time-Series MethodsWeighted Moving Average Patient arrivals Week Example 12.3

  36. 450 — 430 — 410 — 390 — 370 — 6-week MA forecast 3-week MA forecast Weighted Moving Average Assigned weights t 0.70 t-1 0.20 t-2 0.10 F4 = 0.70(411) + 0.20(380) + 0.10(400) Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsWeighted Moving Average Patient arrivals Example 12.3

  37. 450 — 430 — 410 — 390 — 370 — 6-week MA forecast 3-week MA forecast Weighted Moving Average Assigned weights t 0.70 t-1 0.20 t-2 0.10 F4 = 403.7 Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsWeighted Moving Average Patient arrivals Example 12.3

  38. 450 — 430 — 410 — 390 — 370 — 6-week MA forecast 3-week MA forecast Weighted Moving Average Assigned weights t 0.70 t-1 0.20 t-2 0.10 F4 = 404 F5 = 410.7 Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsWeighted Moving Average Patient arrivals Example 12.3

  39. 450 — 430 — 410 — 390 — 370 — 6-week MA forecast 3-week MA forecast Weighted Moving Average Assigned weights t 0.70 t-1 0.20 t-2 0.10 F4 = 404 F5 = 411 Actual patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsWeighted Moving Average Patient arrivals Example 12.3

  40. Exponential Smoothing Model • Premise: The most recent observations might have the highest predictive value. • Therefore, we should give more weight to the more recent time periods when forecasting. Ft +1 = Ft +  (Dt – Ft ) a = smoothing constant

  41. 450 — 430 — 410 — 390 — 370 — Exponential Smoothing  = 0.10 Ft +1 = Ft +  (Dt – Ft ) | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsExponential Smoothing Patient arrivals Example 12.4

  42. 450 — 430 — 410 — 390 — 370 — Exponential Smoothing  = 0.10 F3 = (400 + 380)/2 D3 = 411 F4 = 0.10(411) + 0.90(390) | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsExponential Smoothing Ft +1 = Ft +  (Dt – Ft ) Patient arrivals Example 12.4

  43. 450 — 430 — 410 — 390 — 370 — Exponential Smoothing  = 0.10 Ft +1 = Ft +  (Dt – Ft ) Patient arrivals F3 = (400 + 380)/2 D3 = 411 F4 = 392.1 | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsExponential Smoothing Example 12.4

  44. 450 — 430 — 410 — 390 — 370 — Exponential Smoothing  = 0.10 Ft +1 = Ft +  (Dt – Ft ) Patient arrivals F4 = 392.1 D4 = 415 F4 = 392.1 F5 = 394.4 | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsExponential Smoothing Example 12.4

  45. 450 — 430 — 410 — 390 — 370 — Time-Series MethodsExponential Smoothing Patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Example 12.4

  46. 450 — 430 — 410 — 390 — 370 — Patient arrivals | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsExponential Smoothing Exponential smoothing  = 0.10 Example 12.4

  47. 450 — 430 — 410 — 390 — 370 — 6-week MA forecast 3-week MA forecast Patient arrivals Exponential smoothing  = 0.10 | | | | | | 0 5 10 15 20 25 30 Week Time-Series MethodsExponential Smoothing Example 12.4

  48. 80 — 70 — 60 — 50 — 40 — 30 — Patient arrivals | | | | | | | | | | | | | | | 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Week Time-Series MethodsTrend-Adjusted Exponential Smoothing Actual blood test requests Example 12.5

  49. At = Dt + (1 – )(At-1 + Tt-1) Tt = (At – At-1) + (1 – )Tt-1 Ft+1 = At + Tt Time-Series MethodsTrend-Adjusted Exponential Smoothing At = exponentially smoothed average of the series in period t Tt = exponentially smoothed average of the trend in period t  = smoothing parameter for the average, 0-1 = smoothing parameter for the average, 0-1 F t+1 = forecast for period t + 1

  50. 80 — 70 — 60 — 50 — 40 — 30 — Medanalysis, Inc. Demand for blood analysis At = Dt + (1 – )(At-1 + Tt-1) Tt = (At – At-1) + (1 – )Tt-1 Patient arrivals | | | | | | | | | | | | | | | 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Week Time-Series MethodsTrend-Adjusted Exponential Smoothing Example 12.5

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