Chapter 13 Improved forecasting methods

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Presentation transcript:

Chapter 13 Improved forecasting methods - Better forecasting for rising or falling demand - Coping with seasonal demand - Alternative techniques

Figure 13.1 Forecasting Trend

Figure 13.2 Double exponential model Y=bx + d Y Demand b a Time Periods x

Figure 13.3 Double exponential smoothing process

Figure 13.4 Double Exponential Calculations Week Customer Demand Error Demand Level recalculated, d Trend Recalculated, t Forecast for next week, Dt-1 9 61.34 2.57 63.91 10 91 -27.1 73.66 3.65 77.31 11 40 37.3 63.88 2.16 66.04 12 56 10.0 62.42 1.76 64.18 13 8.2 61.23 1.43 62.66 14 35 27.7 52.70 0.32 53.02 15 80 -27.0 62.74 1.40 64.14 16 85 -20.9 71.65 2.23 73.88 17 43 30.9 62.76 1.00 63.76

Figure 13.5 Double Exponential Smoothing Forecasts Demand Weeks

Figure 13.6 Effects of Sudden Demand Changes 2 1

Seasonal Demand History 1 2 3 4 5 6 7 8 9 10 11 12 Year -3 Year -2 Year -1 50 100 150 200 250 300 Month Demand

Fig 13.7 Creating the Base Series Demand History Seasonal Factors Base Series Month Year 1 Year 2 Year 3 1 40 60 0.421 0.600 0.571 0.531 2 20 0.211 0.200 0.381 0.264 3 80 70 0.842 0.700 0.705 4 200 150 180 2.105 1.500 1.714 1.773 5 230 240 1.579 2.300 2.286 2.055 6 210 270 2.211 2.400 2.571 2.394 7 140 160 1.474 1.600 1.905 1.659 8 0.737 0.503 9 30 0.316 0.300 0.000 0.205 10 0.105 0.095 0.134 11 100 50 1.000 0.476 0.738 12 120 90 1.263 0.857 1.040 Total 95 105

Figure 13.8 Forecasting with Base Series Seasonal Month Forecast Sales Actual Sales Base Series from figure 13.7 Equivalent Average Demand New Smoothed Average Initial Forecast = 130 1 68.90 50 0.53 94.3 122.87 2 31.95 30 0.26 115.4 121.37 3 84.96 70 0.7 100.0 117.10 4 207.26 260 1.77 146.9 123.06 5 252.27 220 2.05 107.3 119.91 6 286.58 2.39 7 1.66 8 0.5 Normal Exponential 9 0.21 Smoothing alpha = 0.2 10 0.13 11 0.74 12 1.04 Calculation Base Series X New Smoothed Average Actual Sales Recorded From Historical Base Series Actual Sales divided by Base Series Value Exponential Calculation from Forecast & Equivalent Actual

Figure 13.9 Baysian Approach Causes of forecast errors

Figure 13.10 Forecasting Models Application Constraints Moving Average Static demand level, irregular pattern Usually Unsuitable for major products Regression Analysis Continuous trend, irregular pattern Oversensitive to outliers Exponential Smoothing Variable demand, gradual changing level, and slow moving spares items Good Basic Workhorse Base Series Seasonal demand, mature products with low variability Basis for most seasonal forecasting Double Exponential Smoothing consistent increasing or decreasing demand, product phase-in and obsolescence Apply where variability does not mask trend Fourier Analysis Cyclic established demand e.g. commodities Product with long consistent history Baysian Forecasting General products Mathematics needs developing Fuzzy Logic Sophisticated modelling Not yet available More sophisticated techniques Improved exponential forecasts with more complex formulae Much history often needed, for longer term forecasting Specific models Based on market knowledge, potentially most accurate Unreliable due to forecasters bias