Interventions required to meet business objectives - from Forecasting Methods, Forecast Accuracy / Error Reduction, Integrate – Sales Forecast / Production to undertaking a CPFR

1. Forecasting Techniques Interventions required to meet business objectives Anand Subramaniam

4. Forecasting Methods

5. Planning Levels

7. Major Areas of Forecasting Economic Forecasting Predicts what the general business conditions will be in the future (Eg. Inflation rates, Gross National Product, Tax, Level of employment) Technology Forecasting Predicts the probability and / or possible future developments in technology (Eg. Competitive advantage or firm’s competitors incorporate into their products and processes) Demand Forecasting Predicts the quantity and timing of demand for a firm’s products

8. Forecasting Methods Subjective Approach (Qualitative in nature and usually based on the opinions of people) Objective Approach (Quantitative / Mathematical formulations - statistical forecasting)

10. Quantitative Methods Time Series Models (Only independent variable is the time used to analyse 1) Trends, or 2) Seasonal, or 3) Cyclical Factors that influence the demand data) Casual Models (Employ some factors other than Time, when predicting forecast values)

13. Quantitative Methods - Examples

14. Simple Moving Average F 4 =(650+678+720)/3 =682.67 F 7 =(650+678+720 +785+859+920)/6 =768.67

15. Simple Moving Average

17. Exponential Smoothing F 1 =820+(0.5)(820-820)=820 F 3 =820+(0.5)(775-820)=797.75

18. Effect of  on Forecast

19. Simple Linear Regression Model

20. Simple Linear Regression Model (Contd)

21. Simple Linear Regression Model (Contd) Y t = 143.5 + 6.3x 135 140 145 150 155 160 165 170 175 180 1 2 3 4 5 Period Sales Sales Forecast

22. Simple Linear Regression Model (Contd) Actual observation (y value) Least squares method minimises the sum of the squared errors (deviations) Time period Values of Dependent Variable Deviation 1 (error) Deviation 5 Deviation 7 Deviation 2 Deviation 6 Deviation 4 Deviation 3 Trend line, y = a + bx ^

23. Forecast Accuracy / Error Reduction

32. Mean Absolute Deviation Month Sales Forecast Abs Error 1 220 n/a 2 250 255 5 3 210 205 5 4 300 320 20 5 325 315 10 40 Note that by itself, the MAD only lets us know the mean error in a set of forecasts.

33. Forecast Error Measures Period Sales (A) Forecast E |E| E 2 |E|/A 1 1600 1650 -50 50 2500 0.0313 2 2200 2010 190 190 36100 0.0864 3 2000 2200 -200 200 40000 0.1000 4 1600 1580 20 20 400 0.0125 5 2500 2480 20 20 400 0.0080 6 3500 3520 -20 20 400 0.0057 7 3300 3310 -10 10 100 0.0030 8 3200 3200 0 0 0 0.0000 9 3900 3850 50 50 2500 0.0128 10 4700 4720 -20 20 400 0.0043 10 -20 580 82800 0.2639 Bias = -2 low/High MAD = 58 MSE = 8280 MAPE= 2.64%

34. Integrate – Sales Forecast / Production

35. Forecasting Process Services Collect Data Select Model Plot Data Develop Forecast Check Accuracy Forecast Adjust Forecast Monitor Forecast Sales and Operations Planning Master Scheduling Customer Scheduling Materials Planning Workforce Scheduling Order Scheduling Manufacturing Forecasting

36. Integrate - Sales Forecast & Production

37. CPFR - Collaborative Planning, Forecasting and Replenishment

39. CPFR Model