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Forecasting OPS 370. Forecasting What is Forecasting? – Determining Future Events Based on Historical Facts and Data Some Thoughts on Forecasts – Forecasts.

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Presentation on theme: "Forecasting OPS 370. Forecasting What is Forecasting? – Determining Future Events Based on Historical Facts and Data Some Thoughts on Forecasts – Forecasts."— Presentation transcript:

1 Forecasting OPS 370

2 Forecasting What is Forecasting? – Determining Future Events Based on Historical Facts and Data Some Thoughts on Forecasts – Forecasts Tend to Be Wrong! – Forecasts Can Be Biased! (Marketing, Sales, etc.) – Forecasts Tend to Be Better for Near Future So, Why Forecast? – Better to Have “Educated Guess” About Future Than to Not Forecast At All! Forecasting - Chapter 4 2

3 What to Forecast? Forecasting - Chapter 4 3 Short Term (0-3 Months) Demand for Individual Products & Services Medium Term (3 Months – 2 Years) Demand for Product & Service Families Long Term (>2 Years) Total Sales, New Offerings

4 How to Forecast? Qualitative Methods – Based On Educated Opinion & Judgment (Subjective) – Particularly Useful When Lacking Numerical Data (Example: Design and Introduction Phases of a Product’s Life Cycle) Quantitative Methods – Based On Data (Objective) Forecasting - Chapter 4 4

5 Qualitative Methods Executive Judgment Sales Force Composite Market Research/Survey Delphi Method Forecasting - Chapter 4 5

6 Quantitative Methods Time Series & Regression Time Series  Popular Forecasting Approach in Operations Management Assumption: – “Patterns” That Occurred in the Past Will Continue to Occur In the Future Patterns – Random Variation – Trend – Seasonality – Composite Forecasting - Chapter 4 6

7

8 UK Airline Miles Thousands of Miles Observe: Increasing trend, Seasonal component. Random variation.

9 Forecasting Steps Forecasting - Chapter 4 9 Data Collection Data Analysis Model Selection Monitoring Collect Relevant/Reliable Data Be Aware of “Garbage-In, Garbage Out”

10 Forecasting Steps Forecasting - Chapter 4 10 Data Collection Data Analysis Model Selection Monitoring Plot the Data Identify Patterns

11 Forecasting Steps Forecasting - Chapter 4 11 Data Collection Data Analysis Model Selection Monitoring Choose Model Appropriate for Data Consider Complexity Trade-Offs Perform Forecast(s) Select Model Based on Performance Measure(s)

12 Forecasting Steps Forecasting - Chapter 4 12 Data Collection Data Analysis Model Selection Monitoring Track Forecast Performance (Conditions May and Often Do Change)

13 Time Series Models Short Term – Naïve – Simple Moving Average – Weighted Moving Average – Exponential Smoothing Forecasting - Chapter 4 13

14 Forecasting Example L&F Bakery has been forecasting by “gut feel.” They would like to use a formal (i.e., quantitative) forecasting technique. Forecasting - Chapter 4 14

15 Forecasting Methods Naïve Forecast for July = Actual for June F t+1 = A t F Jul = A Jun = 600 Forecast Very Sensitive to Demand Changes; Good for stable demand Forecasting - Chapter 4 15

16 Forecasting Methods Naïve (Excel) Forecasting - Chapter 4 16 =C4 =C5

17 Forecasting Methods Moving Average Forecast for July = Average of June, May, and April F t+1 = (A t +A t-1 +…)/n F Jul = ( )/3 = 500 Values Equally Weighted; Good for stable demand; Sensitive to fluctuation; Lags Common application: Stock price forecasting

18 Forecasting Methods 30 Day Moving Average of AAPL Price

19 Forecasting Methods Moving Average (Excel) =AVERAGE(C4:C6) = AVERAGE(C5:C7)

20 Forecasting Methods Moving Average Example Assume n = 2 ( )/2 = 150 ( )/2 = ( )/2 = 150 ( )/2 = 155

21 Forecasting Methods Weighted Moving Average F t+1 = (W 1 A t +W 2 A t-1 +…) Assume that W 1 = 0.5, W 2 =0.3 and W 3 = 0.2 F Jul = (0.5)(600) + (0.3)(500) + (0.2)(400) = = 530 Typically Gives More Weight to Newer Data Lags; Sensitive

22 Forecasting Methods Weighted Moving Average =$G$6*C6+$G$5*C5+$G$4*C4 =$G$6*C7+$G$5*C6+$G$4*C5

23 Forecasting Methods Weighted Moving Average Example Assume n = 2, W 1 = 0.7, W 2 = 0.3 (0.7)(175) + (0.3)(125) = 160 (0.7)(150) + (0.3)(175) = (0.7)(150) + (0.3)(150) = 150 (0.7)(160) + (0.3)(150) = 157

24 Forecasting Methods Exponential Smoothing Forecast for June = Forecast for May +  (Forecast Error in May)  is a constant between 0 and 1 Forecast Error = Difference Actual Demand and Forecasted Demand General Formula: F t+1 = F t +  e t

25 Forecasting Methods Exponential Smoothing Assume that  = 0.3 What is the forecast for July? = June Forecast +  (Forecast Error in June) = (0.3)(257) = 420 Requires less data; Good for stable data

26 Forecasting Methods Exponential Smoothing (Excel) Initial forecast =D4+$G$4*(C4-D4) =D5+$G$4*(C5-D5)

27 Forecasting Methods Exponential Smoothing Example Assume  = 0.4 (125) + (0.4)( ) = 145 (145) + (0.4)( ) = 147 (147) + (0.4)( ) = (148.2) + (0.4)( ) = Need initial forecast; Assume 125 (125) + (0.4)( ) = 125

28 Forecasting Methods How to Select Value of  ? Alpha determine importance of recent forecast results in new forecasts Small alpha  Less importance on recent results (Good for products with stable demand) Large alpha  Recent forecast results more important (Good for product with varying demands)

29 Determining Forecast Quality How Well Did a Forecast Perform? Determine Forecast Error Error = Actual Demand – Forecasted Demand Average Error 121.8

30 Determining Forecast Quality Why is Average Error a Deceiving Measure of Quality? Better Measures: Mean Absolute Deviation Mean Squared Error Root Mean Squared Error

31 Determining Forecast Quality MAD MSE Measure of Bias: Tracking Signal = Sum of Errors/MAD =731/131.8 = 5.55 *OK if between -4 and +4

32 Determining Forecast Quality For this MA(2) forecast. What is MAD, MSE, and TS?

33 Linear Regression Linear Regression  Statistical technique that expresses the forecast variable as a linear function of one or more independent variables Commonly Used for Causal Data – Example: Relationship Between Temperature and Ice Cream Sales Also Used for Time Series Data (x Variable is Time, y is Demand, Sales, etc.)

34 Linear Trend Line Given Data –Y = Values of Response Variable –X = Values of Independent Variable Parameters to estimate –a = Y-intercept –b = slope Use “least squares” regression equations to estimate a and b. –Or …

35 Excel for Linear Regression Use SLOPE Function Use INTERCEPT Function


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