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Forecasting OPS 370.

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Presentation on theme: "Forecasting OPS 370."— Presentation transcript:

1 Forecasting OPS 370

2 Forecasting Forecasting - Chapter 4

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

4 How to Forecast? Qualitative Methods Quantitative 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

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

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

7

8 UK Airline Miles Thousands of Miles

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

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

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

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

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

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

15 Forecasting Methods Naïve Forecasting - Chapter 4

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

17 Forecasting Methods Moving Average

18 Forecasting Methods 30 Day Moving Average of AAPL Price

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

20 Forecasting Methods Moving Average Example Assume n = 2

21 Forecasting Methods Weighted Moving Average

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

23 Forecasting Methods Weighted Moving Average Example
Assume n = 2, W1 = 0.7, W2 = 0.3

24 Forecasting Methods Exponential Smoothing

25 Forecasting Methods Exponential Smoothing

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 a = 0.4

28 Forecasting Methods How to Select Value of a?
Alpha determine importance of recent forecast results in new forecasts

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

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

33 Linear Regression <SKIP Section in Textbook on Exponential Smoothing with Linear Trend>

34 Linear Trend Line Given Data Parameters to estimate
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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