Forecasting OPS 370

Forecasting Forecasting - Chapter 4

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

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

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

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

UK Airline Miles Thousands of Miles

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

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

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

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

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

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

Forecasting Methods Naïve Forecasting - Chapter 4

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

Forecasting Methods Moving Average

Forecasting Methods 30 Day Moving Average of AAPL Price

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

Forecasting Methods Moving Average Example Assume n = 2

Forecasting Methods Weighted Moving Average

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

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

Forecasting Methods Exponential Smoothing

Forecasting Methods Exponential Smoothing

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

Forecasting Methods Exponential Smoothing Example Assume a = 0.4

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

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

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

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

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

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

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 …

Excel for Linear Regression Use SLOPE Function Use INTERCEPT Function