Chapter 7 Demand Forecasting in a Supply Chain

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Presentation transcript:

Chapter 7 Demand Forecasting in a Supply Chain Regression Analysis Ardavan Asef-Vaziri Chapter 7 Demand Forecasting in a Supply Chain

Associative (Causal) Forecasting -Regression The primary method for associative forecasting is Regression Analysis. The relationship between a dependent variable and one or more independent variables. The independent variables are also referred to as predictor variables. We only discuss linear regression between two variables. We consider the relationship between the dependent variable (demand) and the independent variable (time).

Regression Method Computed relationship Least Squares Line minimizes sum of squared deviations around the line

Regression: Chart the Data

Regression: The Same as Solver but This Time Data Analysis

Data/Data Analysis/ Regression

Regression: Tools / Data Analysis / Regression

Regression Output Correlation Coefficient +↑. Close to + 1 Coefficient of Determination ↑ Close to 1 Standard Deviation of Forecast ↓ If the first period is 1, next period is 10+1 = 11 P-value ↓ less than 0.05

Regression Output Ft = 94.13 +30.71t What is your forecast for the next period. F11 = 94.13 +30.71(11) = 431.7 Mean Forecast = 431.7, Standard Deviation of Forecast = 22.21

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