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Regression Analysis for Demand Forecasting in Supply Chains

This chapter delves into associative forecasting within supply chains, primarily utilizing regression analysis. It highlights the relationship between a dependent variable (demand) and independent variables (predictor variables). Focusing on linear regression, it explains how to compute relationships using the least squares line to minimize deviations. Key statistical outputs such as the correlation coefficient, coefficient of determination, and standard deviation of forecasts are discussed. This provides valuable insight into forecasting demand effectively through analytical tools.

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Regression Analysis for Demand Forecasting in Supply Chains

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  1. Forecasting -2.2 Regression Analysis ArdavanAsef-Vaziri Chapter 7Demand Forecastingin a Supply Chain

  2. 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).

  3. Regression Method Computedrelationship Least Squares Line minimizes sum of squared deviations around the line

  4. Regression: Chart the Data

  5. Regression: The Same as Solver but This Time Data Analysis

  6. Data/Data Analysis/ Regression

  7. Regression: Tools / Data Analysis / Regression

  8. 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

  9. 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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