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01 DEMAND FORECASTING 02 AGGREGATE PLANNING IN A SUPPLY CHAIN 03 PLANNING SUPPLY AND DEMAND IN A SUPPLY CHAIN
Typology: Summaries
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N G U Y Ễ N T H Ị B Í C H T R Â M , P H D T R A M. N T B @ O U. E D U. V N
Main contents
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Demand pattern
The role of forecasting Forecasting provides an estimate of future demand, the basis for planning and sound business decisions. Accurate demand forecasts Purchasing department to order the right amount of products Operations department to produce the right amount of products Logistics department to deliver the right amount of products
Forecasting techniques
Qualitative methods Jury of executive opinion Delphi method Sales force composite Consumer survey
Time series forecasting models Naïve forecast Simple moving average Weighted moving average Exponential smoothing Linear trend forecast
Weighted moving average (See Example 5.2)
Linear Trend Forecasting A linear trend forecast can be estimated using simple linear regression to fit a line to a series of data occurring over time. This model is also referred to as the simple trend model. The trend line is determined using the least squares method, which minimizes the sum of the squared deviations to determine the characteristics of the linear equation. The trend line equation is expressed as: Ŷ = b 0 + b 1 x Where Ŷ = forecast or dependent variable; x = time variable; b 0 = intercept of the vertical axis; b 1 = slope of the trend line. (See Example 5.4)
Quantitative methods (cont.) Cause-and-effect forecasting assumes that one or more factors (independent variables) are related to demand and, therefore, can be used to predict future demand. ◦ Simple linear regression forecast ◦ Multiple regression forecast
Multiple regression forecast When several explanatory variables are used to predict the dependent variable, a multiple regression forecast is applicable. Multiple regression analysis works well when the relationships between demand (dependent variable) and several other factors (independent or explanatory variables) impacting demand are strong and stable over time. The multiple regression equation is expressed as: Ŷ = b 0 + b 1 x 1 + b 2 x 2 + … + bkxk Where Ŷ = forecast or dependent variable; xk = k th explanatory or independent variable; b 0 = constant; bk = regression coefficient of the independent variable xk.
Operational parameters in aggregate planning
Trade-off in aggregate planning CAPACITY (REGULAR TIME, OVERTIME, SUBCONTRACTED) INVENTORY BACKLOG/LOST SALES BECAUSE OF DELAY