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Coordinating Demand Forecasting and Operational Decision-Making with Asymmetric Costs: The Trend Case

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Intellectual Contribution by Robert Saltzman

Contribution Title

Coordinating Demand Forecasting and Operational Decision-Making with Asymmetric Costs: The Trend Case

Publication

California Journal of Operations Management

Co-author

Year

2003

Description

This article presents two methods for coordinating demand forecasting and operational decision-making when time series data display a trend and the unit costs of under-forecasting (cu) and over-forecasting (co) may differ. The first method, called ordinary least squares with adjustment (OLSA), makes a simple additive adjustment to the forecasts generated by OLS regression. The second method generates forecasts by fitting a weighted least absolute value (WLAV) regression line through the data, where the weights correspond to the unit underage and overage costs.

Using simulation, both methods are tested against OLS regression forecasts made in the presence of one of four types of error distributions, and numerous combinations of cu and co. The performance measure used for evaluation is the mean opportunity loss (MOL) over the post-sample periods. Simulation results show that the benefits of both approaches increase rapidly as cu and co diverge, regardless of the error distribution. In particular, when the ratio of costs is 3:1, OLSA can reduce post-sample MOL of OLS by 19-23%. Even when the ratio of costs is closer to 2:1, OLSA can still reduce post-sample MOL by 6-7%. In a few scenarios, WLAV outperforms OLSA, but generally is not as robust as OLSA.

Complete Citation

Saltzman, R., "Coordinating Demand Forecasting and Operational Decision-Making with Asymmetric Costs: The Trend Case," California Journal of Operations Management, Vol. 1, No. 1, pp. 14_21 (Feb. 2003).

Website

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