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The impact of forecasting on companies' performance: Analysis in a multivariate setting

By Pamela Danese and Matteo Kalchschmidt

Abstract

Over the years, practitioners and researchers have devoted their attention to forecasting techniques and methods that can be adopted to improve companies' performance. However, forecasting techniques alone are not enough since companies should also consider several other issues associated with forecasting process management, e.g. how companies collect and use information on the market, or how the forecast is used in different decision-making processes. It is also important to understand the existence of interaction effects between these different forecasting variables, as they could determine a positive additional synergistic effect on companies' performance. This paper aims to investigate what relevant forecasting variables should be considered to improve companies' performance, and whether some forecasting variables can interact and influence performance with a synergistic effect. Analyses are conducted by means of data collected by the Global Manufacturing Research Group (GMRG). Data from a sample of 343 manufacturing companies in 6 different countries demonstrate that when companies intend to improve cost and delivery performances, they should devote their attention to all the different forecasting variables. In addition, the results found reveal the existence of positive interaction effects between the collection and use of information on the market and the other forecasting variables, as well as the existence of a negative interaction effect between the adoption of forecasting techniques and the use of forecasts in several decision-making processes. These results have important implications for managers as they provide guidance on how to lever on the different forecasting variables to maximize companies' performance.Demand forecasting GMRG Accuracy Cost and delivery performances Backward stepwise regression

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