ABSTRACT

Forecasts lie at the heart of Production and Operations Management (POM). They serve as a key input to all POM decisions such as inventory management, production planning, and scheduling as well as operations strategy and product innovation. All these decisions are based on a forecast of the future, both long term at the strategic planning level as well as short-term disaggregate forecasts at the SKU level used for tactical decisions. Improving forecasting performance has been shown to lead to significant benefits in both POM and across the supply chain (Oliva and Watson 2011; Moritz et al. 2014). However, implementation of forecasting processes and associated technologies is a challenge. Methodological advancements, available technology, and information access have elevated forecasting capability. In practice, however, forecasting processes still rely heavily on human judgment (Lawrence et al. 2006). Forecasts within the practice of POM are usually produced as a combination of statistical forecasts and judgment (Fildes and Goodwin 2007), where an initial statistical forecast is adjusted judgmentally. Therefore, understanding forecasting requires comprehending both statistical and judgmental methods, as well as ways they can be combined in practice to improve performance.