ABSTRACT
Overview Managing demand estimates to ensure they are accurate and timely is a challenging task for an organization. It requires an understanding of customers’ needs at specific points in time and geographical locations across a supply chain. is is an increasingly challenging task, on the one hand, because consumer markets have become fragmented and culturally diverse, but also easier because information technology can be deployed to immediately measure customer demand when and where it occurs. In this chapter, we will discuss demand management with a focus on the actual mechanics of forecasting. In later chapters we will discuss the migration toward developing more effective demand management systems that rely on customer relationships as well as the collection of real-time demand information. Real-time implies that demand information can be made available to an organization through its information technology (IT) systems, point-of-sale (POS) data collection and analysis, as well as electronic Kanban, and other advanced IT applications that help to manage and control the process workflows of a system. e major purpose of this chapter is to disuses the wide range of quantitative tools and methods that comprise demand management. ese range from forecasting models
using simple time series estimates of future demand to more advanced econometric models that use several independent variables to help predict future sales. Relative to econometric models, independent variables, including leading, lagging, and coincident economic indicators, as well as other variables, are used to construct these models. Although many industries rely on forecasting models, an ultimate goal of an organization should be to build closer customer relationships to better understand its customers’ demand patterns. Closer customer relationships will minimize the necessity of developing formal mathematical forecasting models. In the last part of this chapter, nonmathematical tools and methods will be discussed relative to demand management. Table 8.1 lists key metrics that reinforce a philosophy that organizations should move to the greatest extent possible away from mathematical forecasting. However, if it must be done, then it should be done well. is means that forecasting accuracy should be measured and improved over time. Also, process breakdowns that are attributable to poor demand management practices should be measured and their root causes eliminated from an organization.
