Springerbriefs in Optimization

A rich history of literature exists on modeling and optimization of production and distribution planning problems in supply chains. The overwhelming majority of this literature considers a system with inherent costs and constraints that must respond to some set of demands at a minimum cost. That is, given a demand stream, the goal is to minimize the cost of meeting these demands at a minimum cost. In many cases, this demand is assumed to be deterministic, which implies that the planner knows the timing and quantities of future demands. Alternatively, this demand stream may be stochastic, and although the exact timing and quantities of future demands are not known in advance, the demand stream is described by some probability distribution(s) containing well defined parameters. In both the deterministic and stochastic cases we have described, the supply chain responds to the demand stream based on the available parameters that describe this stream. In practice, however, the supply chain can exert influence over the demand stream parameters. That is, marketing and supply chain planners have a number of levers that they control that can shape the demands to which the system must respond. The most obvious of these levers is that of price. A supply chain offering a product or service with some degree of monopoly power can use price as a mechanism that directly effects demand for the product. In non-monopolistic cases, firms can also use temporary price changes to affect demand in the short run. Similarly, a firm may invest in promotions (through, for example, advertising) to influence short-run demand, whether or not price changes occur. In addition to pricing, the markets and market channels that a supplier chooses may strongly affect demand requirements and costs incurred. Recognizing this ability for a supply chain to influence demand, a growing body of literature has developed in recent years on integrated models and solution methods for optimal demand and production/procurement planning. This body of literature exploits dimensions of demand flexibility in supply chains and characterizes the best fit between demand characteristics and operations capabilities and constraints. The resulting models implicitly consider both the revenue side and the cost side in supply chain planning, and lead to a richer characterization of supply chain optimization. Thus, the purpose of this work is to encapsulate the essential developments in this field in a single resource, as well as to set an agenda for further development in this field. We will trace the origins and seminal works in integrated demand and operations planning and provide an in-depth documentation of the current state of the art. Following this, we will explore essential remaining unanswered questions and highlight the high-value research directions for both practice and for the development of new and interesting optimization models and algorithms.