With today’s information technologies, e.g., RFID tags, there is an ever-growing quantity of information available and an associated escalating desire to use this information. This information can be used to optimize and improve SCM. Herein we study the question of how to use additional information in the framework of the single period stochastic lotsizing problem (newsvendor problem). In addition, we implemented our solution in the print products industry—a real world newsvendor—as a motivated example. but this is not the only industry where our model is applicable. Our model is equally applicable to the fashion and seasonal goods industry. We explain how a typical newsvendor can (and did) exploit additional information available in the supply chain to improve supply chain performance.
For our examination we focused on distribution systems that are based on a network of retailers. We sought to explore and quantify the value of additional information in these systems. In particular, we explore the value of our ability to review the state of the system more frequently and in this way to allow the partial aggregation of the retailers.
Intuitively, this research seeks to implement risk pooling through the partial aggregation of the retailers. From an operational point of view it would be best to fully aggregate the retailers, i.e., effectively combining the retailers into a single retailer. In our case, this aggregation is not possible since each retailer is owned by independent entities with no connection between them. Our model allows partial risk pooling through “virtual” risk pooling, i.e. holding back some inventory until some of the demand is revealed.
In our study we develop a natural formulation for the problem and later on a working formulation for our model. We prove convexity of our model and find an optimal solution for the problem. We developed an algorithm to numerically solve the problem and programmed the algorithm using Matlab. We performed a numerical study, numerical analysis and large-scale field study. We report on the savings that the additional information enabled (i.e., the value of the additional information) and discuss in detail what we learned both about the original system and the information rich system.
