Conference Agenda

This paper addresses the challenge of evaluating existing product alternatives and predicting demand for new entries. We propose two innovative solution approaches. The first is associated with polytope volume computation designed to predict demand shifts resulting from the introduction of new products. We investigate the interrelated challenges, including computational intensity, scalability issues, sensitivity to data quality, and interpretability concerns. In response, we propose the second approach that leverages modern benchmarking. This approach provides a more user-friendly and transparent framework for assessing and predicting demands, complementing the complexity of polytope volume computation. To illustrate the applicability and effectiveness of our approaches, we provide an empirical illustration within the pharmaceutical industry, focusing on the competitive market for diabetes medications in Denmark. This study highlights the potential of our approaches in enhancing decision-making processes, thereby contributing to various aspects of planning within pharmaceutical supply chain management.

We will cover how we incorporate optimization algorithms into the supply chain management system RapidResponse and which challenges arise in real world cases. One aspect is the combination of heuristics and linear optimization techniques to handle large scale, end-to-end supply planning instances. We conclude with a discussion of customer use cases and promising product enhancements.