Predicting demand for new products using polytope volume and benchmarking
针对缺乏历史数据的新产品需求预测难题，提出基于多面体体积和基于DEA超效率的两种方法，并在丹麦糖尿病药物市场验证了其有效性。
• Proposes two approaches to predict demand for new products in competitive markets. • Volume-based approach uses polytope volume to measure product preference shifts. • Benchmarking-based approach uses DEA super-efficiency to predict demand changes. • Empirical illustration on diabetes medications in Denmark's pharmaceutical market. This paper addresses the challenge of evaluating existing product alternatives and predicting demand for new entries. We target settings in which historical demand data for the entrant are unavailable or non-analogous, limiting the usefulness of fitted econometric or machine-learning demand models. To this end, two alternative approaches are proposed: the volume-based approach and the benchmarking-based approach. The former, serving as the theoretical backbone of both approaches, forms weight sets that represent the range of attribute weights under which an alternative is preferred over others. By calculating the volumes of these sets – a process involving the Double Description Method and the Quickhull algorithm – we quantify potential demand shifts from new product introductions. The latter approach – less computationally demanding – uses Data Envelopment Analysis as a core component to assess the efficiency of existing products. We also formulate super-efficiency programs to extend the analysis by measuring the degree to which a product alternative surpasses the efficiency frontier. This enables us to predict how the introduction of a new product will reshape the demand landscape. Graphical examples and an empirical illustration within the pharmaceutical industry, specifically focusing on the rather concentrated but competitive market for diabetes medications in Denmark, demonstrate the practical application and the similarity of the results of these approaches.