Allocation in sales and operations planning

The sales and operations planning (S&OP) process involves a multitude of decision makers from different functions of an organization and aims at aligning the demand for products with the supply. This alignment process is carried out at different levels. The interests of the multiple decision makers involved in S&OP are diverse and partly conflicting. On the tactical level, the sales organization is interested in filling all customer requests that may potentially come up, and therefore tends to over-forecast the demand. This leads to a misallocation of production resources, an increase in inventory, and reduced profits. Similar effects also occur on the operational level. Here, a given supply of products is allocated. It is widely known that customers inflate their forecasts in supply shortage situations to game the allocation mechanism of their supplier and to ensure sufficient supply. This, again, leads to an inefficient allocation with decreased overall service levels and an increased inventory. However, with the recent advances in IT tools, companies are now able to monitor the ordering behavior of their customers on the granularity level of individual customers and individual products. The higher transparency of the customers' ordering behavior provides opportunities to increase the efficiency of supply allocations.

Key challenges in S&OP are therefore to address forecast uncertainty and the strategic behavior of multiple decision makers. To provide decision support for practice, we combine two prescriptive methodologies: Mechanism design and optimization. We aim at designing mechanisms for the dynamic S&OP process, which set incentives for the stakeholders to report their forecasts truthfully. We also aim at developing optimization approaches for the resulting large-scale resource allocations problems, which exploit the available information. Another venue is to investigate behavioral aspects in S&OP group forecasting and decision-making behavior. All developed approaches will be tested based on data from the semiconductor industry and the chemical industry.