Learning about Customers: Demand Implications of Logistics-Related Decision-Making in B2B

In business-to-business (B2B) exchanges, customers are more likely to buy from suppliers who know them well and consistently provide good service. Yet, when planners optimize the operations with a focus on cost-reduction, they risk overlooking the importance of building long-term relationships with their customers. These relationships critically depend on learning the customer’s preferences, priorities, and service expectations. While building strong customer relationships in a B2B context has traditionally been the salespeople’s responsibility, AI developments now open the possibility for AI-based learning about customers. AI-based learning about customers in a B2B setting is complex though because each customer has their own needs and preferences, leading to highly customized offerings. These customized offerings often include agreements on critical logistics-related decisions such as lead times, delivery, and maintenance planning. In this setting, close contact between the people from sales and operations – i.e., a strong marketing-operations interface – benefits the customer relationship. Yet as information on customers is embedded both in IT systems (e.g., CRM systems) and people (e.g., salespersons), this is a domain where B2B firms can benefit greatly from AI. This PhD project thus studies on how AI can help planners tailor their operations to better serve customer needs.

Sarah Gelper Nevin Mutlu Fred Langerak

More info: escf@tue.nl