Designing Artificial Intelligence Systems for B2B Aftersales Decision Support

Abstract

Original equipment manufacturers have started to transform and servitize their business models and processes. This transformation entails shifting their focus from the product development towards the product usage phase. In this phase, they can monetize aftersales services such as maintenance, repair, overhaul, and spare parts delivery. However, low-cost competitors threaten the potential gains from this transformation. This threat requires original equipment manufacturers to become more customer-centric and exploit their internal resources better. Artificial intelligence (AI) has the potential to enable such customer-centric B2B service strategies. However, especially in B2B contexts, professionals and researchers lack guidance about how to design and implement effective AI systems.In reaction to this, I conducted three action design research (ADR) studies at MAN Energy Solutions that follow the dual-mission of information systems research, to create utility for practitioners while extending the scientific body of knowledge. These ADR studies resulted in three implementations of novel AI systems. The systems represent the state-of-the-art in AI value creation while addressing the specific challenges of B2B aftersales contexts. In addition to this, I developed a set of design principles that explicitly guide practitioners and researchers on how to design and implement AI systems for B2B aftersales decision support. These AI systems, in turn, enable B2B firms in general and original equipment manufacturers, in particular, to adopt technology-driven and customer-centric service strategies and thereby to create competitive advantages by providing personalized and high-quality service that the lowcost competition cannot provide