Adapting sales and operations planning to engineer-to-order complexity: a multifaceted information processing approach to managing uncertainty and equivocality

Engineer-to-order (ETO) manufacturing firms face challenges in their sales and operations planning (S&OP) processes due to inherent environmental uncertainty and equivocality stemming from extensive customization and project complexity. These factors result in frequent resource allocation inefficiencies and delays in project execution. This study investigates how the S&OP process design is adapted to manage these complexities. We build a multifaceted theoretical framework to enrich organizational information processing theory (OIPT) with insights from contingency theory, the dynamic capabilities view (DCV), and socio-technical systems (STS) theory. Through a multiple-case study of four large ETO manufacturers, we explore how firms design their S&OP processes to achieve a fit between their information processing requirements (IPRs) and capacity (IPC). Key findings indicate that effective S&OP design depends on the firm's ETO archetype (design-to-order vs. redesign-to-order), which dictates the dominant information problem (equivocality vs. uncertainty). Furthermore, the success of the technical S&OP system depends on the health of its social system. The study serves two primary contributions. It develops a contingent and socio-technical framework for ETO S&OP. It also extends the OIPT by introducing socio-behavioral ambiguities as a distinct class of IPRs and by identifying non-technical information-processing mechanisms (e.g., trust-building) as the corresponding behavioral solutions. The proposed model explains how ETO firms enhance S&OP quality through a contingent, dynamic, and socio-technical planning process. The findings are synthesized into a practical framework for managers to diagnose S&OP challenges and guide process improvement.