PurposeChanges frequently made to material delivery schedules (MDSs) accumulate upstream in the supply chain (SC), causing a bullwhip effect. This article seeks to elucidate how dynamic complexity generates MDS instability at OEMs in the automotive industry.Design/methodology/approachAn exploratory multiple-case study methodology involved in-depth semistructured interviews with informants at three automotive original equipment manufacturers (OEMs).FindingsDynamic complexity destabilizes MDSs primarily via internal horizontal interactions between product and process complexities and demand and SC complexities. A network of complexity interactions causes and moderates such instability through complexity absorption and generation and complexity importation and exportation.Research limitations/implicationsThe multiple-case study contributes to empirical knowledge about the dynamics of MDS instability. Deductive research to validate the identified relationships remains for Future research.Practical implicationsIn revealing antecedents of complexity’s effect on MDS instability, the findings imply the need to develop strategies, programs, and policies dedicated to improving capacity scalability, supplier flexibility, and the flexibility of material order fulfillment.Originality/valueBuilding on complexity literature, the authors operationalize complexity transfer and develop a framework for analyzing dynamic complexity in SCs, focusing on complexity interactions. The identification and categorization of interactions provide a granular view of the dynamic complexity that generates MDS instability. The identified and proposed importance of readiness of the SC to absorb complexity challenges the literature focus on external factors for explaining complexity outcomes. The results can be used to operationalize such dynamic interactions by introducing new variables and networks of relationships. Moreover, the work showcases how a complexity perspective could be used to discern the root causes of a complex phenomenon driven by non-linear relationships.

Fulfilling customer orders in engineer-to-order (ETO) settings entails customization and, thus, greater complexity: detail and uncertainty. Tactical planning aims at demand-supply (DS) balancing by ensuring cross-functional integration (CFI), which incorporates coordination as one dimension. This study uses a case study approach to identify the key coordination mechanisms applied in the customer order fulfilment processes (COFPs) to mitigate the negative impact of complexity on DS balancing in four ETO-oriented settings. Within-case analyses identify the applied mechanisms, and a cross-case analysis elaborates on how they influence the detail and uncertainty in decision-making and problemsolving activities. Findings suggest a positive effect of formalized activity sequences, balanced team compositions, effective task designs and supportive information systems (ISs); and a positive (but contingent) effect of the other mechanisms. Future research may address other CFI dimensions (collaboration), statistically test the findings, or qualitatively deepen the understanding of the forms and impacts of individual mechanisms.

The challenging demand-supply balancing in engineer-to-order (ETO) environments is often attributed to complexity. This study expands the understanding of managing complexity to obtain demand-supply balancing, focussing on the tactical planning logic of the order fulfilment process. An in-depth single case study was conducted and data describing the order fulfilment process at a construction company were collected and analysed. Findings suggest a tactical-level planning process framework, incorporating nine key decisions and three crucial activities, and their potential complexity-reducing and complexity-absorbing impact. The study contributes to the theoretical discussion of complexity in management practices, linking demand-supply balancing as a performance measure. The findings guide practitioners in ETO settings on anticipating potential medium-term consequences of key decisions on capacity. This emphasises the need of proper IT support to apply knowledge generated from previous projects and conduct comprehensive and robust scenario-based analyses.