The System Dynamics of Engineer-to-Order Construction Projects: Past, Present, and Future

System dynamics (SD) applications in high-volume production operations are widely used, helping to define decision rules to reduce costs associated with the variance in planning orders and inventory. The exploitation of SD in engineer-to-order (ETO) project-oriented supply chains—e.g., in construction, shipbuilding, and capital goods—is less well established. Hence, this research reviews the literature that takes a systematic ETO perspective in modeling construction projects, exploiting SD approaches. To comprehensively identify and filter previously published works, we used a keyword searching method using Web of Science and Scopus databases. After applying relevant exclusion criteria, 143 papers were selected. Although previous reviews of ETO literature, more generally, have been done, this work contributes to the body of knowledge by specifically reviewing SD applications in ETO industries and providing insights by creating a categorization system by which to determine existing gaps. Articles are categorized into the classic four phases of a project: aggregated planning, preproject planning, project execution, and postdelivery. Analyses of the methods, attributes, and applications of SD were undertaken for each phase. Findings indicate that SD research covers the range of ETO industries, of which construction is the most dominant, demonstrating SD’s high applicability. The wealth of case-orientated research in the construction field provides a solid foundation for further SD studies in the ETO field. Further research should focus on (1) developing a general ETO archetype for performance benchmarking and strategy development in construction projects; (2) introducing analytical tools, such as control theoretic approaches as found in manufacturing production planning and control design, to improve understanding of the ETO systems’ dynamic behaviors; (3) developing cross-phase, cross-project, design–production integrated, aggregated planning models via hybrid techniques modeling, which can improve understanding of an ETO system’s performance; and (4) improving model fidelity. We also provide a research agenda for each phase of the ETO production.