Tactical capacity planning in a real-world ETO industry case: An action research

Abstract

Many engineering-to-order (ETO) organizations are multi-project capacity-driven production systems in which capacity planning is of major importance in the order acceptance phase. The academic literature, in this area, presents a research-practice gap with a lack of studies on the application of decision support tools to address capacity planning problems in real-world ETO settings. To reduce this gap, this paper presents the findings from an action research on the application of an optimization model to support tactical capacity planning in a medium-sized project-driven ETO organization. To properly model the studied environment, the proposed solution includes the representation of overlapping and variable intensity activities and the use of nonregular capacity options (e.g., overtime, hiring workers, subcontracting). The model was fed with real-world data and solved in order to check whether it actually reflects the planning problem. Furthermore, alternative scenarios were also generated to assist the management board in the order acceptance phase. As for practical implications, for the company manufacturing team, the modeĺs application enhanced the decision-making process regarding tactical capacity planning, addressing different shortcomings of the current planning method. Moreover, the proposed solution represents a diagnostic and decision support tool because it helps this team to identify the potential gaps between capacity and demand and also it demonstrates how to adapt capacity to demand and how to optimally balance demand. From an academic perspective, this research adds empirical evidence to enrich the existing literature, as it not only presents a real case application, but also highlights issues that either are not entirely explored in other studies or must be adapted to properly model a specific but real-world production setting.

Fabricio Oliveira

Associate Professor of Operational Research

Fabricio Oliveira is an Associate Professor of Operational Research in the Department of Mathematics and Systems Analysis.