Small- or Medium-Sized Enterprise Uses Operations Research to Select and Develop its Headquarters Location

Abstract

Location decisions are strategic and usually multicriteria. In decision making, companies need to anticipate future developments at potential locations. Company-driven and municipal development measures change location conditions over time. For location-seeking companies, the realization of municipal measures is fraught with uncertainty. They are planned by several municipal actors, and their long-term implications are hard to predict. Thus, the early-systematic consideration of company-internal and -external development measures is vital for decision makers (DMs) in a future-oriented location assessment. In our paper, we develop a robust decision support framework for companies to solve the regional facility location and development planning problem (RFLDP). Our framework includes a quantitative planning approach based on established operations research (OR) optimization models and a practical guideline for a structured acquisition of relevant data. The chief executive officer (CEO) (or DM) of a small- or medium-sized enterprise (SME) asked us to solve his acute RFLDP. For this, we proposed a systematic workflow and accompanied the SME’s regional facility location and development planning. In doing so, we structured the CEO’s decision-making process effectively and created an objective-transparent basis for his strategic decisions. The core feature of our work is the inclusion of the human factor of DMs, as we interacted with the CEO along his decision-making process to gradually develop decision recommendations. As a result, the SME benefited from a better-informed and transparent planning process. We recommended a decision option that was structurally superior to other options, which emerged from the CEO’s intuition and conventional facility location problem solution approaches. Other stakeholders also benefited from the results of our work. History: This paper was refereed. Funding: This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [Grant 439640382].