Partner selection for interfirm collaboration in ship design

The purpose of this paper is to apply a mathematical method of formal concept analysis (FCA) to facilitate evaluation of potential partners, and to select the most appropriate partner for horizontal strategic alliances. Horizontal collaboration between ship design firms is important in relation to business cyclicality in the industry. The workload in ship design firms drops during the troughs of the shipbuilding cycle and increases dramatically during the peaks of the cycle.

The proposed method of partnership selection applies FCA, which is based on mathematical lattice theory. FCA allows firms to evaluate and select the best suitable partners for horizontal interfirm cooperation from several possible candidate firms. Utilization of FCA allows a firm to visually analyze a potential partner for a horizontal strategic alliance.

The contribution of this study to the literature is twofold. First, it contributes to the literature on the application of FCA in management field. Second, this study contributes to the partner selection literature. The contribution of the study is an alternative quantitative method for partner selection based on FCA. FCA compliments qualitative approaches in the process of alternatives evaluation and decision‐making regarding partner selection for horizontal collaboration.

Practitioners from ship design firms can use the FCA tool to facilitate decision‐making relating to the screening of potential partners for horizontal cooperation with regard to pre‐specified selected criteria.

FCA has been marginally applied to aid managerial decision making. The FCA tool is valuable for practitioners from ship design firms to manage the selection of partners for horizontal collaboration. The FCA tool is associated with numerous advantages, notably, relative simplicity and versatility of visual analysis when compared with other mathematical approaches such as the analytic hierarchy process, the analytic network process, optimization modeling, and fuzzy set logic.