AHP-based micro and small enterprises' cluster identification

Micro and Small Enterprises' (MSEs) cluster is a group of small firms operating in a defined geographic location, producing similar products or services, cooperating and competing with one another, learning from each other to solve internal problems, setting common strategies to overcome external challenges, and reaching distance markets through developed networks. During recent years, identifying MSEs cluster has become a key strategic decision. However, the nature of these decisions is usually complex and involves conflicting criteria. The aim of this paper is to develop an AHP-based MSEs cluster identification model. As a result, quantitative and qualitative factors including geographical proximity, sectorial concentration, market potential, support services, resource potential and potential entrepreneurs are found to be critical factors in cluster identification. In this paper, linguistic values are used to assess the ratings and weights of the factors. Then, AHP model will be proposed in dealing with the cluster selection problems. Finally, a case study was taken to prove and validate the procedure of the proposed method. A sensitivity analysis is also performed to justify the results.