Abstract
The problems of leather sector in Ethiopia starts from animal husbandry stage. This calls for intervention options as early as possible in the supply chain. In this paper livestock husbandry cluster is proposed to mitigate the problems of Ethiopian leather sector at animal husbandry stage. The first and the most important stage of industrial clustering procedure is identifying best area for cluster development. Livestock husbandry cluster identification is a strategic decision with uncertainties. To handle the uncertainties, Fuzzy-AHP based livestock husbandry cluster identification is proposed. Up to now, there is no research conducted on Fuzzy-AHP for livestock husbandry cluster identification. Therefore, the aim of this paper is to identify livestock husbandry cluster in Ethiopia using Fuzzy-AHP. As a result, three alternatives (i.e. West Gojjam, East Gojjam and North Shewa) and six quantitative and qualitative criteria (i.e. geographical proximity, sectorial concentration, market potential, support services, resource potential and potential entrepreneurs) are found. Finally, North Shewa is selected as best area for livestock husbandry clusters. A sensitivity analysis is also performed to justify the results.
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Jote, N., Beshah, B., Kitaw, D. (2015). Ethiopian Livestock Husbandry Cluster Identification Using FUZZY-AHP Approach. In: Abraham, A., Krömer, P., Snasel, V. (eds) Afro-European Conference for Industrial Advancement. Advances in Intelligent Systems and Computing, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-319-13572-4_19
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DOI: https://doi.org/10.1007/978-3-319-13572-4_19
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