相似係數於皮革工業上之應用：以印尼為例

Similarity Coefficient for Leather Industry Clusters: Case Study in Indonesia

蔣黛薇(Devi Jayawati)

指導教授 ： 洪暉智

國立交通大學/管理學院/工業工程與管理系所/碩士(2014年)

https://doi.org/10.6842/NCTU.2014.00343

摘要

As the fast growing of leather industry in Indonesia, it is urgent to figure out the strategies for successful cluster of leather industries. In this work, we apply quantitative methods with the idea of grouping technology and introduce the similarity coefficient for clusters of leather industry. Similarity coefficient is the quantified degree of similarity to compare a cluster to the benchmark of successful clusters. Benchmarks are selected first as the best cluster. Then clusters are compared to the benchmark pairwisely. Critical successful clusters are caught as a string and weighted by their importance levels. For a pair cluster, similarity coefficient is composed of weighted merger distance between two strings. We then collect similarity coefficients of successful clusters as fingerprints in building new clusters of leather industry. As a case study, clusters of leather industry in Indonesia are examined. It gives recommendation to related government and sheds light on the road of success.

關鍵字

Cluster of Leather Industry, Grouping technology, Similarity coefficient, Merger distance, Importance weights

並列摘要

As the fast growing of leather industry in Indonesia, it is urgent to figure out the strategies for successful cluster of leather industries. In this work, we apply quantitative methods with the idea of grouping technology and introduce the similarity coefficient for clusters of leather industry. Similarity coefficient is the quantified degree of similarity to compare a cluster to the benchmark of successful clusters. Benchmarks are selected first as the best cluster. Then clusters are compared to the benchmark pairwisely. Critical successful clusters are caught as a string and weighted by their importance levels. For a pair cluster, similarity coefficient is composed of weighted merger distance between two strings. We then collect similarity coefficients of successful clusters as fingerprints in building new clusters of leather industry. As a case study, clusters of leather industry in Indonesia are examined. It gives recommendation to related government and sheds light on the road of success.

並列關鍵字

Cluster of Leather Industry, Grouping technology, Similarity coefficient, Merger distance, Importance weights

參考文獻

Sun, H., and Wang, J. (2013). Industrial Integration in Changchun and Jilin City Based on the Similar Coefficients of Industrial Structure, American Journal of Industrial and Business Management, 127–130. doi: 10.4236/ajibm.2013.32016

Athiyaman, A., and Parkan, C. (2008). A Functionalist Framework for Identifying Business Clusters: Applications in Far North Queensland. Australian Journal of Management, 33 (1), 201‐229. Retrieved from http://aum.sagepub.com/content/33/1/201

Aziz, K. A., and Norhashim, M. (2008). Cluster‐Based Policy Making: Assessing Performance and Sustaining Competitiveness. Review of Policy Research, 25 (4), 349‐375. doi: 10.1111/j.1541-1338.2008.00336.x

Gower, J.C., 1971. A General Coefficient of Similarity and Some of its properties, Biometrics 27, 857–74.s

Hallencreutz, D., and Lundequist, P. (2003). Spatial Clustering and the Potential for Policy Practice: Experiences from Cluster‐building Processes in Sweden. European Planning Studies, 11 (5), 534‐547. doi: 10.1080/09654310303654