Enhancing business intelligence for supply chain operations through effective classification of supplier management

Chen, Y.; Chiu, Y.

Abstract: Global supply chains have to manage production over the whole world. Therefore, production plants are needed to supply the demand of products and parts. Due to complication and uncertainty of production market, portfolio selection is one of the most challenging problems. Type-2(T2) fuzzy is a model, which provides the ability to handle the effect of uncertainty. Aiming at this problem, we propose a T2 supplier management system operation scheme, which not only employs fuzzy C-Means clustering algorithm by dynamically increasing cluster center, but also it achieves good classification performance. The key result is that fuzzy classification applications improve the planning and operating of supply and demand in a distributed production and global supply chain.

How to cite this paper

        APA: Chen, Y & Chiu, Y. (2014). Enhancing business intelligence for supply chain operations through effective classification of supplier management. Uncertain Supply Chain Management, 2(4), 229-236.
    

        Chicago/Turabian: Chen, Y & Chiu, Y. 2014. "Enhancing business intelligence for supply chain operations through effective classification of supplier management." Uncertain Supply Chain Management 2, no. 4 (2014): 229-236.
    

        AMA: Chen, Y & Chiu, Y. Enhancing business intelligence for supply chain operations through effective classification of supplier management. Uncertain Supply Chain Management. 2014;2(4):229-236.
    

References

Aliev, R. A., Fazlollahi, B., Guirimov, B. G., & Aliev, R. R. (2007). Fuzzy-genetic approach to aggregate production–distribution planning in supply chain management. Information Sciences, 177(20), 4241-4255.

Choi, B. I., & Chung-Hoon Rhee, F. (2009). Interval type-2 fuzzy membership function generation methods for pattern recognition. Information Sciences,179(13), 2102-2122.

Fan, M., Stallaert, J., & Whinston, A. B. (2003). Decentralized mechanism design for supply chain organizations using an auction market. Information Systems Research, 14(1), 1-22.

Hashemzadeh, G., Modiri, M., & Rahimi, Z. (2014). Identification and ranking effective factors on establishment of green supply chain management in railway industry. Uncertain Supply Chain Management. 2(4).

Hayes, J., & Finnegan, P. (2005). Assessing the of potential of e-business models: towards a framework for assisting decision-makers. European Journal of Operational Research, 160(2), 365-379.

Hwang, C., & Rhee, F. H. (2007). Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means. Fuzzy Systems, IEEE Transactions on, 15(1), 107-120.

Karnik, N. N., & Mendel, J. M. (1999). Applications of type-2 fuzzy logic systems to forecasting of time-series. Information Sciences, 120(1), 89-111.

Karnik, N. N., & Mendel, J. M. (2001). Centroid of a type-2 fuzzy set.Information Sciences, 132(1), 195-220.

Radjou, N. (2002). Building an adaptive supply network. Supply-Chain World North America, April 22-24, New Orleans, LA.

Chung-Hoon Rhee, F. (2007). Uncertain fuzzy clustering: insights and recommendations. Computational Intelligence Magazine, IEEE, 2(1), 44-56.

Wu, D., & Mendel, J. M. (2009). Enhanced karnik--mendel algorithms. Fuzzy Systems, IEEE Transactions on, 17(4), 923-934.

Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information sciences, 8(3), 199-249.

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