An Evaluation Model of Supply Chain Performances Using 5DBSC and LM-BP Neural Network Algorithm

Fan, Xuemei, Zhang, Shujun ORCID: 0000-0001-5699-2676, Hapeshi, Kevin, Wang, Longzhao and Yang, Yinsheng (2013) An Evaluation Model of Supply Chain Performances Using 5DBSC and LM-BP Neural Network Algorithm. Journal of Bionic Engineering, 10 (3). pp. 383-395. doi:10.1016/S1672-6529(13)60234-6

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Abstract

A high efficient Supply Chain (SC) would bring great benefits to an enterprise such as integrated resources, reduced logistics costs, improved logistics efficiency and high quality of overall level of services. So it is important to research various methods, performance indicator systems and technology for evaluating, monitoring, predicting and optimizing the performance of a SC. In this paper, the existing performance indicator systems and methods are discussed and evaluated. Various nature-inspired algorithms are reviewed and their applications for SC Performance Evaluation (PE) are discussed. Then, a model is proposed and developed using 5 Dimensional Balanced Scorecard (5DBSC) and LMBP (Levenberg-Marquardt Back Propagation) neural network for SC PE. A program is written using Matlab tool box to implement the model based on the practical values of the 14 indicators of 5DBSC of a given previous period. This model can be used to evaluate, predict and optimize the performance of a SC. The analysis results of a case study of a company show that the proposed model is valid, reliable and effective. The convergence speed is faster than that in the previous work.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	supply chain; performance evaluation; 5DBSC; bionics; LMBP neural network
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HF Commerce
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Ineke Tijsma
Date Deposited:	17 Mar 2015 16:25
Last Modified:	31 Aug 2023 08:01
URI:	https://eprints.glos.ac.uk/id/eprint/1510

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