An Evaluation Model of Supply Chain Performances Using 5DBSC and LMBP Neural Network Algorithm

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.

[1]  Phillip E. Pfeifer,et al.  Marketing Metrics: The Definitive Guide to Measuring Marketing Performance , 2010 .

[2]  Philip Richardson,et al.  Fitness for the future: applying biomimetics to business strategy , 2010 .

[3]  Gordon Stewart,et al.  Supply‐chain operations reference model (SCOR): the first cross‐industry framework for integrated supply‐chain management , 1997 .

[4]  S. Lamouri,et al.  A framework for analysing supply chain performance evaluation models , 2013 .

[5]  Monique Snoeck,et al.  Classification With Ant Colony Optimization , 2007, IEEE Transactions on Evolutionary Computation.

[6]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[7]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[8]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[9]  Zdzislaw Pawlak,et al.  Rough sets, decision algorithms and Bayes' theorem , 2002, Eur. J. Oper. Res..

[10]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[11]  Kai-Ying Chen,et al.  Applying back propagation network to cold chain temperature monitoring , 2011, Adv. Eng. Informatics.

[12]  R. Boxwell Benchmarking for competitive advantage , 1994 .

[13]  Bernard Bobée,et al.  Daily reservoir inflow forecasting using artificial neural networks with stopped training approach , 2000 .

[14]  Cao Qing-kui,et al.  A comparative inquiry into supply chain performance appraisal based on Support Vector Machine and neural network , 2008, 2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings.

[15]  Thomas L. Saaty,et al.  Conflict resolution : the analytic hierarchy approach , 1989 .

[16]  Luquan Ren,et al.  Progress in the bionic study on anti-adhesion and resistance reduction of terrain machines , 2009 .

[17]  Timothy Masters,et al.  Advanced algorithms for neural networks: a C++ sourcebook , 1995 .

[18]  Mostafa Zandieh,et al.  An immune algorithm approach to hybrid flow shops scheduling with sequence-dependent setup times , 2006, Appl. Math. Comput..

[19]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[20]  T. Seeley The Wisdom of the Hive: The Social Physiology of Honey Bee Colonies , 1995 .

[21]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[22]  R. Kaplan,et al.  The balanced scorecard--measures that drive performance. , 2015, Harvard business review.

[23]  Benita M. Beamon,et al.  Measuring supply chain performance , 1999 .

[24]  Vincent A. A. Jansen,et al.  Metapopulation persistence despite local extinction: predator-prey patch models of the Lotka-Volterra type , 1991 .

[25]  van Ke Kim Oorschot,et al.  Developing a balanced scorecard with System Dynamics , 2002 .

[26]  R. Kaplan,et al.  Using the balanced scorecard as a strategic management system , 1996 .

[27]  Kuan Yew Wong,et al.  A review on benchmarking of supply chain performance measures , 2008 .

[28]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[29]  Gunter Bolch,et al.  Queueing Networks and Markov Chains - Modeling and Performance Evaluation with Computer Science Applications, Second Edition , 1998 .

[30]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[31]  J. Vincent,et al.  Biomimetics: its practice and theory , 2006, Journal of The Royal Society Interface.

[32]  C. Tappert,et al.  A Genetic Algorithm for Constructing Compact Binary Decision Trees , 2009 .

[33]  Timothy Masters,et al.  Advanced algorithms for neural networks: a C++ sourcebook , 1995 .