A computer-integrated FMEA for dynamic supply chains in a flexible-based environment

Quality management (QM) for dynamic supply chains (DSCs) in a flexible-based environment has received increasing attention in current business environment. This study focuses on the development of an QM tool, based on the failure modes and effects analysis (FMEA) approach, and demonstrates how it can be extended from application in supply chain management (SCM) to other business environments. The main contributions are to implement an iterative process using FMEA for DSCs in a flexible-based environment by overcoming the weaknesses associated with traditional FMEA system and to develop a user-friendly interface, with which the distributed parties in chains could cooperate as a whole and meet the needs of dynamic control. In specific, this paper proposes a computer-integrated FMEA approach that enhanced FMEA in SCM through automated processing using a fuzzy approach and a computer-integrated and internet-based interface to support the system implementation. The efficiency and practical implication of the proposed system has been validated by the developed system with a case study. The developed system can help prevent possible failures in the design and operations and, furthermore, the assurance of quality for DSCs in a flexible-based environment. The proposed system may easily be extended to more complex real-world applications.

[1]  Harold Ascher,et al.  Handbook of Reliability Engineering and Management , 1988 .

[2]  S. G. Deshmukh,et al.  Select supplier-related issues in modelling a dynamic supply chain: potential, challenges and direction for future research , 2009 .

[3]  Sinn-Cheng Lin.  Dynamic-link rule base in fuzzy inference system , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[4]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[5]  Ming Xie.  Fundamentals of Robotics - Linking Perception to Action , 2003, Series in Machine Perception and Artificial Intelligence.

[6]  Ping Lou,et al.  Study on multi-agent-based agile supply chain management , 2004 .

[7]  Felix T.S. Chan,et al.  Framework for flexibility in dynamic supply chain management , 2008 .

[8]  Jian-Bo Yang,et al.  Development of a fuzzy FMEA based product design system , 2008 .

[9]  Yaochu Jin,et al.  Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement , 2000, IEEE Trans. Fuzzy Syst..

[10]  M. H. Korayem,et al.  Design, manufacturing, and experimental tests of a prismatic robot for assembly line , 2006 .

[11]  Sheng‐Hsien Teng,et al.  Failure mode and effects analysis: An integrated approach for product design and process control , 1996 .

[12]  Jon Sticklen,et al.  Distributed causal reasoning , 1989 .

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

[14]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[15]  J. B. Bowles,et al.  Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis , 1995 .

[16]  William C. Jordan,et al.  Principles on the benefits of manufacturing process flexibility , 1995 .

[17]  Dong Li,et al.  Dynamic planning with a wireless product identification technology in food supply chains , 2006 .

[18]  C. Madu,et al.  Identifying Critical Success Factors for Supply Chain Quality Management (SCQM) , 2001 .

[19]  S. Gary Teng,et al.  Implementing FMEA in a collaborative supply chain environment , 2006 .

[20]  Jian-Bo Yang,et al.  Dynamic analysis of a two-stage supply chain—a switched system theory approach , 2009 .

[21]  Chee Peng Lim,et al.  Fuzzy FMEA with a guided rules reduction system for prioritization of failures , 2006 .

[22]  John Yen,et al.  Simplifying fuzzy rule-based models using orthogonal transformation methods , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[23]  Christian N. Madu,et al.  Implementing supply chain quality management , 2008 .

[24]  Selwyn Piramuthu,et al.  Production , Manufacturing and Logistics Knowledge-based framework for automated dynamic supply chain configuration , 2003 .

[25]  Kwai-Sang Chin,et al.  A computer-integrated framework for global quality chain management , 2006 .

[26]  S. T. Foster,et al.  Towards an understanding of supply chain quality management , 2008 .

[27]  Jin Wang,et al.  Modified failure mode and effects analysis using approximate reasoning , 2003, Reliab. Eng. Syst. Saf..

[28]  Samuel M. Smith,et al.  A simple weight based fuzzy logic controller rule base reduction method , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[29]  Mark Lee,et al.  Automating the FMEA process , 1993 .

[30]  M. Malhotra,et al.  Defining the concept of supply chain quality management and its relevance to academic and industrial practice , 2005 .

[31]  Abdul Raouf,et al.  A revised failure mode and effects analysis model , 1996 .

[32]  Hale Kaynak,et al.  A replication and extension of quality management into the supply chain , 2008 .

[33]  Sime Curkovic,et al.  An examination of ISO 9000:2000 and supply chain quality assurance , 2008 .

[34]  Wen-Tsann Lin,et al.  Failure evaluation and the establishment of an improvement model for product data management introduced to enterprises , 2006 .

[35]  Gyu-Bong Lee,et al.  Module-based Failure Propagation (MFP) model for FMEA , 2011 .