A three-stage field service management model for effective post-sales service supply chain management

Addressing the dynamic and unique characteristics of post-sales field service management, this paper proposes a Three-Stage Field Service Management (3S-FSM) model with optimisation and advanced information technology capabilities for effective post-sales e-service supply chain management. The 3S-FSM model involves a novel approach to creating a new paradigm of supply chain management focusing on implementing integrated Condition-Based Maintenance (CBM) and intelligent field service scheduling solutions in three areas: (1) prognostic CBM capabilities enabled by data mining, (2) prescheduling service territory and field service planning by mathematical programming and service job clustering techniques and (3) multi-criteria field service scheduling and post-scheduling systems learning using fuzzy logic.

[1]  Kazuo Tanaka,et al.  Parallel distributed compensation of nonlinear systems by Takagi-Sugeno fuzzy model , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[2]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Theodosios Pavlidis,et al.  Fuzzy Decision Tree Algorithms , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  J. Bezdek,et al.  Generalized k -nearest neighbor rules , 1986 .

[5]  Albert H. C. Tsang,et al.  Condition-based maintenance: tools and decision making , 1995 .

[6]  H. Ishibuchi,et al.  Distributed representation of fuzzy rules and its application to pattern classification , 1992 .

[7]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[8]  Salih O. Duffuaa,et al.  A generic conceptual simulation model for maintenance systems , 2001 .

[9]  Paul R. Drake,et al.  Condition-based maintenance and machine diagnostics , 1994 .

[10]  Uday S. Karmarkar,et al.  Performance Evaluation of Service Territories , 1992, Oper. Res..

[11]  Marcus Bengtsson.  Condition Based Maintenance System Technology - Where is Development Heading? , 2004 .

[12]  J. Yen,et al.  Fuzzy Logic: Intelligence, Control, and Information , 1998 .

[13]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[14]  A structured approach to the selection of condition based maintenance , 1997 .

[15]  Oded Berman,et al.  Operations Management of Distributed Service Networks: A Practical Quantitative Approach , 1988 .

[16]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Paul Gray,et al.  Introduction to Data Mining and Knowledge Discovery , 1998, Proceedings of the Thirty-First Hawaii International Conference on System Sciences.

[18]  Gerald M. Knapp,et al.  Statistical‐based or condition‐based preventive maintenance? , 1995 .

[19]  Hau L. Lee,et al.  Information sharing in a supply chain , 2000, Int. J. Manuf. Technol. Manag..

[20]  Phipps Arabie,et al.  AN OVERVIEW OF COMBINATORIAL DATA ANALYSIS , 1996 .

[21]  Yiqing Lin,et al.  A simulation model for field service with condition-based maintenance , 2002, Proceedings of the Winter Simulation Conference.

[22]  Joongseon Joh,et al.  On the stability issues of linear Takagi-Sugeno fuzzy models , 1998, IEEE Trans. Fuzzy Syst..

[23]  Ian Witten,et al.  Data Mining , 2000 .