A model for supply management of agile manufacturing supply chains

This paper addresses the configuration problem of Manufacturing Supply Chains (MSC) with reference to the supply planning issue. Assuming that the manufacturing system is composed of different stages, we present a technique for the strategic management of the chain addressing supply planning and allowing the improvement of the MSC agility in terms of ability in reconfiguration to meet performance. More in detail, we enhance a previous design method by some of the authors that employs digraph modeling and integer linear programming to optimally design the MSC. The original approach avoids supply chain disruption and stock out and, at the same time, can manage spare parts distribution. In order to take into account the level of demands and maximum production capacities with single/multiple sourcing, in this new formulation we introduce supplier capacity constraints. A case study is presented describing the optimal MSC configuration of an Italian manufacturing firm. The obtained results show that the design method provides managers with key answers to issues related to the supply chain strategic configuration and agility, e.g., choosing the right location for distributors and retailers for enhanced MSC flexibility and performance.

[1]  Maria Grazia Scutellà,et al.  Distribution network design: New problems and related models , 2005, Eur. J. Oper. Res..

[2]  G. Nemhauser,et al.  Integer Programming , 2020 .

[3]  N. Costantino,et al.  Choosing between single and multiple sourcing based on supplier default risk: A real options approach , 2010 .

[4]  MengChu Zhou,et al.  Design and optimization of integrated E-supply chain for agile and environmentally conscious manufacturing , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  MengChu Zhou,et al.  Introduction to Petri Nets in Flexible and Agile Automation , 1995 .

[6]  Jinwoo Park,et al.  A combined model of network design and production/distribution planning for a supply network , 2002 .

[7]  R. C. Baker,et al.  A multi-phase mathematical programming approach for effective supply chain design , 2002, Eur. J. Oper. Res..

[8]  Reza Zanjirani Farahani,et al.  A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain , 2008 .

[9]  Naiqi Wu,et al.  An approach to partner selection in agile manufacturing , 1999, J. Intell. Manuf..

[10]  Matthias Ehrgott,et al.  Multicriteria Optimization , 2005 .

[11]  Nukala Viswanadham,et al.  Partner selection and synchronized planning in dynamic manufacturing networks , 2003, IEEE Trans. Robotics Autom..

[12]  MengChu Zhou,et al.  An integrated e-supply chain model for agile and environmentally conscious manufacturing , 2001 .

[13]  David J. Barnes,et al.  Formulating partner selection criteria for agile supply chains: A Dempster-Shafer belief acceptability optimisation approach , 2010 .

[14]  Anna Nagurney,et al.  Optimal Supply Chain Network Design and Redesign at Minimal Total Cost and with Demand Satisfaction , 2010 .

[15]  M. Naim,et al.  Supply chain flexibility as a determinant of supplier selection , 2010 .

[16]  M. Christopher The Agile Supply Chain : Competing in Volatile Markets , 2000 .

[17]  Mariagrazia Dotoli,et al.  A decision support system framework for purchasing management in supply chains , 2009 .

[18]  S. Chopra,et al.  Supply Chain Management: Strategy, Planning & Operation , 2007 .

[19]  Erick C. Jones,et al.  Multi-objective stochastic supply chain modeling to evaluate tradeoffs between profit and quality , 2010 .

[20]  Hong Yan,et al.  A strategic model for supply chain design with logical constraints: formulation and solution , 2003, Comput. Oper. Res..

[21]  Frederick S. Hillier,et al.  Introduction of Operations Research , 1967 .

[22]  P. Tsiakis,et al.  OPTIMAL PRODUCTION ALLOCATION AND DISTRIBUTION SUPPLY CHAIN NETWORKS , 2008 .