Multicriteria Supplier Selection for Supply Chain Management

Under the pressure of global competition, cycle time reduction and increasing product complexity, enterprises have realized that internal improvements are certainly important but not sufficient. Company performances depend not only on internal activities but also on external resources from supply networks made up of customers, suppliers, and subcontractors. In the present paper, the conventional choice of suppliers involved in a supply network is reconsidered. The process proposed to support decisions in a context of choice is based on the exploitation of a Simulation of Extended Enterprises ‘SEE’ platform for the configuration of a multicriteria method (Analytic Hierarchy Process ‘AHP’) and the analysis of the choice of suppliers. This multicriteria method, restructured into two phases, is presented and the implementation process is described. The case study focuses on snowmobile assembly with 75 workstations in a multi-product assembly line, 420 supplied parts and 48 suppliers. Multiple simulation cases are presented and the obtained results are analysed.

[1]  Iraj Mahdavi,et al.  Electronic Supply Network Coordination in Intelligent and Dynamic Environments: Modeling and Implementation , 2010 .

[2]  Patrick Pujo,et al.  Study of an intelligent and multicriteria scheduling service, using academic benchmarks , 2016, Int. J. Comput. Integr. Manuf..

[3]  Benoît Montreuil,et al.  Holistic modelling, simulation and visualisation of demand and supply chains , 2015, Int. J. Bus. Perform. Supply Chain Model..

[4]  F. Ounnar,et al.  Evaluating suppliers within a self‐organized logistical network , 2005 .

[5]  Patrick Pujo,et al.  PROSIS: An isoarchic structure for HMS control , 2009, Eng. Appl. Artif. Intell..

[6]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[7]  Bilge Bilgen,et al.  Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem , 2010, Expert Syst. Appl..

[8]  Benoît Montreuil,et al.  Toward a Physical Internet: meeting the global logistics sustainability grand challenge , 2011, Logist. Res..

[9]  Damien Trentesaux,et al.  Future Industrial Systems: Best Practices of the Intelligent Manufacturing and Services Systems (IMS2) French Research Group , 2017, IEEE Transactions on Industrial Informatics.

[10]  Sergio Cavalieri,et al.  Simulation in the supply chain context: a survey , 2004, Comput. Ind..

[11]  Benoît Montreuil,et al.  Toward a methodological framework for agent-based modelling and simulation of supply chains in a mass customization context , 2007, Simul. Model. Pract. Theory.

[12]  Michael Tracey,et al.  Current purchasing practices and JIT: some of the effects on inbound logistics , 1995 .

[13]  Benoît Montreuil,et al.  Modeling client profiles for order promising and delivery , 2013, Simul. Model. Pract. Theory.

[14]  Patrick Pujo,et al.  Service Oriented Architecture for Holonic Isoarchic and Multicriteria Control , 2012, Service Orientation in Holonic and Multi-Agent Manufacturing Control.

[15]  Patrick Pujo,et al.  Wireless Holon Network for job shop isoarchic control , 2016, Comput. Ind..

[16]  Tillal Eldabi,et al.  Simulation in manufacturing and business: A review , 2010, Eur. J. Oper. Res..

[17]  R. Johnston,et al.  Operations and Process Management: Principles and Practice for Strategic Impact , 2006 .

[18]  László Monostori,et al.  Agent-based systems for manufacturing , 2006 .

[19]  Patrick Pujo,et al.  Pull control for job shop: holonic manufacturing system approach using multicriteria decision-making , 2012, J. Intell. Manuf..

[20]  B. Montreuil Production planning optimization modeling in demand and supply chains of high-value consumer products , 2005 .

[21]  Paulo Leitão,et al.  Agent-based distributed manufacturing control: A state-of-the-art survey , 2009, Eng. Appl. Artif. Intell..