Abstract
Decision system technologies have long since been a strong support to model and solve planning complexities in the supply chain in a collaborative context. Moreover, one of the main topics to emerge is reverse logistics, which is becoming more relevant in supply chains in terms of the logistics process of removing new or used products from their initial point. Therefore, to present the main aspects that should be considered to share the decision information, which is already used among the members of the supply chain, a study of reverse logistics has been carried out to discover how decision-making activities support the process in supply chains. Furthermore, a simulation experiment has been performed with both the DGRAI 3.0 tool and Rockwell Arena 11® to observe the quality evolution of decision making and the economical impact that the proposed collaborative model will have on the current system. Moreover, this research work shows that a clear impact will appear on the decisional quality at the bottom levels of the supply chain than on the decisional quality of the whole system. The main work hypothesis is that the logistic process costs must lower given the implementation of the proposed collaborative model.
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References
Ackermann F, Franco LA, Gallupe G, Parent M (2005) GSS for multi-organizational collaboration: reflections on process and content. Group Decis Negot 14(4): 307–331
Aranguren R, Eirich P, Fox M, Jorgenson B, Karinthi R, Kosanke K, Lynch F, Maney G, Neches R, Speyer B (1992) The process of modelling and model integration. In: Petrie C (ed) Proceedings of the First International Conference on Enterprise Integration Modelling, Working Group 3 of ICEIMT workshop I. MIT Press, London
Ashayeri J, Kampstra P (2003) Collaborative replenishment: a step-by-step approach, Ref: KLICT Project: OP-054, Dynamic Green Logistics, Tilburg University
Bogataj M, Bogataj L (2004) On the compact presentation of the lead times perturbations in distribution networks. Int J Prod Econ 88(2): 145–155
Chen D, Vallespir B, Doumeingts G (1997) GRAI integrated methodology and its mapping onto generic enterprise reference architecture and methodology. Comput Ind 33: 387–394
Chen HK, Chou HW, Chiu YC (2007) On the modeling and solution algorithm for the reverse logistics recycling flow equilibrium problem. Transp Res Part C Emerg Technol 15(4): 218–234
Cooper M, Eilram LM, Gardner JT, Hanks AM. (1997) Meshing multiple alliances. J Bus Logist 18(I):67–89. In http://findarticles.com/p/articles/mi_qa3705/is_199701/ai_n8753721. Cited 10 Oct 2009
Di Domenica N, Mitra G, Valente P, Birbilis G (2007) Stochastic programming and scenario generation within a simulation framework: an information systems perspective. Decis Support Syst 42(4): 2197–2218
Doumeingts G (1984) Méthode GRAI: méthode de conception des systémes en productique (Thése d’état: Automatique: Université de Bordeaux 1)
Doumeingts G, Chen D, Marcotte D (1992) Concepts, models and methods for the design of production management systems. Comput Ind 19: 89–111
Doumeingts G, Chen D, Vallespir B, Fenie P (1994) GRAI integrated methodology (GIM) and its evolutions: a methodology to design and specify advanced manufacturing systems. IFIP Trans B Comput Appl Technol B-14: 101–117
Dudek G, Stadtler H (2005) Negotiation-based collaborative planning between supply chains partners. Eur J Oper Res 163(3): 668–687
Franco LA (2008) Facilitating collaboration with problem structuring methods: a case study of an inter-organisational construction partnership. Group Decis Negot 17(4): 267–286
Fu Y, Piplani R, de Souza R, Jingru W (2000) Multi-agent enabled modelling and simulation towards collaborative inventory management in supply chains. In: Paper presented at the 32nd winter simulation conference proceedings, vol. 2. pp 1763–1771
Georgiadis P, Vlachos D (2004) Decision making in reverse logistics using system dynamics. Yugosl J Oper Res 14(2): 259–272
Gimpel H (2007) Loss aversion and reference-dependent preferences in multi-attribute negotiations. Group Decis Negot 16(4): 303–319
Gray B (1989) Collaborating: finding common ground for multiparty problems. Jossey-Bass, San Francisco
Hernández JE, Poler R, Mula J (2007) A conceptual decisional model of the reverse logistic function in the supply chain. In: An application to the automobile sector. Paper presented at the IV Congreso La investigación Ante la Sociedad del Conocimiento. Sostenibilidad y Medio Ambiente, Alcoy. Spain. 14–16 November 2007
Hernández JE, Mula J, Ferriols FJ, Poler R (2008) A conceptual model for the production and transport planning process: an application to the automobile sector. Comput Ind 59(8): 842–852
Johnson PF, Leenders MR (2006) Make-or-buy alternatives in plant disposition strategies. J Supply Chain Manage 33(2): 20–26
Jung HS, Jeong B (2005) Decentralised production-distribution planning system using collaborative agents in supply chain network. Int J Adv Manuf Technol 25(1–2): 167–173
Ko HJ, Evans GW (2007) A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Comput Oper Res 34(2): 346–366
Kopicky RJ, Berg MJ, Legg L, Dasappa V, Maggioni C (1993) Reuse and recycling: reverse logistics opportunities. Council of Logistics Management, Oak Brook
Lai G, Li C, Sycara K (2006) Efficient multi-attribute negotiation with incomplete information. Group Decis Negot 15(5): 511–528
Lambert MD, Cooper MC (2000) Issues in supply chain management. Ind Mark Manage 29: 65–83
La Forme FG, Genoulaz VB, Campagne J (2007) A framework to analyse collaborative performance. Comput Ind 58(7): 687–697
Lee J, McShane H, Kozlowski W (2002) Critical issues in establishing a viable supply chain/reverse logistic management program. In: Paper presented at the 2002 IEEE international symposium on electronics & the environment, conference record. San Francisco, USA, pp 150–156. 6–9 May 2002
Lieckens K, Vandaele N (2007) Reverse logistics network design with stochastic lead times. Comput Oper Res 34(2): 395–416
Logozar K, Radonjic G, Bastic M (2006) Incorporation of reverse logistics model into in-plant recycling process: a case of aluminium industry. Resour Conserv Recycl 49(1): 49–67
Mattessich PW, Monsey BR (1992) Collaboration: what makes it work? In: Research literature on factors influencing successful collaboration. Amherst H. Wilder Foundation, St Paul, MN
Mesarovic MD, Macko D, Takahara Y (1970) Theory of hierarchical multi-level systems. Academic Press, Londres
Minner S (2001) Strategic safety stocks in reverse logistics supply chains. Int J Prod Econ 71(1–3): 417–428
Poler R, Lario FC, Doumeingts G (2002) Dynamic modelling of decision systems (DMDS). Comput Ind 49(2): 175–193
Poler R, Hernández JE, Mula J, Lario FC (2008) Collaborative forecasting in networked manufacturing enterprises. J Manuf Technol Manage 19(4): 514–528
Rogers D, Tibben-Lembke R (1998) Going Backwards: reverse logistics trends and practices. University of Nevada, Reno, Center of logistics management
Saetta S, Tiacci L (2003) Modelling and simulation of the supply chain: a problem of preventive transshipment. In: Paper presented at the 2003 summer computer simulation conference, Montreal, Canada, 20–24 July 2003
Schultmann F, Zumkeller M, Rentz O (2006) Modeling reverse logistic tasks within closed-loop supply chains: an example from the automotive industry. Eur J Oper Res 171(3): 1033–1050
Simon HA (1996) The sciences of the artificial, 3rd edn. MIT Press, Cambridge
Soosay CA, Hyland PW, Ferrer M (2008) Supply chain collaboration: capabilities for continuous innovation. Supply Chain Manage Int J 13(2): 160–169
Soubie J, Zaraté P (2005) Distributed decision making: a proposal of support through cooperative systems. Group Decis Negot 14(2): 147–158
Srivastava SK (2007) Green supply chain management: a state-ofthe-art literature review. Int J Manage Rev 9(1): 53–80
Stock JR (1992) Reverse logistics. Council of Logistics Management, OAK Brook
Tatineni VC, Demetsky MJ (2005) Supply chain models for freight transportation planning. University of Virginia, USA
Tosh M (1998) Focus on forecasting. Prog Groc 77(10): 113–114
Winer M, Ray K (1994) Collaboration handbook: creating, sustaining and enjoying the journey. Amherst H. Wilder Foundation, Minnesota
Yang HL, Wang CS (2007) Integrated framework for reverse logistics. Lecture notes in computer science. Springer Berlin, Heidelberg, pp, pp 501–510
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This research has been carried out in the framework of a project funded by the Ministry of Science and Education of Spain, entitled Simulation and evolutionary computation and fuzzy optimisation models of transportation and production planning processes in a supply chain. Proposal of collaborative planning supported by multi-agent systems. Integration in a decision system. Applications (EVOLUTION project, DPI2007-65501, www.cigip.upv.es/evolution).
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Hernández, J.E., Poler, R., Mula, J. et al. The Reverse Logistic Process of an Automobile Supply Chain Network Supported by a Collaborative Decision-Making Model. Group Decis Negot 20, 79–114 (2011). https://doi.org/10.1007/s10726-010-9205-7
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DOI: https://doi.org/10.1007/s10726-010-9205-7