A reference architecture for the collaborative planning modelling process in multi-tier supply chain networks: a Zachman-based approach

A prominent and contemporary challenge for supply chain (SC) managers concerns the coordination of the efforts of the nodes of the SC in order to mitigate unpredictable market behaviour and satisfy variable customer demand. A productive response to this challenge is to share pertinent market-related information, on a timely basis, in order to effectively manage the decision-making associated with the SC production and transportation planning processes. This paper analyses the most well-known reference modelling languages and frameworks in the collaborative SC field and proposes a novel reference architecture, based upon the Zachman Framework (ZF), for supporting collaborative plan- ning (CP) in multi-level, SC networks. The architecture is applied to an automotive supply chain configuration, where, under a collaborative and decentralised approach, improvements in the service levels for each node were observed. The architecture was shown to provide the base discipline for the organisation of the processes required to manage the CP activity.The authors thanks the support from the project 'Operations Design and Management in Global Supply Chains (GLOBOP)' (Ref. DPI2012-38061-C02-01), funded by the Ministry of Science and Education of Spain, for the supply chain environment research contribution.Hernández Hormazábal, JE.; Lyons, AC.; Poler, R.; Mula, J.; Goncalves, R. (2014). A reference architecture for the collaborative planning modelling process in multi-tier supply chain networks: a Zachman-based approach. Production Planning and Control. 25(13-14):1118-1134. https://doi.org/10.1080/09537287.2013.808842S111811342513-14Al-Mutawah, K., Lee, V., & Cheung, Y. (2008). A new multi-agent system framework for tacit knowledge management in manufacturing supply chains. Journal of Intelligent Manufacturing, 20(5), 593-610. doi:10.1007/s10845-008-0142-0Baïna, S., Panetto, H., & Morel, G. (2009). New paradigms for a product oriented modelling: Case study for traceability. Computers in Industry, 60(3), 172-183. doi:10.1016/j.compind.2008.12.004Berasategi, L., Arana, J., & Castellano, E. (2011). A comprehensive framework for collaborative networked innovation. Production Planning & Control, 22(5-6), 581-593. doi:10.1080/09537287.2010.536628Chan, H. K., & Chan, F. T. S. (2009). A review of coordination studies in the context of supply chain dynamics. International Journal of Production Research, 48(10), 2793-2819. doi:10.1080/00207540902791843Chen, D., Doumeingts, G., & Vernadat, F. (2008). Architectures for enterprise integration and interoperability: Past, present and future. Computers in Industry, 59(7), 647-659. doi:10.1016/j.compind.2007.12.016Choi, Y., Kang, D., Chae, H., & Kim, K. (2006). An enterprise architecture framework for collaboration of virtual enterprise chains. The International Journal of Advanced Manufacturing Technology, 35(11-12), 1065-1078. doi:10.1007/s00170-006-0789-7Choi, Y., Kim, K., & Kim, C. (2005). A design chain collaboration framework using reference models. The International Journal of Advanced Manufacturing Technology, 26(1-2), 183-190. doi:10.1007/s00170-004-2262-9COLQUHOUN, G. J., BAINES, R. W., & CROSSLEY, R. (1993). A state of the art review of IDEFO. International Journal of Computer Integrated Manufacturing, 6(4), 252-264. doi:10.1080/09511929308944576Danilovic, M., & Winroth, M. (2005). A tentative framework for analyzing integration in collaborative manufacturing network settings: a case study. Journal of Engineering and Technology Management, 22(1-2), 141-158. doi:10.1016/j.jengtecman.2004.11.008Derrouiche, R., Neubert, G., Bouras, A., & Savino, M. (2010). B2B relationship management: a framework to explore the impact of collaboration. Production Planning & Control, 21(6), 528-546. doi:10.1080/09537287.2010.488932Dudek, G., & Stadtler, H. (2005). Negotiation-based collaborative planning between supply chains partners. European Journal of Operational Research, 163(3), 668-687. doi:10.1016/j.ejor.2004.01.014Gruat La Forme, F.-A., Genoulaz, V. B., & Campagne, J.-P. (2007). A framework to analyse collaborative performance. Computers in Industry, 58(7), 687-697. doi:10.1016/j.compind.2007.05.007Gutiérrez Vela, F. L., Isla Montes, J. L., Paderewski Rodríguez, P., Sánchez Román, M., & Jiménez Valverde, B. (2007). An architecture for access control management in collaborative enterprise systems based on organization models. Science of Computer Programming, 66(1), 44-59. doi:10.1016/j.scico.2006.10.005Hernández, J. E., Poler, R., Mula, J., & Lario, F. C. (2010). The Reverse Logistic Process of an Automobile Supply Chain Network Supported by a Collaborative Decision-Making Model. Group Decision and Negotiation, 20(1), 79-114. doi:10.1007/s10726-010-9205-7Hernández, J. E., J. Mula, R. Poler, and A. C. Lyons. 2013. “Collaborative Planning in Multi-Tier Supply Chains Supported by a Negotiation-Based Mechanism and Multi-Agent System.”Group Decision and Negotiation Journal. doi:10.1007/s10726-013-9358-2.Jardim-Goncalves, R., Grilo, A., Agostinho, C., Lampathaki, F., & Charalabidis, Y. (2013). Systematisation of Interoperability Body of Knowledge: the foundation for Enterprise Interoperability as a science. Enterprise Information Systems, 7(1), 7-32. doi:10.1080/17517575.2012.684401Kampstra, R. P., Ashayeri, J., & Gattorna, J. L. (2006). Realities of supply chain collaboration. The International Journal of Logistics Management, 17(3), 312-330. doi:10.1108/09574090610717509Kim, W., Chung, M. J., Qureshi, K., & Choi, Y. K. (2006). WSCPC: An architecture using semantic web services for collaborative product commerce. Computers in Industry, 57(8-9), 787-796. doi:10.1016/j.compind.2006.04.007Ku, K.-C., Kao, H.-P., & Gurumurthy, C. K. (2007). Virtual inter-firm collaborative framework—An IC foundry merger/acquisition project. Technovation, 27(6-7), 388-401. doi:10.1016/j.technovation.2007.02.010LEE, J., GRUNINGER, M., JIN, Y., MALONE, T., TATE, A., YOST, G., & OTHER MEMBERS OF THE PIF WORKING GROUP. (1998). The Process Interchange Format and Framework. The Knowledge Engineering Review, 13(1), 91-120. doi:10.1017/s0269888998001015Lee, J., Chae, H., Kim, C.-H., & Kim, K. (2009). Design of product ontology architecture for collaborative enterprises. Expert Systems with Applications, 36(2), 2300-2309. doi:10.1016/j.eswa.2007.12.042Liu, J., Zhang, S., & Hu, J. (2005). A case study of an inter-enterprise workflow-supported supply chain management system. Information & Management, 42(3), 441-454. doi:10.1016/j.im.2004.01.010Marques, D. M. N., & Guerrini, F. M. (2011). Reference model for implementing an MRP system in a highly diverse component and seasonal lean production environment. Production Planning & Control, 23(8), 609-623. doi:10.1080/09537287.2011.572469Mula, J., Peidro, D., & Poler, R. (2010). The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand. International Journal of Production Economics, 128(1), 136-143. doi:10.1016/j.ijpe.2010.06.007Murata, T. (1989). Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4), 541-580. doi:10.1109/5.24143Noran, O. (2003). An analysis of the Zachman framework for enterprise architecture from the GERAM perspective. Annual Reviews in Control, 27(2), 163-183. doi:10.1016/j.arcontrol.2003.09.002Olorunniwo, F. O., & Li, X. (2010). Information sharing and collaboration practices in reverse logistics. Supply Chain Management: An International Journal, 15(6), 454-462. doi:10.1108/13598541011080437Recker, J., Rosemann, M., Indulska, M., … Green, P. (2009). Business Process Modeling- A Comparative Analysis. Journal of the Association for Information Systems, 10(04), 333-363. doi:10.17705/1jais.00193Rodriguez, K., & Al-Ashaab, A. (2005). Knowledge web-based system architecture for collaborative product development. Computers in Industry, 56(1), 125-140. doi:10.1016/j.compind.2004.07.004Romero, F., Company, P., Agost, M.-J., & Vila, C. (2008). Activity modelling in a collaborative ceramic tile design chain: an enhanced IDEF0 approach. Research in Engineering Design, 19(1), 1-20. doi:10.1007/s00163-007-0040-zSandberg, E. (2007). Logistics collaboration in supply chains: practice vs. theory. The International Journal of Logistics Management, 18(2), 274-293. doi:10.1108/09574090710816977Spekman, R. E., & Carraway, R. (2006). Making the transition to collaborative buyer–seller relationships: An emerging framework. Industrial Marketing Management, 35(1), 10-19. doi:10.1016/j.indmarman.2005.07.002Stevens, W. P., Myers, G. J., & Constantine, L. L. (1974). Structured design. IBM Systems Journal, 13(2), 115-139. doi:10.1147/sj.132.0115Ulieru, M. (2000). A multi-resolution collaborative architecture for web-centric global manufacturing. Information Sciences, 127(1-2), 3-21. doi:10.1016/s0020-0255(00)00026-8Van der Aalst, W. M. P. (1999). Formalization and verification of event-driven process chains. Information and Software Technology, 41(10), 639-650. doi:10.1016/s0950-5849(99)00016-6Zachman, J. A. (1987). A framework for information systems architecture. IBM Systems Journal, 26(3), 276-292. doi:10.1147/sj.263.0276Zapp, M., Forster, C., Verl, A., & Bauernhansl, T. (2012). A Reference Model for Collaborative Capacity Planning Between Automotive and Semiconductor Industry. Procedia CIRP, 3, 155-160. doi:10.1016/j.procir.2012.07.028Zeng, Y., Wang, L., Deng, X., Cao, X., & Khundker, N. (2012). Secure collaboration in global design and supply chain environment: Problem analysis and literature review. Computers in Industry, 63(6), 545-556. doi:10.1016/j.compind.2012.05.00

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