A Parallelizable Heuristic for Solving the Generic Materials and Operations Planning in a Supply Chain Network: A Case Study from the Automotive Industry

[EN] A trend in up-to date developments in multi-site operations planning models is to consider in details the different ways to produce, buy or transport products and the distributed decision-making process for operations planning. One of the most generic approaches to support global optimization in those supply chain networks by considering all the different operations alternatives and product structures is the Generic Materials & Operations Planning Problem. This problem can be modelled by a Mixed Integer Linear Programming model capable of considering production, transportation, procurement tasks and their alternatives and other relevant issues such as packaging. The aim of this paper is to introduce the implementation of a parallelizable heuristic method for materials and operations planning and its application to a case of a Supply Chain Network of the automotive industry. The approach uses variants of the GMOP model to overcome traditional MRP systems' limitations.Maheut ., JP.; García Sabater, JP. (2013). A Parallelizable Heuristic for Solving the Generic Materials and Operations Planning in a Supply Chain Network: A Case Study from the Automotive Industry. IFIP Advances in Information and Communication Technology. 397:151-157. doi:10.1007/978-3-642-40352-1_20S151157397Maheut, J., Garcia-Sabater, J.P.: La Matriz de Operaciones y Materiales y la Matriz de Operaciones y Recursos, un nuevo enfoque para resolver el problema GMOP basado en el concepto del Stroke. Dirección y Organización 45, 46–57 (2011)Garcia-Sabater, J.P., Maheut, J., Marin-Garcia, J.A.: A new formulation technique to model Materials and Operations Planning: the Generic Materials and Operations Planning (GMOP) Problem. European J. Industrial Engineering 7, 119–147 (2013)Mula, J., Maheut, J., Garcia-Sabater, J.P.: Supply Chain Network Design. Journal of Marketing and Operations Management Research 1, 378–383 (2012)Dudek, G., Stadtler, H.: Negotiation-based collaborative planning between supply chains partners. European Journal of Operational Research 163, 668–687 (2005)Torabi, S.A., Hassini, E.: Multi-site production planning integrating procurement and distribution plans in multi-echelon supply chains: an interactive fuzzy goal programming approach. International Journal of Production Research 47, 5475–5499 (2009)Kanyalkar, A.P., Adil, G.K.: Aggregate and detailed production planning integrating procurement and distribution plans in a multi-site environment. International Journal of Production Research 45, 5329–5353 (2007)de Kok, T.G., Fransoo, J.C.: Planning Supply Chain Operations: Definition and Comparison of Planning Concepts. In: Graves, S.C. (ed.) Handbooks in Operations Research and Management Science Supply Chain Management: Design, Coordination and Operation, vol. 11, pp. 597–675. Elsevier (2003)Buschkühl, L., Sahling, F., Helber, S., Tempelmeier, H.: Dynamic capacitated lot-sizing problems: a classification and review of solution approaches. OR Spectrum (2009)Maheut, J., Garcia-Sabater, J.P., Mula, J.: A supply Chain Operations Lot-Sizing and Scheduling Model with Alternative Operations. In: Sethi, S.P., Bogataj, M., Ros-McDonnell, L. (eds.) Proceedings of the Industrial Engineering: Innovative Networks, 5th International Conference on Industrial Engineering and Industrial Management "CIO 2011", Cartagena, Spain, pp. 309–316. Springer, London (2012)Garcia-Sabater, J.P., Maheut, J., Garcia-Sabater, J.J.: A two-stage sequential planning scheme for integrated operations planning and scheduling system using MILP: the case of an engine assembler. Flexible Services and Manufacturing Journal 24, 171–209 (2012)Pinto, J.M., Chen, P., Papageorgiou, L.G.: A discrete/continuous time MILP model for medium term planning of single stage multiproduct plants, pp. 1–6. Elsevier, B.V. (2007)Scheer, A.W.: Business Process Engineering - Reference Models for Industrial Enterprises. Springer (1994)Lin, J.T., Chen, T.L., Lin, Y.T.: Critical material planning for TFT-LCD production industry. International Journal of Production Economics 122, 639–655 (2009)Escudero, L.F.: CMIT, capacitated multi-level implosion tool. European Journal of Operational Research 76, 511–528 (1994)Maheut, J., Garcia-Sabater, J.P., Valero-Herrero, M.: MILP model for solving the supply chain operations scheduling problem with alternative operations considering delay penalization: a case study of a mass customization company. In: Proceedings of the 41st International Conference on Computers & Industrial Engineering, pp. 289–294 (2011

Applicability of ERP for Production Network Planning: A Case Study

Part 2: Knowledge Discovery and SharingInternational audienceA production network enables a company to develop capabilities to respond to diversity in national or regional demand, while at the same time integrate and coordinate their activities. Many companies have implemented enterprise resource planning (ERP) systems to overcome problems associated with coordination and planning in an organization, in recent years. In addition, advanced planning and scheduling (APS) systems have emerged to address the planning insufficiencies of ERP systems. However, due to complexity and competence dependency of these systems, advantages of them are overlooked by large organizations, and therefore ERP systems are still in use for planning. ERP systems are used in production networks for coordination of various plans and decisions across network partners. This paper aims to assess the fit and alignment between ERP functions and production network requirement for supporting production planning processes. Using a case study approach, the paper illustrates and discusses the applicability of ERP systems for planning in production networks. The case study showed that ERP systems have limited ability in coordinating order allocation in the network, detailed short term production planning and inter network distributions. It is concluded that use of ERP systems for network planning may in fact limit the network’s ability to reap the full benefits associated with planning across several facilities in a network