David Pérez-Perales1, Faustino Alarcón Valero1, Andrés Bozá García1  and Pedro Gómez-Gasquet1

1 Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, Camino de Vera, s/n, 46022, Valencia, Spain


Keywords: Collaborative planning; methodology; mathematical models, scenarios evaluation; ceramic sector

1   Introduction

Nowadays, supply chain (SC) decentralised decision-making where different decisional units have to be tactically and operationally coordinated to achieve a desired level of SC performance is the most common situation.

This SC collaborative planning (CP) process has led in the last two decades to the publication of many works addressing the relevance of CP modelling, such as Stadler (2009) [1], as well as the development of decision making analytical tools, which consider that the SC-CP process is made of a set of decisional units that interact in order to reach individual and SC goals. Among these tools, optimization ones are of special relevance, and among the latter, those based on mathematical programming. While is true that in the context of supply chains operations planning, mathematical programming models have been mainly used for centralised decision-making, last decade has issued an increasing research about decentralized ones interacting by means of coordination mechanisms. A complete and a very current state of the art may be found in Rius et al. [2].

Three shortcomings are derived from the previous review. First there is a lack of works that precisely link the conceptual model of the SC-CP process with its mathematical-based programming modelling. This fact make these mathematical models not fully capture the reality complexity. Secondly, but closely linked, there is a lack of works addressing temporal and spatial interactions. Finally, the way these mathematical models are constructed make difficult their transferability to other collaborative situations if some changes arise.

The above together with the reluctance of many companies to collaboratively planning due to the uncertain benefits such collaboration will bring them and how they will be shared justifies this paper, which proposes a methodology for the evaluation of supply chain collaborative planning scenarios through its mathematical modelling (based on MILP) and integrated resolution. It is important to remark that this methodology may guide companies to simulate different future CP scenarios so that the benefits or costs may be known “a priori”.

2. Methodology for the evaluation of supply chain collaborative planning scenarios. Application to a ceramic SC

The proposed methodology is based on two main “inputs” derived from previous works from this paper authors.

On the one hand a framework for the analytical modeling of the SC-CP-Process [3]. Its main objective is to help, facilitate and guide those responsible of the SC-CP process in the task of modeling for specific situations. It provides, in an organized manner, the pertinent concepts (conceptual and analytical) so that, in the modeling procedure, all important aspects that influence the planning process are taken into account. That framework integrates four different modeling views: physical, organisation, decision and information, and the relationships between them. This facilitates the development of integrated models of the SC CP process, leading to more realistic and versatile models that can be applied to any complex SC. It also addresses the definition of different temporal decision levels and decision centres (DC). At each level, the decision-making may be centralised (one DC) or distributed (several DC). These DC are subject to two interdependence relationship types, temporal (between DC belonging to different decision levels) and spatial (between DC belonging to the same decision level).

On the other hand a methodology for the (conceptual) modeling of the SC-CP-Process [4]. It aims to indicate all the steps to obtain an integrated model of the SC CP process, in which all the decisional activities, execution order an exchanged information due to their relationships are described.

Based on these inputs, this paper proposes a methodology for the evaluation of SC-CP planning scenarios through its mathematical modelling and integrated resolution.

Some of the premises of the proposed methodology are as follows:

  • The analytical models are deterministic and based on MILP.
  • It is considered an organizational context, that is, the collaborative decision process aims to reach a global common goal between the different decisional centres through coordination mechanisms. No oportunistic behaviour exists.
  • It is considered a hierarchical context, with just one cycle instruction-reaction. The hierarchical relationships will be either temporal or spatial.

Finally, the methodology is applied (integrated MILP modeling, resolution and performance evaluation) to a ceramic SC.


  1. Stadtler H. (2009). A Framework to Collaborative Planning and state-of-the-art. OR Spectrum, 31, 5-30.
  2. Rius G.; Maheut J.; Estellés-Miguel S.; García-Sabater J.P. (2020). Coordination mechanisms with mathematical programming models for decentralized decision-making: a literature revie. Cent Eur J Oper Res 28(15).
  3. Pérez D.; Lario F.C.; Alemany M.M.E.; Hernandez J. (2012). Framework for Modelling the Decision View of the SC-CP. Int J Decis Support Syst Technol 4(2), 59-77.
  4. Pérez D.; Alemany M.M.E.; Molasy M. (2016) The Methodology of Modeling the Decision-Making Process for Planning the Logistics Supply Chain. In Information Systems Architecture and Technology, 85-96. Springer.


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Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización Copyright © by (Eds.) José Manuel Galán; Silvia Díaz-de la Fuente; Carlos Alonso de Armiño Pérez; Roberto Alcalde Delgado; Juan José Lavios Villahoz; Álvaro Herrero Cosío; Miguel Ángel Manzanedo del Campo; and Ricardo del Olmo Martínez is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.

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