Optimal integration of third-parties in a coordinated supply chain management environment

Abstract

A generic tactical model is developed considering third party price policies for the optimization of coordinated and centralized multi-product Supply Chains (SCs). To allow a more realistic assessment of these policies in each marketing situation, different price approximation models to estimate these policies are proposed, which are based on the demand elasticity theory, and result in different model implementations (LP, NLP, and MINLP). The consequences of using the proposed models on the SCs coordination, regarding not only their practical impact on the tactical decisions, but also the additional mathematical difficulties to be solved, are verified through a case study in which the coordination of a production–distribution SC and its energy generation SC is analyzed. The results show how the selection of the price approximation model affects the tactical decisions. The average price approximation leads to the worst decisions with a significant difference in the real total cost in comparison with the best piecewise approximation.Peer ReviewedPostprint (author's final draft

Scenario-based dynamic negotiation for the coordination of multi-enterprise supply chains under uncertainty

“…(4) and (5) represent the minimum and maximum production and storage capacities, respectively. Similar constraints can be found in Laínez et al (2007,2009), and Hjaila et al (2015,2016 correspond to the retail price of the final product resource, the negotiation item r' transfer price, and the retail price of r' to the external clients, respectively. The negotiation item value…”

“…As aforementioned, the 3 rd parties participate in the decision-making by considering their price policies as variables in the model formulation following the piecewise pricing model proposed by Hjaila et al (2016). Here, we will explain the integration of the 3 rd party (external client xc) price policy as an example.…”

“…The developed MINLP models have been implemented and solved for a case study based on the centralized SC system proposed by Hjaila et al (2016). The centralized SC is decentralized by assuming a decentralized SC superstructure in order to illustrate the proposed approach.…”

“…The final products (A and B) are stored in 2 warehouses (w1 and w2) to be distributed later to three polystyrene markets (m1, m2, and m3) (Figure 8). Prices of both SCs external resources follow a piecewise pricing model, as in Hjaila et al (2016) with an elasticity's price of demand of (-20) for the raw materials of the polystyrene production-distribution SC, and (-25) for the raw materials of the energy generation SC. Table 1 and Table 2 list the distances that the polystyrene RMs travel through the leader SC, and the polystyrene production cost using the different RMs.…”

“…Zamarripa et al (2014) propose a LP tactical model for optimizing multi-site multi-product SC networks, coordinating the different steps through the insurance of the consistency of the supply/demand SC. This work has been expanded later by Hjaila et al (2016) by integrating third parties' financial perspectives as new elements to be considered in the decision making, thus resulting in different (LP, NLP, MINLP) tactical centralized models.…”