Integration of sale and leaseback in the optimal design of supply chain networks

• We develop a mixed integer linear programming (MILP) model for the optimal design of supply chain networks.
• We incorporate the sale and leaseback method within the model.
• We incorporate demand and real estate market uncertainty within the model.
• We evaluate the model with a real case study and we communicate the results.
• We provide managerial implications and future research directions.

Supply chain network (SCN) modeling has gain a great research interest as companies realized that scientific approaches in managing their SCN's, rather than “common-sense” heuristics, are the roads to achieve sustainability, profitability, growth, and competiveness. Ever since, the relevant literature is supplied with models that aim to optimize SCN's design and/or operation. The former is a strategic process that designs network's infrastructure and concerns long term investments which undertake huge amounts of capitals. SCN managers should be able to evaluate how these decisions contribute to the overall performance of the company and not assess them with only cost oriented indicators. By employing advance financial management methods, such as sale and leaseback (SLB), fixed assets could be the medium to improve liquidity and strengthen credit solvency. This paper aims to enrich the SCN design literature by introducing a Mixed-Integer Non Linear Programming (MINLP) model that integrates SLB technique with SCN design decisions. By exploiting the properties of the MINLP model it is reformulated into an exact Mixed-Integer Linear Programming (MILP) model that is solved to global optimality. A real case study from a consumer goods company is utilized in order to show model's functionality and to evaluate its adaptability, robustness, and benefit. The model could assist and support SCN managers in effective decision making in the strategic level.