To remain healthy in today’s competitive environment, chemical companies must operate their supply chain (SC) efficiently by simultaneously optimizing multiple problems at different levels of operation, geographic locations, and SC functions (Figure 1). However, this is a challenging task because supply chains are highly dynamic and interconnected networks, often comprised of different decision makers (Figure 2). Thus, centralized approaches cannot be implemented in practice, while existing decentralized methods yield suboptimal solutions and are insufficient to meet industrial needs. Accordingly, the goal of our research is to develop a distributed cooperation-based framework for supply chain operation planning that considers local decision-making while at the same time accounts for the interactions between the nodes of the SC. In developing this framework, we address the following three challenges:
1. Formulate local (node) models as well as models for the entire supply chain that accurately describe the dynamics of and interconnections among different SC nodes.
2. Develop novel cooperation-based methods for improved decision-making in distributed dynamic supply chains.
3. Assess the proposed methods using real-world data and develop strategies for successful implementation of the proposed methods.
Our initial efforts focus on the industrial gas supply chain (to this end, we have established a collaboration with Praxair Inc). However, the proposed framework will enable us to effectively address operational planning problems in a wide range of manufacturing supply chains.