Conclusions and Perspectives
Supply Chain optimization presents multiple opportunities for implementation in the process industry and has been the focus of increasing research by the PSE community. However, there are many challenges that still prevent the successful implementation of SC solutions. In this paper, we describe a real-world, process supply chain through an industrial gas SC whose goal is to illustrate the complexity of the decisions and information involved. Several challenges are discussed that relate to the modeling of entities (energy, production, distribution), the different approaches that result from integrated decision support systems, such as multiscale and mainly uncertainty and its treatment. Finally, we also discuss implementation challenges that result from large scale models that require custom solution methods, as well as the impact of decision support tools that requires the need for the management of change. The supply chain work available in the literature indicates the need of comprehensive decision support tools required to coordinate cross-functional models. Such tools should allow the treatment of real supply chain characteristics in which different aspects must be considered: the presence of uncertainty - modeling resilience and risk; sustainability goals - accounting simultaneously for economic; environmental and social concerns; international taxes; transfer prices, duties as well as multi-modes and outsourcing options, amongst others. Such challenges lead to further complex, often multiscale models that demand the investment in efficient solution methods. To conclude we want to raise the need to combine the traditionally PSE models and methods with big data analytics, machine learning, and advanced statistical methods, amongst others, so as to be able to inform the supply chain decision process supporting better decisions. This calls for a close integration between academia and industry aiming to reduce the gap between research and implementation.
