Supply chains now cope with a lot of uncertainties, and their stakeholders are intensely interconnected, revealing new opportunities at a tremendous pace. In this context, companies must rethink their decision support systems to remain competitive. Particularly strategic supply chain capacity planning systems that should ensure resource availability. Unfortunately, existing systems do not satisfactorily consider this new deal. Therefore, this paper develops a conceptual framework providing guidelines for designing a decision support system for strategic supply chain capacity planning under uncertainty. To validate the conceptual framework, a decision support system has been designed accordingly, and two industrial experiments have been conducted.

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PurposeThe Covid-19 pandemic has created an environment of high uncertainty and caused major disruptions in supply chains. The new normal that has emerged during the pandemic is leading to a need to identify new solutions to improve supply chain crisis management in the future. Practitioners require adapted recommendations for solutions to implement. These recommendations are laid out in this paper.Design/methodology/approachA combination of a systematic literature review (SLR), qualitative semi-structured interviews and a questionnaire survey of supply chain practitioners is applied. The interviews provide insights into supply chain practitioners' views of their approaches and, together with the solutions proposed in the literature, provide future recommendations for action for supply chain managers.FindingsDuring the pandemic, companies experienced disruptions in supply, production and demand, as well as interruptions in transportation and distribution. The majority of the solutions proposed in the literature, coincide with the opinions of practitioners. These include collaborative risk management, real-time monitoring and information sharing, supply network management, scenario planning and “what-if” simulations.Research limitations/implicationsAlthough the number of interviews conducted and questionnaires completed is limited, they still serve to supplement the SLR with important practical insights and recommendations.Originality/valueThis paper presents a review of recent academic literature focusing on the impact of Covid-19 on supply chains and the existing solutions to mitigate that impact and manage future crises. It has been expanded to include industry perspectives and experiences. The findings of this study present recommended practices and strategies for better managing supply chains during a crisis.

Public and private organizations cope with a lot of uncertainties when planning the future of their supply chains. Additionally, the network of stakeholders is now intensely interconnected and dynamic, revealing new collaboration opportunities at a tremendous pace. In such a context, organizations must rethink most of their supply chain planning decision support systems. This is the case regarding strategic supply chain capacity planning systems that should ensure that supply chains will have enough resources to profitably produce and deliver products on time, whatever hazards and disruptions. Unfortunately, most of the existing systems are unable to consider satisfactorily this new deal. To solve this issue, this paper develops a decision support system designed for making strategic supply chain capacity planning more dynamic to cope with hyperconnected and uncertain environments. To validate this decision support system, two industrial experiments have been conducted with two European pharmaceuticals and cosmetics companies.

Strategic supply chain planning and supply chain risk management are two fields of supply chain management that are inseparable nowadays. The ability to consider risks is essential to maintain business performance. In addition, integrating the different business departments' visions in a common business vision is necessary to properly plan the future of a company. However, it is still a challenge for companies to design and maintain a decision-making process supporting strategic supply chain decisions that integrates risk management and unify business vision across departments. This paper relates an industrial experiment as an attempt to meet this challenge. This experiment was asked by a pharmaceutical company with the aim of supporting strategic decisions regarding its network of suppliers. It led to a decision-making process including the use of a computerized information system composed of a software for computations and a business intelligence software to easily make decisions. This process was put in practice on a pilot use case with two years old data. It resulted in the identification of several decisions that could have been made if the process was in operation two years ago, which is considered as a first validation of the approach. Finally, limitations have been identified regarding the data collection, opening avenues for future research on an innovative approach combining supply chain hyperconnectivity and event-driven principles.

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Supply Chain (SC) uncertainty perspectives must now be translated into practice. SC entities must accept crises and catastrophes as normal situations and increase significantly their culture of SC risk management. They should adapt their decision-support systems to be able considering disruptions as regular inputs, whether small, large or huge. Collaboration should not be limited to few entities of a SC, but to the whole SC. Concrete tools allowing entities to share vital information to give visibility, ensure synchronization of the material flows, align management of emergencies and use of critical resources must be developed and used. That is the purpose of this paper. Practically, a framework for SC risk and opportunity management and a Collaborative and Open Supply Chain Management Operating Services (COSMOS) platform are presented. An illustrative case is developed to highlight the potential benefits of the proposal on one service example.

The complexity of making supply chain planning decisions is growing along with the Volatility, Uncertainty, Complexity and Ambiguity of supply chain environments. As a consequence, the complexity of designing adequate decision support systems is also increasing. New approaches emerged for supporting decisions, and digital twins is one of those. Concurrently, the artificial intelligence field is growing, including approaches such as reinforcement learning. This paper explores the potential of creating digital twins with reinforcement learning capabilities. It first proposes a framework for unifying digital twins and reinforcement learning into a single approach. It then illustrates how this framework is put into practice for making supply and delivery decisions within a drug supply chain use case. Finally, the results of the experiment are compared with results given by traditional approaches, showing the applicability of the proposed framework.