Data-driven decision models for Industry 4.0 supply chain management

点击翻译按钮获取中文摘要

Industry 4.0 (I4.0) revolves around an industrial production that is highly flexible in production volume and customization. It relies on the power of digitalization, internet of things (IoT) and artificial intelligence. Confronted with the dynamic business ecosystem of I4.0 in addition to the unprecedent challenges of recent pandemic, the majority of manufacturing enterprises strive to redesign their supply chains (SCs) towards resilience and responsiveness in meeting the needs of their clients. They also aim for improving the agility and flexible decision capabilities to meet the challenges of a highly-customized manufacturing era. A high level of customization, on the other hand, calls for a flexible and hyper-connected supply network where the entities agree on sharing technological and manufacturing resources to achieve the collective goal of improving agility and reducing costs. Consequently, the integration among SC stakeholders, enabled by IoT technologies, must be leveraged towards sharing real-time information in terms of manufacturing operations. In addition, the explosion in the availability and accessibility of data along with breakthrough advances in machine learning (ML) approaches must be adequately exploited to promote real-time decision-making in modern SCs. Given the paucity of fundamental research on quantitative I4.0 SC management tools, the overall goal of this research program is to propose data-driven and robust decision support tools for strategic, tactical and operational planning in smart SCs. With a particular focus on a cloud manufacturing environment, this goal can be cascaded into the following objectives: i) to develop decision models and solution algorithms for the design of a resilient and configurable SC by incorporating the uncertainty inherent in the manufacturing of highly customized products; ii) to propose resource and cost sharing mechanisms to promote horizontal collaboration in I4.0 SCs; iii) to propose a robust I4.0 SC tactical planning framework, adaptive to the dynamic manufacturing context of mass customization; iv) to propose a robust and data-driven assembly line balancing model that facilitates the frequent reconfiguration of the manufacturing line in the context of highly-customized manufacturing; and v) to develop data-driven decision models and solution algorithms for intelligent and adaptive operational-level planning in I4.0 SCs. The methodology adopted in this proposal is a combination of data analytics methods, such as ML, and classical Operations Research/Management Science approaches, such as stochastic and robust optimization, decomposition algorithms, metaheuristics, and SC collaborative games. Relying on prescriptive analytics and smart decision support tools, this proposal will harness comprehensively the new Industry 4.0 digital transformation paradigm to unlock new opportunities for renovating Canadian manufacturing SC that has been long suffering from low productivity.