Data-driven decision models for Industry 4.0 supply chain management

工业4.0 （I4.0）围绕着在产量和定制方面高度灵活的工业生产。它依赖于数字化、物联网和人工智能的力量。面对工业4.0的动态商业生态系统，以及最近的大流行带来的前所未有的挑战，大多数制造企业都在努力重新设计其供应链，以提高其弹性和响应能力，以满足客户的需求。他们还致力于提高敏捷性和灵活的决策能力，以应对高度定制制造时代的挑战。另一方面，高水平的定制需要一个灵活和超连接的供应网络，在这个网络中，实体同意共享技术和制造资源，以实现提高敏捷性和降低成本的共同目标。因此，必须利用物联网技术实现供应链利益相关者之间的集成，以共享制造运营方面的实时信息。此外，必须充分利用数据可用性和可访问性的爆炸式增长以及机器学习（ML）方法的突破性进展，以促进现代sc的实时决策。鉴于对定量工业4.0供应链管理工具的基础研究的缺乏，本研究计划的总体目标是为智能供应链的战略、战术和运营规划提出数据驱动和强大的决策支持工具。特别关注云制造环境，该目标可以级联为以下目标：i)通过纳入高度定制产品制造中固有的不确定性，为设计具有弹性和可配置的SC开发决策模型和解决方案算法；ii)提出资源和成本分担机制，以促进工业4.0中国家的横向合作；iii)提出一个健壮的I4.0 SC战术规划框架，以适应大规模定制的动态制造环境；Iv)提出了一个健壮的数据驱动的装配线平衡模型，该模型促进了高度定制制造背景下生产线的频繁重新配置；v)开发数据驱动的决策模型和解决方案算法，用于I4.0 sc中的智能和自适应运营级规划。本提案中采用的方法是数据分析方法（如ML）和经典运筹学/管理科学方法（如随机和鲁棒优化、分解算法、元启发式和SC协作游戏）的结合。依托规范性分析和智能决策支持工具，该提案将全面利用新的工业4.0数字化转型范式，为长期遭受低生产率困扰的加拿大制造业SC创造新的机会。

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.