Data-driven decision models for Industry 4.0 supply chain management

工业 4.0 供应链管理的数据驱动决策模型

• 批准号: RGPIN-2022-04672
• 负责人: KazemiZanjani, Masoumeh
• 金额: $ 2.26万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760514
• 关键词: Data; driven; decision; models; Industry

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.

行业4.0（I4.0）围绕着生产量和定制非常灵活的工业生产。它依赖数字化，物联网（IoT）和人工智能的力量。面对I4.0的动态业务生态系统，除了最近大流行的前所未有的挑战之外，大多数制造业企业都致力于重新设计其供应链（SCS），以满足客户的需求，以应对弹性和响应能力。它们还旨在提高敏捷性和灵活的决策能力，以应对高度定制的制造时代的挑战。另一方面，高水平的自定义要求建立灵活且超连接的供应网络，该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集体目标。因此，由物联网技术启用的SC利益相关者之间的集成必须利用以制造运营来共享实时信息。此外，必须充分利用数据的可用性和可访问性以及机器学习方法（ML）方法的突破性进步，以促进现代SC中的实时决策。鉴于对定量I4.0 SC管理工具的基本研究很少，因此该研究计划的总体目标是为SMART SC中的战略，战术和运营计划提供数据驱动和强大的决策支持工具。特别关注云制造环境，可以将该目标层叠成以下目标：i）通过结合高度定制产品的制造中固有的不确定性来开发设计模型和解决方案算法，以设计弹性和可配置的SC。 ; ii）提出资源和成本共享机制，以促进I4.0 SC的水平合作； iii）提出一个强大的I4.0 SC战术计划框架，适应大规模定制的动态制造环境； iv）提出了一个强大的数据驱动组装线平衡模型，以促进在高度定制的制造业中频繁地重新配置制造线； v）为I4.0 SC中的智能和适应性操作级别的计划开发数据驱动的决策模型和解决方案算法。本提案中采用的方法是数据分析方法（例如ML）以及经典运营研究/管理科学方法的组合，例如随机和健壮的优化，分解算法，元启发式学和SC协作游戏。该提案依靠规范性分析和智能决策支持工具，将全面利用新的行业4.0数字转型范式，以解锁对加拿大制造业SC的新机会，而加拿大制造业SC长期以来一直遭受生产力低。