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.

工业4.0（I4.0）围绕着在生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（IoT）和人工智能的力量。面对I4. 0充满活力的商业生态系统，加上近期疫情带来的前所未有的挑战，大多数制造企业努力重新设计其供应链，以满足客户的需求。他们还致力于提高敏捷性和灵活决策能力，以应对高度定制化制造时代的挑战。另一方面，高水平的定制要求一个灵活和高度连接的供应网络，其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集体目标。因此，通过物联网技术实现的供应链利益相关者之间的集成必须用于共享制造运营方面的实时信息。此外，随着机器学习（ML）方法的突破性进展，数据可用性和可访问性的爆炸性增长沿着，必须充分利用，以促进现代SC的实时决策。鉴于缺乏定量I4.0供应链管理工具的基础研究，本研究计划的总体目标是提出数据驱动和强大的决策支持工具，用于智能供应链的战略，战术和运营规划。特别关注云制造环境，这一目标可以级联为以下目标：i）通过结合高度定制产品制造中固有的不确定性，为设计弹性和可配置的供应链开发决策模型和解决方案算法; ii）提出资源和成本共享机制，以促进I4.0供应链中的横向协作; iii）提出一个健壮的I4.0供应链战术规划框架，适应大规模定制的动态制造环境; iv）提出一个健壮的、数据驱动的装配线平衡模型，便于在高度定制的制造环境中频繁地重新配置生产线;以及v）为I4.0供应链中的智能和自适应运营级规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（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、元分析和SC协作游戏）的组合。依靠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，为长期以来生产力低下的加拿大制造业SC提供新的机会。