Data-driven Spare Parts Inventory Management

数据驱动的备件库存管理

• 批准号: RGPIN-2021-03478
• 负责人: Huang, Kai
• 金额: $ 3.13万
• 依托单位: McMaster 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=746442
• 关键词: Data; driven; Spare; Parts; Inventory

Spare parts are stock items used in maintenance activities required to keep equipment in operating condition. The spare parts inventory management system is critical because the cost of spare parts accounts for a significant share of the equipment's lifecycle cost. The long-term goal is to develop new mathematical models and solution methodologies for data-driven optimization approaches in inventory management. In the short term, the aim is to investigate data-driven optimization approaches for spare parts inventory management. In academia, this program will provide new data-driven models for spare parts inventory management, and develop solution methodologies for these models. In practice, the resulting methodologies will save costs related to Canada's spare parts inventory management across a broad range of industries, e.g., automobiles. Importantly, this program will emphasize supply chain sustainability and analytics for inventory management. Most of current literature focus on managing spare parts of capital goods. Unlike capital goods, consumer durable goods usually have a shorter lifecycle and a larger consumer base. The spare parts inventory of consumer durable goods is normally held by an Original Equipment Manufacturer (OEM) or third-party service provider to fulfill the warranty contracts while that of capital goods is held to support maintenance services regulated by service contracts. These differences indicate that the optimal inventory policies for spare parts of capital goods are unlikely to be optimal for those of consumer durable goods. As environmental concerns have been increasingly growing, the concept of supply chain sustainability is widely advocated. Reverse logistics (RL) is one of the popular topics in the literature on supply chain sustainability. The forward supply chain and the reverse supply chain constitute the closed-loop supply chain. In the existing literature, there is no study that investigates reducing the life cycle cost of spare parts in a closed-loop supply chain. Big data analytics (BDA) utilizes evidence-based data, statistical and operations analysis, predictive modeling, forecasting, and optimization techniques to evaluate operations and strategies to obtain insights in management. There is a clear gap in the literature indicating that BDA is rarely used in spare parts inventory management. Existing literature in spare parts inventory management mainly focus on managing at most hundreds of spare parts while it is common to see thousands of spare parts in practice. This program will address gaps in the literature while also ensuring impact within industry. Moreover, training of the next generation of scholars and supply chain executives is a key anticipated outcome; the HQP involved in this project will gain research skills and exposure challenges facing key stakeholders. This will ensure success in their future career choice and will assist in filling the skills and personnel gap.

备品备件是维护活动中使用的库存物品，用于保持设备的运行状态。备件库存管理系统至关重要，因为备件成本占设备生命周期成本的很大一部分。长期目标是为库存管理中的数据驱动优化方法开发新的数学模型和解决方法。在短期内，目标是研究备件库存管理的数据驱动优化方法。在学术界，该项目将为备件库存管理提供新的数据驱动模型，并为这些模型开发解决方案。在实践中，由此产生的方法将节省与加拿大广泛行业(如汽车)的备件库存管理相关的成本。重要的是，该计划将强调供应链的可持续性和库存管理的分析。目前的文献大多集中在资本品备件的管理上。与资本品不同，耐用消费品通常具有较短的生命周期和较大的消费基础。耐用消费品的备件库存通常由原始设备制造商(OEM)或第三方服务提供商持有，以履行保修合同，而资本品的备件库存则用于支持服务合同规定的维护服务。这些差异表明，资本货物备件的最佳库存政策不太可能对耐用消费品的库存政策是最佳的。随着人们对环境问题的日益关注，供应链可持续发展的概念得到了广泛的倡导。逆向物流(RL)是供应链可持续性文献中的热门话题之一。正向供应链和逆向供应链构成了闭环供应链。在现有文献中，没有关于降低闭环供应链中备件生命周期成本的研究。大数据分析(BDA)利用基于证据的数据、统计和运营分析、预测建模、预测和优化技术来评估运营和战略，以获得对管理的洞察。文献中有一个明显的空白，表明BDA很少用于备件库存管理。现有的备件库存管理文献主要集中在对最多数百个备件的管理，而实际中经常会出现数千个备件的管理问题。该计划将解决文献中的空白，同时也确保在行业内产生影响。此外，对下一代学者和供应链管理人员的培训是一个关键的预期结果；参与该项目的HQP将获得研究技能和关键利益相关者面临的暴露挑战。这将确保他们在未来的职业选择中取得成功，并将有助于填补技能和人员缺口。