Data-driven Spare Parts Inventory Management

备件是用于维护活动的库存物品，以保持设备处于运行状态。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，目标是研究数据驱动的备件库存管理优化方法。在学术界，该计划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的备件库存管理相关的成本，例如，汽车等重要的是，该计划将强调供应链的可持续性和库存管理分析。 目前的文献大多集中在资本货物的备件管理。与资本货物不同，耐用消费品通常具有较短的生命周期和较大的消费群体。耐用消费品的备件库存通常由原始设备制造商或第三方服务供应商持有，以履行保修合同，而资本货物的备件库存则用于支持服务合同所规定的维修服务。这些差异表明，资本货物备件的最优库存政策不太可能是最优的耐用消费品。 随着人们对环境问题的日益关注，供应链可持续性的概念被广泛提倡。逆向物流（RL）是供应链可持续性研究中的热门话题之一。正向供应链和逆向供应链构成闭环供应链。在现有的文献中，还没有研究如何在闭环供应链中降低备件的生命周期成本。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，以获得管理方面的见解。文献中存在明显的差距，表明BDA很少用于备件库存管理。现有的备件库存管理的文献主要集中在管理最多数百个备件，而在实践中，成千上万的备件是常见的。 该计划将解决文献中的空白，同时确保在行业内的影响。此外，下一代学者和供应链管理人员的培训是一个关键的预期成果;参与该项目的HQP将获得研究技能和面临的主要利益相关者面临的挑战。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方面的差距。

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.