Predictive Three-Dimensional Printing of Spare Parts with Internet of Things

• 成果类型: Article
• 署名作者: Song, Jing-Sheng; Zhang, Yue
• 署名单位: Duke University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.00978
• 发表日期: 2025
• 关键词: internet of things (IoT); predictive analytics; 3D printing; condition-based maintenance (CBM); production-inventory system; spare parts inventory management

摘要

Industry 4.0 integrates digital and physical technologies to transform work management, where two core enablers are the internet of things (IoT) and threedimensional printing (3DP). IoT monitors complex systems in real time, whereas 3DP enables agile manufacturing that can respond to real-time information. However, the details of how these two can be integrated are not yet clear. To gain insights, we consider a scenario where a three-dimensional (3D) printer supplies a critical part to multiple machines that are embedded with sensors and connected through IoT. Although the public perception indicates that this integration would enable on -demand printing, our research suggests that this is not necessarily the case. Instead, the true benefit is the ability to print predictively. In particular, it is typically more effective for the 3D printer to predictively print to stock based on a threshold that depends on the system's status. We also identify a printing mode called predictive print on demand that allows for minimal inventory, and we find the speed of 3DP to be the primary factor that influences its optimality. Furthermore, we assess the value of IoT in cost reductions by separately analyzing the impact of advance information from embedded sensors and the real-time information fusion through IoT. We find that IoT provides significant value in general. However, the conventional wisdom that IoT's value scales up for larger systems is suitable only when the expansion is paired with appropriate 3DP capacity. Our framework can help inform investment decisions regarding IoT/embedded sensors and support the development of scheduling tools for predictive 3DP.