Growing complexity and uncertainty are some of the most significant characteristics of today’s manufacturing, primarily attributable to the growing diversity of products, market volatility, and the extensive expansion of global manufacturing networks. Recent advancements in Industry 4.0 (e.g., IoT and sensing technologies) have enabled the capture of extensive manufacturing data, including information on order progress, material usage, workforce conditions, vehicle locations, and machine statuses. This has made data-driven approaches increasingly appealing for manufacturing optimization. However, relying solely on data-driven methods may overlook crucial qualitative and contextual information essential for comprehending intricate manufacturing systems, thus presenting challenges for their practical implementation in real-world manufacturing settings. Within the manufacturing domain, a wealth of invaluable human expertise exists beyond the confines of data repositories. We are interested in maximizing the benefits of both human expertise and data in manufacturing. Specifically, we aim to explore the following aspects:
- How does integrating domain knowledge and real-time data reduce complexity and uncertainty in manufacturing systems?
- How can domain knowledge be leveraged alongside real-time data analytics to improve decision-making in manufacturing operations management?
- What are effective strategies for integrating domain knowledge and data to enhance the overall performance of manufacturing processes or systems?