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1. Introduction
The genesis of lean manufacturing management, which aspires to enhance value creation by reducing waste and thereby elevating productivity (Koskela et al., 2002), can be traced back to Toyota in the 1950s, where it became known as the Toyota Production System (Ballard and Howell, 1998). This strategy emphasizes continuous process improvement, concentrating on the elimination of wasteful practices such as overproduction, waiting time, unnecessary movement, defects, excess inventory, unused talent and over-processing (Ohno, 1988). By adhering to this approach, organizations aim to refine their operational efficiency, curtail costs and escalate customer satisfaction (Sundar et al., 2014).
Since 2010, the rapid evolution of technology, characterized by the rise of artificial intelligence, big data and the Internet of Things (IoT) has provided unprecedented opportunities for manufacturing industries to amplify their quality and efficiency (Kusiak, 2023). The advances in information and communication technology have been a pivotal force in this transformation, catalyzing the shift from traditional computer-aided manufacturing to a more data-centric, intelligent approach. With IoT infrastructure and simulation, a manufacturing system that is able to automatically analyze the data to aid decision-making in lean manufacturing can obtain the operation efficiency (Wang, 2020). The implementation of machine learning algorithms with various computing models also enhance the incorporation of advanced sensors in the context of Industry 4.0 (Md et al., 2022).
However, the introduction of these disruptive technologies is not without its challenges, particularly from a data management perspective in a lean manufacturing process. These include handling the heterogeneous nature of the data, managing its enormous volume and addressing the need for real-time processing (Dai et al., 2020). Furthermore, dealing with the vast quantities of data being generated daily, and the corresponding demand for efficient data storage, access and computation resources, has proven to be a critical management issue. Consider, for example, the data value (DV) chain as shown in Figure 1 (Jarr, 2012). Within this chain, individual DVs are derived from interactive real-time manufacturing data, such as digitalized manufacturing machine analog signals, measurements, settings and collected images. In contrast, aggregated time series manufacturing data, subjected to exploratory data analytics based on the accumulated data, can yield significant business value. Analyzing individual data can enable real-time interventions to prevent defects, while studying aggregated...