Cyber-physical systems to monitor the efficiency and sustainability of human-centric manufacturing systems

The manufacturing domain has been experiencing several revolutions over the years that have been shaping not only the design and management of processes but also their core drivers and value propositions. Industry 4.0 unleashes many enabling technologies such as the Internet of Things sensors and machine learning algorithms to boost industries’ productivity through data-driven process monitoring, rather than relying on operation manager experience. However, this fourth revolution does not set as strategic goals sustainability drivers (e.g., social and environmental) triggered by external forces that undermine modern societies. European policymakers address this structural limitation by defining the Industry 5.0 paradigm focused on human-centric and sustainable value creations. In this fast-paced landscape, this doctoral thesis targets the limitations of Industry 4.0 related contributions and defines three research questions to demonstrate the competitive advantages in designing cyber-physical systems to monitor the efficiency and sustainability of human-centric manufacturing environments. The human-centricity is an important feature of this work because, despite the rise of automation, workers represent a strategic and fragile resource in industrial plants. Therefore, Internet of Things technologies are leveraged to achieve a digital representation of workers. The acquired measurements are fed into computational algorithms to appreciate data-driven managerial insights based on the returned Key Performance and Risk indicators. The contributions of this thesis can be conceptually divided into two separate streams. The first demonstrates the relevance of enhancing the operational visibility of in-plant operations by exploiting Real Time Locating Systems acquisition layers. Although this technology indoor locates whichever (manufacturing) entity and asset in a defined coverage area, the returned workers’ positions fail to evaluate systems’ performances and sustainability. For this purpose, density-based machine learning algorithms and neural networks are introduced and validated to embed operational metrics into Decision Support Systems. Multidimensional managerial insights prove the consistency of this methodology in three different manufactur- ing environments. Considering production settings, managers appreciate the uptimes of workers and resource utilizations while evaluating the layout configurations and the related efficiency in manual material handling activities. This twofold level of analysis enables to eventually increase in-plant productivity while optimizing workers’ efforts in replenishment routes. The logistic investigation offers similar takes by monitoring the Overall Equipment Effectivness of manual forklifts and the distribution of picking/depositing activities in storage areas. Potential inefficiencies provide valid input to optimize the performances while reducing the energy consumption of logistics vehicles. The second stream focuses on workers’ physical resilience during task executions. To achieve this purpose, ergonomic indices are largely adopted to mitigate work- related musculoskeletal disorders in the workforce. The European Assembly Worksheet screening tool is the most complete one focusing on several parameters ranging from working postures to exterted forces. The developed cyber-physical system mirrors in digital spaces workers’ operations through a multi-device acquisition layer. While a four-channel surface ElectroMyoGraphy and a network of markerless cameras acquire muscular contractions in upper limbs and body joints, a radio-frequency-based smart glove detects process interactions such as tool usages and component pickings and thus segments production activities. These digital measurements are fed into computational algorithms to automate the mentioned ergonomic assessment. The experimental campaign validates the proposed cyber-physical systems and draws several managerial insights. For instance, strong bending postures may highlight a poor workplace design suggesting the need of self-adjustable workstations to accommodate a diverse workforce. At the same time, worrisome exerted forces could require line rebalancing to fairly redistribute muscular activity rates among operators. In summary, this thesis represents a significant advancement in digital manufacturing, offering ready-to-deploy systems while outlining future research opportunities and applications.