SPS - Sustainable Production Systems Laboratory

About the lab

The Laboratory of Sustainable Production Systems (SPS-Lab) is dedicated to research and education activities aimed at the development and use of methods and tools for the design, analysis and management of products, processes and production systems.

Featured projects (16)

MAYA aims at developing simulation methodologies and multidisciplinary tools for the design, engineering and management of CPS-based (Cyber Physical Systems) Factories, in order to strategically support production-related activities during all the phases of the factory life-cycle, from the integrated design of the product - process - production system, through the optimization of the running factory, till the dismissal/reconfiguration phase. The concurrence and the cross-combination of the Cyber and the Physical dimensions with the Simulation domain is considered as cornerstone in MAYA innovations, to successfully address a new generation of smart factories for future industry responsiveness.
The S-MC-S project aims at supporting European manufacturing to adapt to global competitive pressures by developing methods and innovative enabling technologies towards a customer oriented and eco-efficient manufacturing. To this end, S-MC-S vision is to define and research a new production paradigm, Sustainable Mass-Customization, while also presenting Customization as one of the main driving forces behind the future success of Sustainability.
The project strives to develop, evaluate, and pilot a system innovation enhancing the ecological sustainability in the field of consumer electronics (CE), focusing on television sets (TVs). The project will provide guidelines and policy recommendations, tested in two industry pilots (demonstrators), to enhance the eco-sustainability of TVs by shifting its value chain from the current mass production of products with short technology cycles (“gadgetization”) towards a mass customization (MC) of TV sets meeting individual users’ demands. MC has been regarded by the European Commission as one of the main value drivers of a sustainable European economy. Still, in sharp contrast to other consumer industries, manufacturers of consumer electronics and TVs in particular have not yet followed this business paradigm.
The project concept is here summarized in 4 points: 1 - Development of a Reference Model for factory planning The current phase-based reference model of factory planning is analyzed. A new planning framework, allowing parallel planning phases is foreseen. It will provide a consistent data platform (“information market place”), intelligent project management support systems and the visualization of cross-links and interconnections between planning objects through a virtual prototype. Factory = Product allows the application of existing product lifecycle management tools and methods onto the factory. Its main activity is the development of a Factory Data Model for buildings, processes, resources and products. 2 - Development of the VF Manager core The VF Manager describes each element composing the manufacturing environment and their relations in order to perform a specific required activity. The VF manager guarantees data consistency and availability to any Functional Modules. The representation of this common space is based on the Factory Data Model. 3 - Development of the decoupled Functional Modules The Functional Modules implement various tools and services for factory design, reconfiguration, evaluation, management, any functional module respecting the interfaces defined by the VF manager and based on the Factory Data Model can be integrated. The VFF project develops some of the key components of this modular approach: expected results in VFF project are collaborative customer-driven VF tools (based on the new paradigm of seeing the factory as a product) for cost-effective and rapid creation, management and use of complex knowledge-based Factories. 4 - Development of the Knowledge repository and Good Practice VFF develops semantically enriched taxonomy rules in order to properly structure the knowledge recorded in all dimensions of the manufacturing enterprise. These rules will enable the systematic classification of all factory elements facilitating the modeling of complex systems and eventually enabling the examination of the factory as a product. The formalized knowledge will integrate strategies in order to adapt to fluctuating market demands, capacity planning methods, demand profiles and forecasts, modern business models and more. Towards this objective, this activity will introduce the capability to interrelate data from different sources through intelligent algorithms.

Featured research (17)

Circularity is clearly a competitive advantage and a market opportunity for European industries. From this perspective, while digitalization is largely recognized as an accelerator and an enabler of Circular Economy, the fact that European industry is strong but fragmented (highly specialized medium- and small-sized companies have different needs and different tools) naturally results in the proliferation of commercial platforms for digitalized manufacturing. If such fragmentation is not properly addressed, it will eventually become a threat to European competitiveness. Despite some examples, value networks still do not operate in a seamless, transparent, and effective way. This paper addresses the challenges and the resulting technical funding pillars for an IDS (International Data Space) manufacturing platform meant to empower a fully digital circular thread of products and services.
Today, there are many examples in the literature where digital copies of machines, devices, products or entire production systems are used to improve performance, make predictions and take decisions. However, humans have been so far excluded from these digital representations, even though their influence on process quality, performance and continuous improvement is significant. Typically, human factors are only considered in the context of job design and not in continuous decision-making and control. However, to create production systems that seamlessly complement human capabilities, the digital factory must include an accurate and realistic digital representation of workers: the Human Digital Twin. Human Digital Twins must incorporate data and behavioural models based on psychophysical status, skills, performance and personal needs, and communicate with decision-making and control systems. This research explores the concept of Human Digital Twin from a modelling perspective and proposes a meta-model that supports the definition of digital representation of workers in manufacturing. This meta-model is based on the analysis of the existing literature on humans modelling in industrial contexts and on the need for a flexible and modular representation of the worker in the digital factory. The proposed meta-model was instantiated and validated through an experiment in an injection moulding work cell to demonstrate its effectiveness and industrial relevance. The results showed that workers' well-being improved, while production and quality performance were optimised.
Multiple and diverse factory digital twins have been proposed in the literature. However, despite the recognized growing importance of workers in smart and autonomous industrial settings, such models still lack or oversimplify human representation. Human digital twins must include human monitoring and behavioural data and models based on psychophysical status, knowledge, skills, and personal needs to manage production systems that aim, at the same time, to achieve process performance and workers’ wellbeing. This paper proposes a metamodel based on data, events, and connectors that supports the modular composition of tailored human digital twins. This work also addresses an industrial application of the metamodel for preliminary validation.

Lab head

Paolo Pedrazzoli
  • Department of Innovative Technologies

Members (32)

Luca Canetta
  • University of Applied Sciences and Arts of Southern Switzerland
Donatella Corti
  • University of Applied Sciences and Arts of Southern Switzerland
Marco Silvestri
  • SUPSI and University of Parma
Marzio Sorlini
  • University of Applied Sciences and Arts of Southern Switzerland
Andrea Bettoni
  • University of Applied Sciences and Arts of Southern Switzerland
Andrea francesco Barni
  • University of Applied Sciences and Arts of Southern Switzerland
Alessandro Fontana
  • University of Applied Sciences and Arts of Southern Switzerland
Diego Rovere
  • University of Applied Sciences and Arts of Southern Switzerland