To ensure food security, agricultural production systems should innovate in the direction of increasing production while reducing utilized resources. Due to the higher level of automation with respect to traditional agricultural systems, Controlled Environment Agriculture (CEA) applications generally achieve better yields and quality crops at the expenses of higher energy consumption. In this context, Digital Twin (DT) may constitute a fundamental tool to reach the optimization of the productivity, intended as the ratio between production and resource consumption. For this reason, a DT Architecture for CEA systems is introduced within this work and applied to a case study for its validation. The proposed architecture is potentially able to optimize productivity since it utilizes simulation software that enables the optimization of: (i) Climate control strategies related to the control of the crop microclimate; (ii) treatments related to crop management. Due to the importance of food security in the worldwide landscape, the authors hope that this work may impulse the investigation of strategies for improving the productivity of CEA systems.

Digital Twin (DT) is considered a key approach to enhance the system reactivity to uncertain events due to its ability to getting data from the field and triggering actions on the physical asset. Given the modern technological and rapidly changing work environment, it is likely that in the next years companies will need to retrofit their manufacturing systems by integrating DTs. In this context, it is fundamental to define the necessary steps for the development of DTs and for their integration into manufacturing systems through a DT architecture. In response to this issue, a methodology based on Virtual Commissioning is proposed. A stepwise approach is illustrated in which the DT is designed, integrated and verified using a virtual environment. The methodology is validated through the integration of a DT into a flow shop for the implementation of a scheduling reactive to machine breakdown. By following the steps of the proposed methodology, a DT architecture able to improve the makespan of the studied flow shop is developed, suggesting the potential applicability of the approach to industrial manufacturing systems.

The last decade saw the rise of digitalization and data-supported decision making in the manufacturing industry: the Fourth Industrial Revolution. This trend, also known as Industry 4.0, allows manufacturing enterprises to discover manufacturing uncertainties and measure their real manufacturing capability. One of the ways in which Industry 4.0 trends have been exploited is in the improvement of maintenance, which went from following planning-focused paradigms to more proactive-focused stances. Enabling the Industry 4.0 vision for maintenance purposes has historically required companies to either replace or upgrade their existing legacy devices. It is through the latter course of action that Smart retrofitting in maintenance (SRM) intends to bring value to enterprises. This work aims to present a systematic literature review on SRM, utilizing the oft-cited PRISMA methodology. Through this analysis, a definition of SRM that reflects the current state of the art is proposed. Furthermore, the research in SRM applied in the context of different maintenance strategies is assessed (i.e. reactive, planned, proactive and strategic maintenance), and the most common drivers and challenges in SRM are presented. Finally, a roadmap for the implementation of SRM is proposed. The analysis of the SRM literature reveals that there are important research opportunities in the exploitation of SRM for strategic maintenance and asset management. The authors hope that this document leads to the consolidation of a new research area that aims to add value to maintenance in enterprises through the application of smart retrofitting in preexisting legacy devices.

Manufacturing companies are facing different challenges. On the one hand, production is moving from mass-production to mass-customization and personalization. Reconfigurable-, adaptive- and evolving-factories are necessary to achieve the required flexibility. On the other hand, technology must be integrated with human skills to assist the operators in supervising and maintaining manufacturing plants with growing complexity. In line with the Industry 4.0 paradigm, a Digital Twin Demonstrator is proposed to support the supervision activity of the operator in the context of flexible manufacturing with robotics. The supervision is achieved through a Human-Computer-Machine Interaction (HCMI) enabled with the Digital Twin technology. The suggested demonstrator is implemented and validated using a lab case study where it is demonstrated how the proposed HCMI interaction enables ‘close-to-real-time’ supervision of the manufacturing system in its self-adaptation to production and environmental changes.