The process of adopting innovation, especially with regard to precision farming (PF), is inherently complex and social, and influenced by producers, change agents, social norms and organizational pressure. An empirical analysis was conducted among Italian farmers to measure the drivers and clarify “bottlenecks” in the adoption of agricultural innovation. The purpose of this study was to analyze the socio-structural and complexity factors that affect the probability to adopt innovations and the determinants that drive an individual’s decisions. Preliminary results found high levels of adoption among younger farmers, those that had a high level of education, those with high intensity of information, with large farm sizes, and high labor intensity. A logit model was used to understand the role played by labor intensity and perceived in the adoption process. In light of the Common Agricultural Policy Reform post 2020, the findings suggest relevant policy implications, such as the need to increase awareness of PF tools and foster dissemination of information aimed at reducing the degree of perceived complexity.

The logistics sector plays one of the most important roles in the supply chain with the aim of providing a fast, flexible, safe, economical, efficient, and environmentally acceptable performance of freight transport flows. In addition, the popularization of the concept of a circular economy (CE) used to retain goods, components, and materials at their highest usability and value at all times, illustrates the importance of the adequate performance of reverse logistics (RL) processes. However, traditional RL is unable to cope with the requirements of modern supply chains and requires the application of Industry 4.0 technologies, which would make it more efficient. The main aim of this study was to evaluate the applicability of various Industry 4.0 technologies in the RL sector in order to point out the most applicable ones. To solve the defined problem, a novel multi-criteria decision making (MCDM) model was defined by combining the best—worst method (BWM) to obtain the criteria weights, and the newly developed comprehensive distance-based ranking (COBRA) method to rank the technologies. Another aim of the study was to validate the newly established method. The results indicated that the most applicable technologies were the Internet of Things, cloud computing, and electronic—mobile marketplaces. These technologies will have a significant impact on the development of RL and the establishment of CE systems, thus bringing about all the related positive effects.

In the Industry 4.0 era, digital tools applied to production and manufacturing activities represent a challenge for companies. Digital Twin (DT) technology is based on the integration of different “traditional” tools, such as simulation modeling and sensors, and is aimed at increasing process performance. In DTs, simulation modeling allows for the building of a digital copy of real processes, which is dynamically updated through data derived from smart objects based on sensor technologies. The use of DT within manufacturing activities is constantly increasing, as DTs are being applied in different areas, from the design phase to the operational ones. This study aims to analyze existing fields of applications of DTs for supporting safety management processes in order to evaluate the current state of the art. A bibliometric review was carried out through VOSviewer to evaluate studies and applications of DTs in the engineering and computer science areas and to identify research clusters and future trends. Next, a bibliometric and systematic review was carried out to deepen the relation between the DT approach and safety issues. The findings highlight that in recent years, DT applications have been tested and developed to support operators during normal and emergency conditions and to enhance their abilities to control safety levels.

Electronic traceability (e-traceability) is a growing trend in the agri-food industry, offering improved transparency, accountability, and reduced risk of foodborne illnesses through the use of electronic systems to trace products throughout the entire supply chain. E-traceability drivers in the agri-food supply chain encompass diverse factors motivating companies to adopt electronic systems for product tracking, aiming to enhance visibility, minimize risk, ensure compliance, and promote safety, sustainability, and efficiency through clear and verifiable records of product origins, quality, and sustainability, building consumer trust and loyalty. By identifying the main drivers of e-traceability, this research aims to shed light on the factors that motivate companies to implement electronic systems for tracking and monitoring products. For solving this problem of multi-criteria decision-making (MCDM), this study proposes a hybrid MCDM model. The model combines “Factor Relationship” (FARE) and “Axial Distance-based Aggregated Measurement” (ADAM) methods in the fuzzy environment. The results indicate that the most important drivers are supply chain efficiency, technology development and sustainability. These drivers are critical and they significantly impact the successful implementation and adoption of e-traceability strategies in the agri-food sector.

Purpose: Circular supply chains (CSC) are particularly important for the agri-food sector, which faces strict requirements generated by increased food consumption as a consequence of world population growth, changes in lifestyle, development of consumer society and increasing health awareness. Recent disruptive factors have placed the vulnerability of agri-food supply chains in the spotlight. Therefore, the purpose of this paper was to identify the most manageable groups of risks in order to ensure the smooth operation of agri-food circular supply chains.Design/methodology/approach: Seven main risk groups were evaluated in relation to nine criteria. To solve this multi-criteria decision making (MCDM) problem, a novel MCDM model, which integrates the best-worst method (BWM) and the COmprehensive distance-Based RAnking (COBRA) method in a grey environment, was developed.Findings: Three risks were singled out, namely, product features risks, logistics risks and managerial risks. The obtained risks are those whose management would create the most positive effects for the stakeholders and help them achieve their primary goals regarding the circularity of agri-food supply chains.Originality/value: This study investigates the main characteristics of the CSC in the agri-food sector, identifies, simultaneously explores and ranks all main risk groups associated with them and expands the possibilities for solving these kinds of problems by developing a novel MCDM model. It also identifies the most significant risks, both for individual stakeholders and for all stakeholder groups together.

Multicriteria decision making (MCDM) is a field that helps decision makers evaluate alternatives based on multiple criteria and encompasses scoring, distance-based, pairwise comparison, and outranking methods. Recent developments have aimed to solve specific problems and overcoming the limitations of previous methods. This paper proposes a new axial-distance-based aggregated measurement (ADAM) method, which is used in combination with the best-worst method (BWM) to evaluate agri-food circular economy (CE)-based business models (BMs) to create a more sustainable and efficient system for producing and consuming food. This paper proposes nine BMs, which were evaluated against eight criteria. The BWM method was used to obtain the criteria weights, while the ADAM method was used to obtain a final ranking of the BMs. The results indicate that a sustainable circular agri-food supply chain is a BM that can bring companies the most significant progress in business and strengthen their position in the market. We concluded that the ADAM method is effective for solving MCDM problems and that, overall, the model is an effective tool for solving the problem defined in this study. The main contributions are the development of a new MCDM method and a hybrid model, the establishment of the framework for evaluation and selection of CE-based BMs, and the identification of the most important ones.