Counterfeiting is found today in many industries and in various forms with severe consequences for supply chain operations. Products counterfeiting can be detected in consumer goods such as clothing, food and beverages, accessories, pharmaceuticals, electronics, and luxury goods. The continuous violations in the supply chain have led to the need for mobilization of all involved stakeholders to overcome counterfeiting challenges. Effective traceability seems to be the only way to combat this phenomenon, ensuring safe and sustainable supply chain operations. This paper presents a structured literature review on traceability approaches for combatting the product supply chain counterfeiting phenomenon that led to forming a structured classification framework. The performed analysis aims to identify trends and good practices and can be used as a guideline for real-life projects against supply chain counterfeiting. The results show that traditional traceability methods are not effective as they can be easily falsified using today’s technological advancements. However, these same advancements also present valuable technologies such as blockchain and the internet of things to ensure safe and sustainable supply chain operations.

In today’s era, humanity has been overwhelmed by technological revolutions that have changed and will continue to change how business operations are performed, directly or indirectly. At the same time, the processes within the supply chain are quite complex, and as technology and processes evolve, they become more and more challenging. Traceability has become a critical issue in the food industry to ensure safety, quality, and compliance with regulations. The adoption of blockchain technology in the food supply chain has gained significant attention as a potential solution to improve traceability. This paper presents the development of a distributed application for table olives’ traceability on the Ethereum network. The paper also presents a methodological framework, which can help anyone aiming to implement an Ethereum decentralized application and demonstrates the practical use of the developed application by a Greek table olives producer. The application significantly improved the producer’s product traceability by providing a secure, transparent, and efficient solution for tracking and tracing the products in the supply chain. The app reduced the time, increased the accuracy and reliability of data, improved supply chain efficiency, and helped the producer comply with international regulations and standards.

As environmental regulations on sulphur emissions become more severe, the maritime sector is looking for alternative solutions. This study evaluates greenhouse gas (GHG) reduction alternatives and their combined ability to decarbonise international transport. Liquefied natural gas (LNG) is becoming widely used, reducing CO₂ emissions by 20–30 percent, while it has similar action in other emissions such as SO_X. Although costs are attractive, methane slip, which depends on the engine type, reduces GHG gains. Replacing conventional fuels such as heavy fuel oil and marine diesel oil with alternative ones is an effective method to decrease SO_x emissions. Liquefied natural gas is highly appreciated as an alternative fuel for maritime transportation. In this frame, the possibility of using alternative fuels, such as LNG, to reduce NO_x, CO₂ and SO_x emissions in Heraklion Port, including certain regionally defined waters, over the life of the vessel will also be explored. The study is conducted for ships calling at Heraklion Port and using alternative fuel such as LNG in different modes (cruising, hotelling, manoeuvring). A fuel-based emission reduction factor, rE_if, is defined in relation to the comparison of two different fuels: conventional (heavy oil, marine diesel) and alternative fuels (LNG). The bottom-up method is used for this data analysis. This study, by defining the reduction of several emissions with the use of LNG, indicates that it is actually an efficient transitional fuel to lead international transport to decarbonisation.

Background: Reverse supply chains of machinery and equipment face significant challenges, and overcoming them is critical for effective customer service and sustainable operation. Maintenance and repair services, strongly associated with the reverse movement of equipment, are among the most demanding reverse supply chain operations. Equipment is scattered in various locations, and multiple suppliers are involved in its maintenance, making it challenging to manage the related reverse supply chain operations. Effective maintenance is essential for businesses-owners of the equipment, as reducing costs while improving service quality helps them gain a competitive advantage. Methods: To enhance reverse supply chain operations related to equipment maintenance, this paper presents the operational framework, the methodological approach, and the architecture for developing a system that covers the needs for predictive maintenance in the service supply chain. It is based on Industry 4.0 technologies, such as the Internet of things, machine learning, and cloud computing. Results: As a result of the successful implementation of the system, effective equipment maintenance and service supply chain management is achieved supporting the reverse supply chain. Conclusions: This will eventually lead to fewer good-conditioned spare part replacements, just in time replacements, extended equipment life cycles, and fewer unnecessary disposals.

The Vehicle Routing Problem with Time Windows (VRPTW) is an NP-Hard optimization problem which has been intensively studied by researchers due to its applications in real-life cases in the distribution and logistics sector. In this problem, customers define a time slot, within which they must be served by vehicles of a standard capacity. The aim is to define cost-effective routes, minimizing both the number of vehicles and the total traveled distance. When we seek to minimize both attributes at the same time, the problem is considered as multiobjective. Although numerous exact, heuristic and metaheuristic algorithms have been developed to solve the various vehicle routing problems, including the VRPTW, only a few of them face these problems as multiobjective. In the present paper, a Multiobjective Large Neighborhood Search (MOLNS) algorithm is developed to solve the VRPTW. The algorithm is implemented using the Python programming language, and it is evaluated in Solomon’s 56 benchmark instances with 100 customers, as well as in Gehring and Homberger’s benchmark instances with 1000 customers. The results obtained from the algorithm are compared to the best-published, in order to validate the algorithm’s efficiency and performance. The algorithm is proven to be efficient both in the quality of results, as it offers three new optimal solutions in Solomon’s dataset and produces near optimal results in most instances, and in terms of computational time, as, even in cases with up to 1000 customers, good quality results are obtained in less than 15 min. Having the potential to effectively solve real life distribution problems, the present paper also discusses a practical real-life application of this algorithm.

This paper addresses the critical and urgent need to reduce food losses and waste (FLW) resulting from stringent marketing standards. It proposes a comprehensive and actionable framework grounded in the three pillars of sustainability—environmental, economic, and social—to effectively evaluate FLW across the entire food supply chain. The paper involves a thorough review of existing marketing standards, including research on FLW due to marketing standards, and proposes the implementation of targeted key actions within four key food sectors: fruits, vegetables, dairy, and cereals. The study provides a detailed analysis of the significant impact marketing standards have on FLW at various stages of the supply chain, including primary production, processing, retail, and consumption. By focusing on these critical points, the research underscores the necessity of addressing marketing standards to achieve meaningful reductions in FLW. The proposed framework aims to foster improved business practices and drive the development of innovative, sector-specific solutions that balance sustainability goals with economic viability. The holistic approach followed for this research lays the foundation for ensuring that the proposed framework is adaptable and practical, leading to measurable improvements in reducing FLW and promoting sustainability across the food industry.