This study explores the integration of Artificial Intelligence (AI) into the Reverse Logistics Process (RLP) to enhance decision-making, operational efficiency, and resource recovery. Through a systematic literature review, the applications of AI across critical RLP stages (planning, execution and control) are identified. Key findings highlight the transformative role of AI in optimizing network design, improving product collection and inspection, and supporting decision-making for the disposition of recovered items. AI technologies and functionalities such as machine learning, predictive analytics, and decision-support systems demonstrate significant potential for automating complex processes, reducing operational costs, and improving logistical precision. However, barriers to AI adoption in the RLP include high implementation costs, organisational resistance, lack of specialist personnel, and limited technological infrastructure. The review also identifies enablers such as advancements in the Internet of Things, blockchain, and big data analytics, which facilitate AI adoption and integration in RLP. The study concludes that AI is essential for establishing resilient and adaptive RL systems and offers substantial opportunities to address uncertainty while improving efficiency and decision-making. Future research should focus on overcoming adoption barriers, advancing predictive models, and integrating AI with other Industry 4.0 technologies to enhance RLP outcomes. This review fills a significant gap in the literature by offering a structured analytical framework that categorises AI applications based on decision types and RLP stages, providing an integrated perspective not previously addressed in the field.

The semiconductor industry faces the dual challenge of risk assessment and process improvement. This paper introduces a framework that integrates Failure Mode and Effects Analysis (FMEA) and Plan-Do-Check-Act (PDCA) cycle to address these challenges effectively. FMEA serves as the initial step to identify potential risks within the system, followed by applying the PDCA cycle to systematically address and enhance the identified risks. The comparison between the initial Risk Priority Number (RPN) value, determined through FMEA, and the post-RPN in PDCA value gauges the success of the framework. Implementing the system in a semiconductor assembly line yielded a significant 51% improvement in RPN, with additional Lean tools incorporated into PDCA, such as SMART goals, 6M, and multi-voting. This integrated framework amplifies risk management, fosters continuous improvement, optimizes resource utilization, and empowers data-driven decisions, ultimately bolstering organizational growth. However, the study acknowledges limitations such as its single-case focus and potential RPN calculation subjectivity.

The paper discusses the concept of Kaizen, a Japanese philosophy of continuous improvement, and highlights its historical significance and widespread adoption in various industries worldwide. This paper is based on literature review of 98 research articles published related kaizen's evolution, industry application, and limitations during 2000-2022. The study employed a comprehensive search strategy across multiple databases like Scopus, Google Scholar, and others to gather relevant systematic reviews on kaizen. It references established guidelines like PRISMA and it mentions the planned thematic analysis using NVivo software, highlighting its applicability in identifying patterns and themes across diverse data sources. The review showed that Kaizen adoption is a useful tool for organizations looking to achieve continuous performance improvement and sustainability over time.

The operations of international trade drive global economies, with maritime transport serving as a key pillar in market connectivity and acceleration. The opportunities presented by Industry 4.0 to connect supply chain actors through integrated logistics pose a challenge that fuels the number of studies in this area, with a particular interest in the transportation stage due to its responsibilities for efficiency. This study conducts a systematic literature review by analyzing scientometric aspects and identifying research trends related to maritime transportation logistics. The PRISMA methodology, graph theory, and bibliometric indicators such as indegree, betweenness, and outdegree were employed to process the information related to maritime transportation logistics from the Scopus and Web of Science databases spanning from 2000 to 2022. The gathered information and results were processed and analyzed using R-studio and Bibliometrix tools. The documents were classified as original, structural, and research trends, with the latter focused on topics related to the environment, port competitiveness, logistics optimization, and economics. Finally, an agenda for future research in the field is presented.

Nowadays, the waste of resources has become one of the biggest problems for industries, due to the serious environmental, social and economic consequences it generates. Therefore, to ensure a production based on sustainable processes, it’s essential to have a responsible management of resources, being the first step one of the most important ones, the identification. Thus, the present research work aims to develop and implement a method based on the integration of Green and Lean methodologies to systematically identify ecological waste, taking as a case study a nectar factory in Lima - Peru. Through the implementation of tools such as Environmental Value Stream Mapping, Process Mapping or Failure Mode and Effects Analysis, it was found that the company generated a waste of 1584 litres of water and 38.5 kg of conditioned fruit every month. The identification of green waste is vital, as it is the first link in a long chain that contributes directly to improving the company's efficiency, profitability and reputation, as well as protecting the environment and promoting sustainable development.

<p class="TtuloAbstract">The aim of the article is to study the issues of analysis, modeling with the purpose of forecasting the payment of value added tax (VAT) on goods, works and services imported as imports into the customs territory of Ukraine. The reliability and validity of the planned VAT rate depend on the assessment of the status, forecast, seasonality and trends of economic and social development. The purpose of the work is to analyze and systematize the methodology for modeling VAT revenues from imports, justify the use of the econometric method and develop an adequate ARIMA model. It application is possible in the long term as well as smaller periods of time, which is relevant for monitoring and control of tax revenues. The study revealed the main factors influencing the application of the ARIMA model when modeling VAT revenues from imports. The resulting regression model in STATISTICA linked the variables with an accurate approximation.</p>

<p>Genetic Algorithms (GAs) are introduced to tackle the packing problem. The scheduling in Additive Manufacturing (AM) is also dealt with to set up a managed market, called “Lonja3D”. This will enable to determine an alternative tool through the combinatorial auctions, wherein the customers will be able to purchase the products at the best prices from the manufacturers. Moreover, the manufacturers will be able to optimize the production capacity and to decrease the operating costs in each case.</p>

<p>In assembly lines, rework stations are generally used for reprocessing defective items. On the other hand, using rework stations for this purpose only might cause inefficient usage of the resources in this station especially in an assembly line with a low defective rate. In this study, a mixed-integer programming model for cycle time minimization is proposed by considering the use of rework stations for performing parallel tasks. By linearizing the non-linear constraint about parallel tasks using a variate transformation, the model is transformed to a linear-mixed-integer form. In addition to different defective rates, different rework station positions are also considered using the proposed model. The performance of the model is analyzed on several test problems from the related literature<strong><em>.</em></strong></p>

<div><p class="TtuloAbstract">The model of inventory management on the basis of which the calculations of the efficiency of the use of inventories, minimizing the total costs associated with their delivery, storage and fines for lack of stocks in the presence of demand for them have been made and presented in the article. Using it in practice will allow managing the logistics and warehouse costs, the risk of not receiving or reducing the company's profits due to the excess costs and the efficiency of turnover of inventories. The proposed model takes into account the depending on the price of shipment from the supplier to the warehouse, the cost of holding stocks, the cost of holding the stocks, the amount of storage costs and penalties for non-shipped products. It is proved that rationally organized order in stock management allows companies to calculate the order quantity, its optimal size and time interval between orders taking into account the prospect of delay in supply, to plan the amount of profit, to organize the accounting of the profitable level of stocks, to determine the sizes of the highest and guarantee level of the stock. Approbation of the developed model was carried out on the basis of the data of the woodworking enterprise. We used elements of the Data Mining Results technology – the MARSPline module in the application package Statistica to achieve the goals. This made it possible to construct a regression model in multidimensional space and spline surfaces through the main functions.</p></div>

<p>This paper aims at analysing firms implementing new products. Based on a cluster analysis, three types of manufacturers have been identified representing different types of product innovators according to the competitiveness factors important for their business, environmentally sensitive new products, and a performance indicator, such as the share of turnover from new products.</p>