Approaches to digitalization and optimization of logistics

Logistic optimization is a strategic element in many industrial processes, given that an optimized logistics makes the processes more efficient. A relevant case, in which the optimization of logistics can be decisive, is the maintenance in a Wind Farm where it can lead directly to a saving of energy cost. Wind farm maintenance presents, in fact, significant logistical challenges. They are usually distributed throughout the territory and also located at considerable distances from each other, they are generally found in places far from uninhabited centers and sometimes difficult to reach and finally spare parts are rarely available on the site of the plant itself. In this paper, we will study the problem concerning the optimization of maintenance logistics of wind plants based on the use of specific vehicle routing optimization algorithms. In particular a pickup-and-delivery algorithm with time-window is adopted to satisfy the maintenance requests of these plants, reducing their management costs. The solution was applied to a case study in a renewable energy power plant. Results time reduction and simplification and optimization obtained in the real case are discussed to evaluate the effectiveness and efficiency of the adopted approach.

Urban logistics is a discipline born in the 1990s, with the aim of solving the problems that exist regarding the nuisance caused by the urban freight transport. The optimisation of urban logistics takes a crucial importance not only for the development of the logistics competitiveness of the country but also for the economic development. The optimisation of logistics also enables a better quality of life and promotes the accessibility and attractiveness of urban communities. Urban freight transport and package delivery have undergone a paradigm shift in recent decades, caused by increasing urbanisation and the development of cities. Highly studied in terms of public policies and decision making methods, this discipline has left the supply chain management dimension and the link with the global supply chain in the background. This dimension is fundamental for the sustainability of urban distribution systems with a high environmental respect. The planning and quantification of urban freight delivery areas is one of the main tools for improving delivery conditions in public spaces. The implementation of these areas addresses a problem of management of the use of urban space by the various users. Indeed, economic activities generate many movements of goods which increase the need for deliveries. The non-inclusion of loading/unloading movements in the planning of urban space can be the source of many dysfunctions (congestion, road insecurity, noise pollution, etc.). The existence of parking areas dedicated to professionals also has a major economic aspect. The time spent by operators in finding a parking location directly impacts their efficiency and economic performance. This research paper aims to analyse the relationship between the neglect of package delivery needs in urban space planning and the performance of last-mile logistics in the city of Meknes. According to the results of the survey, more than 55% of the operators consider that the supply of parking spaces is insufficient and that the time spent by delivery personnel to find a location has a negative impact on the cost of package delivery.

Container logistics is a typical logistics model with complex business processes. In recent years, operation optimization in logistics is identified as significant in cutting labor costs and boosting productivity. To implement operation optimization in container logistics, the accurate load measuring and operational situation information are the prerequisites. However, the current labor-driven measurement and situation recognition suffer from inevitable delays, errors, and expenses. To tackle these challenges, the authors propose a smart sensing system that intelligently achieves the container load measurement and operational situation recognition. In this paper, the authors present a novel approach for load measurement and the design of an edge-computing-powered controller. The kNN (k-Nearest Neigh-bor) has been introduced to develop an Operational Situation Recognition (OSR) model. The authors realize and implement the smart sensing system in the production environment. Rigorous analysis and prototype evaluations demonstrate the effectiveness of the proposed system.

Purpose: The aim of the study was to analyze the challenges faced by SME companies and analyze the opportunities and challenges in the supply chain logistics sector and analyze the best practices and techniques for the optimization of supply chain and logistics. Design/methodology/approach: Exploratory research was carried out using the secondary data collected from the Supply Chain Logistics companies and the analysis of the study was carried out using prior academic literature. Findings: From the study, it was found that the major challenges faced by SMEs in Oman were the lack of finance, lack of marketing knowledge, operational limitations, and lack of entrepreneurial culture and managerial skills. The opportunities in the supply chain sector included the Location, Infrastructure, Economic Opportunities, and Economic Diversification of Oman along with challenges like Human Resource Competency, Competition, Connection, Technology, and Transactions. Research limitations/implications: Based on the results, it was recommended that SMEs should focus on improving their Human Resource Competency, and gain a competitive advantage through optimization of supply chain and logistics activities using technology and implementing optimization strategies. Social Implications: The findings from this study have provided an academic and empirical contribution to the cargo and logistics companies and the SME community in Oman. Furthermore, provides contextual and managerial contributions to the literature on the optimization of supply chain logistics in SMEs. Originality / Value: No prior studies were found addressing optimization challenges and opportunities faced by SME cargo and logistics companies. Through this theoretical study, the researcher attempted to shed light on and tackle this situation. Keywords: SMEs in Oman, Supply Chain and Logistics Optimization, SME Cargo Companies in Oman, Porter’s Value Chain, Challenges and Opportunities faced by SME Cargo companies in Oman.

PurposeThis study primarily explores the influence of green warehousing, logistics optimization and social values and ethics on supply chain sustainability and economic performance. The study further examines the mediating role of supply chain sustainability between economic performance and green warehousing, logistics optimization and social values and ethics.Design/methodology/approachThe study employs a quantitative research approach where survey data are collected from 200 managers of manufacturing companies in Ghana. The dataset is analyzed using partial least square structural equation modeling software (PLS-SEM) SmartPLS 3.FindingsThe results show that green warehousing and logistics optimization negatively influence economic performance but improves economic performance through supply chain sustainability. It is further discovered that social values and ethics have a positive influence on supply chain sustainability and economic performance.Originality/valueThis paper proposes and tests a theoretical model that explores the relationships between green warehousing, supply chain sustainability, economic performance, logistics optimization and social values and ethics through the resource dependency theory (RDT) in the manufacturing firms in Ghana.

Road construction all over the world especially in the developing countries is considered to be one of the most important projects for a country. As important as they might be it becomes more important to execute them with utmost care as to optimize the logistics and minimizing the waste to optimize the cost and ultimately planning in such a way to maximize the productivity of the construction team which depends mainly upon different aspects relating to materials access to the workers on site, cost of them, choice of materials etc. Logistics optimization is one of the major factors for successful delivery of road construction projects. Logistics comprises the flow of information and material, optimization can be achieved through effective planning in terms of material planning, material delivery, material management and material handling. This research paper is aimed at studying the importance of material in logistic process of road construction projects in Pakistan. The study may be applicable to any developing or developed country as the logistics optimization for road construction remains same. A case study approach is taken to study two major road construction firms operating in Pakistan, the data is collected through observation and semi structured interviews of project managers, directors and on site managers. The in depth analysis presented with critical aspects of material management, as per MATRACON Pvt ltd and Marksons Pakistan. A comparative analysis provided us with a lot of components which are important for logistics optimization out of which this research focuses on the most important factor which is Material optimization in Logistics. Keywords: Logistics optimization, Road construction, Material management, Material planning.

In recent years, the integration of artificial intelligence (AI) into logistics optimization has gained significant attention, particularly concerning sustainability criteria. This article provides an overview of the diverse AI models and algorithms employed in logistics optimization, with a focus on sustainable practices. The discussion covers several techniques, including generative models, machine learning methods, metaheuristic algorithms, and their synergistic combinations with traditional optimization and simulation methods. By employing AI capabilities, logistics stakeholders can enhance decision-making processes, optimize resource utilization, and minimize environmental impacts. Moreover, this paper identifies and analyzes prominent challenges within sustainable logistics, such as reducing carbon emissions, minimizing waste generation, and optimizing transportation routes while considering ecological factors. Furthermore, the paper explores emerging trends in AI-driven logistics optimization, such as the integration of real-time data analytics, blockchain technology, and autonomous systems, which hold immense potential for enhancing efficiency and sustainability. Finally, the paper outlines future research directions, emphasizing the need for further exploration of hybrid AI approaches, robust optimization frameworks, and scalable solutions that accommodate dynamic and uncertain logistics environments.

This article delves into the significance of logistics process optimization in the construction industry and its direct impact on productivity. Construction companies face unique challenges in managing the flow of materials, equipment, and personnel, making streamlined logistics crucial for efficient project delivery. By examining key strategies and approaches to logistics optimization, this article aims to provide insights into how construction companies can increase productivity and maximize project outcomes. The construction industry is a complex and dynamic sector that relies heavily on effective logistics processes to ensure the smooth flow of materials, equipment, and personnel. In an era where efficiency and productivity are key drivers of success, construction companies are increasingly focusing on optimizing their logistics operations. By streamlining and enhancing these processes, companies can significantly improve productivity, reduce costs, and ultimately deliver projects more efficiently. This article explores the importance of logistics optimization in construction and provides insights into key strategies for achieving enhanced productivity, because optimizing logistics processes in construction is instrumental in increasing productivity and achieving successful project outcomes. By focusing on effective planning, technology adoption, lean principles, collaboration, and modular construction, companies can streamline operations, reduce costs, and enhance resource utilization. The article emphasizes that logistics optimization is an ongoing journey that necessitates adaptability and awareness of industry trends. By prioritizing logistics optimization, construction firms can position themselves for success, deliver projects on time and within budget and ultimately gain a competitive advantage in the market.

Modern Russia goes through a difficult period of crisis and exchange of sanctions that have left a definite imprint on the Russian players in the logistics market. Logistics, as a so-called satellite, the motor trade is in their systems a huge potential. It is a sort of “blood arteries of the country” – water, road, rail, air. Any changes in international economic relations are automatically projected onto the market of logistics services. This article discusses the position of logistics in today's economic situation, as well as methods and means of adaptation and optimization under these conditions. The article reveals the undeniable advantages of the WMS – system, and describes a method for effective solutions for cost optimization in logistics as a cross – docking. This article also describes the idea and in fractional reveals the essence of this method of cost optimization solutions. In the process of studying the article becomes abundantly clear that this system has a distinct advantage in the absence of the need for investment funds, the only necessity of the system is an effective HR management and monitoring processes. There is confirmation of the fact that in difficult times of crisis, logistics has a huge potential and opportunities for growth and optimization. We give evidence that the innovative WMS technology strengthens its position and deeper embedded in the system of logistics and cross-docking, in turn, is one of the effective ways of making cost optimization in logistics at this time in this situation.

Methods. The research employs a comprehensive systemic approach to analyze the economic, social, and political factors influencing Ukrainian retail during wartime. Methods include statistical analysis to track changes in market structure (e.g., sales dynamics, share of local producers, and consumer spending patterns), comparative analysis of pre- and post-war business models, content analysis of scientific publications and reports from major retailers, and case studies on adaptive strategies of companies such as «ATB-Market», «Ashan» and «Silpo». The study is based on official statistics, marketing research, and expert evaluations. Results. Key adaptation strategies of Ukrainian retailers are highlighted, namely: localization of supplies, which led to an increase in the share of Ukrainian goods in chains, for example, in «ATB» this indicator increased by 20%; omnichannel, integration of online and offline sales, for example, «Rozetka» opened more than 50 points of order delivery; optimization of logistics - investments in local logistics centers and automation of processes. The supplier «Nova Poshta» has implemented new sorting systems; revision of the assortment in order to increase the number of middle and economy class goods. Retail chains are introducing their own brands of basic goods; digitalization – the use of data analysis for inventory management and demand forecasting. Competition from small and medium-sized businesses is a significant problem for large retailers in the form of growing competition from small trading enterprises. Often, such enterprises are more flexible in pricing, can respond more quickly to changes in demand, and are also able to adapt their strategies to specific market conditions. Novelty. The study presents a comprehensive analysis of the transformational processes in Ukrainian retail under wartime conditions. For the first time, it systematically categorizes adaptive measures like supply chain localization, omnichannel development, and digitization. The study also highlights changes in consumer behavior, including increased demand for essential and budget products, offering unique insights and practical recommendations for retailers in times of crisis. Practical value. The results are useful for retailers in developing localization strategies, digital transformation, and targeting the needs of different consumer groups. Ukrainian retail in wartime conditions has undergone large-scale changes caused by economic, logistical, and social challenges. The findings can also be used for state support of retail and small businesses in times of crisis.

The advancement of technological innovation and the growth of the variety of customer demands, promote the evolution of traditional challenges that pushes the industrial sector to change in order to follow market trends and maintain its positioning, this transformation impacts the current structural and managerial organization of companies. Technological innovation has become a prerequisite for modern production which influences directly the concept of logistics. In the context of Industry 4.0 which includes not only the development but also the integration of new information and communication technologies within the industry, the widespread use in the last decade of applications such as Enterprise Resource Planning, Transportation Management Systems and Warehouse Management Systems prove the adoption of technological innovation through logistics. Following the major stages of the industrial revolution, logistics is currently at the level of phase 4. The Distributed scheduling algorithm might be the new driving force of optimization in the Logistics 4.0 under Industry 4.0 key enabling technologies.

In 21 century, the fourth industrial revolution has influenced the way that modern business is continuing. These days’ Supply Chain Management (SCM) is playing a vital role and higher completive area. Therefore, worth exploring how techniques of Industry 4.0 utilization in the process of supply chain & the role of Industry 4.0 on achieving strategic objectives of SCM. The study presented in this paper reviews recent literature in Industry 4.0 and SCM to link the effects of Industry 4.0 on strategic objectives of supply chain management. Within this study, 30 different articles based on the study area were reviewed, through which the aspects of quality improvement, the efficiency of supply chains, optimization of logistics & distribution are found to be heavily impacted due to the developments of industry 4.0. The findings of this study explored that quality improvement, the efficiency of supply chains, transport, and logistics are heavily impacted due to the developments of industry 4.0 concepts within supply chains in the modern age. After achieving objectives of quality enhancement and optimization of logistics and distribution, it will lead to achieve the third objective of efficiency. Further, using the proposed model of this study indicated the linkage between the strategic objective of the supply chain in the industry 4.0 era. Future research suggested to consider other strategic objectives such as the leadership of SCM. And conduct the study concerning specific industries such as apparel, fast food.

Practices of collaborative operation in supply logistics is analyzed and the concept of Supply-Hub is discussed.Then the research of collaborative operation mode is reviewed based on supply-hub.Furthermore,research progress of collaborative operation and optimization in supply logistics based on supply-hub is analyzed from the four aspects,namely inventory coordination,vendor managed inventory(VMI) integration and just in time(JIT) technology,coordination between production and distribution,the other operation mode and technology.On this basis,main problems of collaborative operation and optimization in supply logistics based on Supply-Hub are put forward.Based on comparison of current results and investigation of manufacturing enterprises,research trends and directions of operation and optimization in supply logistics based on Supply-Hub are expounded.

This review paper examines the use of artificial intelligence (AI) and big data analytics in solving supply chain management (SCM) issues and enhancing future operational efficiency. The primary objective is to synthesize existing research and provide a comprehensive overview of how these technologies are revolutionizing SCM. The paper systematically reviews recent literature on AI and big data applications in SCM, focusing on key areas such as demand forecasting, inventory management, and logistics optimization. By analyzing various studies and case examples, it highlights the transformative effects of these technologies on supply chain processes. The review covers a range of AI techniques including machine learning, deep learning, and predictive analytics, as well as the role of big data in capturing and processing large volumes of supply chain-related information. Key findings from the literature indicate significant improvements in supply chain visibility, decision-making accuracy, and operational efficiency. AI and big data analytics enable more precise demand forecasting, better inventory control, and optimized logistics, leading to cost reductions and enhanced responsiveness to market fluctuations. The review also discusses the challenges and considerations for implementing these technologies, such as data quality, integration complexity, and the need for specialized skills. The paper emphasizes the critical role of AI and big data analytics in addressing contemporary SCM issues and fostering future-ready supply chains. It underscores the necessity for organizations to adopt these technologies to stay competitive and achieve long-term operational excellence. The insights provided serve as a valuable resource for researchers and practitioners aiming to leverage AI and big data for supply chain innovation. Keywords: Artificial Intelligence (AI), Big Data Analytics (BDA), Supply Chain Management (SCM), Predictive Analytics, Internet of Things (IoT), Blockchain Technology, Automation, Machine Learning, Inventory Management, Logistics Optimization, Supply Chain Visibility, Real-time Monitoring, Data Quality, System Integration, Risk Management, Operational Efficiency, Strategic Decision-making, Sustainability, Supply Chain Resilience, Emerging Technologies.

Equipment failures in manufacturing processes concern industries because they can lead to severe issues regarding human safety, environmental impact, reliability, and production costs. The stochastic nature of equipment degradation and the uncertainty about future breakdowns affect significantly the maintenance and inventory decisions. Proactive event processing can facilitate this decision-making process in an Industrial Internet of Things (IIoT) environment, but real-time data processing poses several challenges in efficiency and scalability of the associated information systems. Therefore, appropriate real-time, event-driven algorithms and models are required for deciding on the basis of predictions, ahead of time. We propose a proactive event-driven model for joint maintenance and logistics optimization in a sensor-based, data-rich industrial environment. The proposed model is able to be embedded in a real-time, event-driven information system in order to be triggered by prediction events about the future equipment health state. Moreover, the proposed model handles multiple alternative (imperfect and perfect) maintenance actions and associated spare parts orders and facilitates proactive decision making in the context of Condition-Based Maintenance (CBM). The proposed proactive decision model was validated in real industrial environment and was further evaluated with a comparative and a sensitivity analysis.