Nowadays, integrating Artificial Intelligence (AI) into supply chains (SCs) is a great challenge in research and for manufacturing managers. The main goal of this study is to determine the role of AI in the context of the new SCs, according to the concept of Industry 5.0. in order to improve the level of sustainable production. The research was based on a systematic analysis of the scientific literature and application of the PRISMA methodology. Due to the relatively new vision of introducing AI into SC, it was decided to analyse the years 2021–2024. A total of 1181 research articles were identified in Science Direct, Springer and the Willey Online Library that combined AI-based methods and tools that support SCs in order to identify the impacts and challenges of integrating AI in SCs in the context of sustainable production (SP). In this study, 48 items were then analysed in detail. The results achieved highlighted the main AI-based tools applied in SCs and, secondly, revealed the main benefits of this integration for manufacturing in the following areas of manufacturing: predictive maintenance, production planning and customer relationships. The findings of our study revealed the main challenges and directions: (1) integrating digitalisation and green SP in order to build resilience to the SP, (2) create a sustainable work environment, (3) and develop a sustainable and advanced architecture for relationships with customers.

Nowadays, managers are facing the challenge of operating in situations of high uncertainty: delayed deliveries, lack of energy or rising energy and gas costs, the need to replace energy sources, and changing supply and sales markets. In the literature, two dominant trends in the activities of enterprises in the face of crises can be distinguished: (I) changes in supply chain management (increased flexibility by searching for local suppliers); and (II) transition to digital production and investment in technologies in the concept of Industry 4.0 or even Industry 5.0, such as artificial intelligence, 3D printing, robots, cyber-physical systems, digital manufacturing, and blockchain. A gap in the research has been observed in examining the impacts of these actions on the implementation of sustainable solutions and designating organizational changes in manufacturing. The main goal of this study is to review the literature using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) for data collection and, secondly, the methodology of Systematic Literature Review (SLR) and Mapping in Literature Reviews (MLR). Our literature review of the selected databases is based on 566 published articles in 2020–2022. The achieved results indicate the main organizational changes in the context of sustainable development in manufacturing, namely in the business management area (adopting Sustainable Project Management (SPM), Sustainable Supply Chain Management practices, Sustainable Supplier Selection (SSS), and Resilient Manufacturing Strategy (RMS)) and in the production area (adopting Internet of Things (IoT)-enabled Additive Manufacturing assists, simulation software, and Life Cycle Assessment. The findings of our study revealed key relationships between the adoption of fifth-generation industrial technologies and the sustainable development of manufacturing.

In recent years, more reports and articles have been published on the necessity of implementing changes to manufacturing processes in the context of sustainable development (SD). The use of IT systems in manufacturing companies creates the opportunity to support changes implemented in the context of adaptation to the concept of SD. This paper proposes and explores a Sustainable Development Card (SDC) within an enterprise, the use of which will enable knowledge about the enterprise’s development level to be gained in the context of SD through the data and information collected from the information system, namely, the Enterprise Resource Planning system (ERP) implemented within a company. This research uses studies from the literature on the subject and also the case study in order to determine the SDC integrated with the ERP system. The Analytic Hierarchy Process (AHP) method is used, which uses validity indicators to allow a manufacturing company’s development to be evaluated within the context of SD. The results were verified by a real case study of a Polish manufacturing company. It was determined that the proposed approach allows SD to be assessed in a manufacturing company and enables the implementation process of SD strategy to be constantly monitored. This paper contributes to theory and practice at the interface of the development and sustainability of a manufacturing company, whereby assessing sustainability is associated with the development of strategy and information systems that are associated with the development of an organisation. The possibilities of using the results of research, in practice, are demonstrated convincingly.

The widespread implementation of Human–Robot Collaborations (HRC) in industry is becoming an obvious step in the era of Industry 4.0 or even 5.0 and the requirements of sustainable production (SP) due to the increase in productivity and quality while reducing production costs. However, due to limited studies on the establishment of collision thresholds in HRC, in-depth research is needed to determine possible disruptions for safety in HRC. So, firstly, in order to identify current issues and critical challenges in the field of HRC safety, an analysis of the literature was carried out using the Systematic Literature Review (SLR) & Mapping in Literature Reviews (MLR) methodology based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines (PRISMA). A total of 4,107 research articles were identified in Science Direct—Elsevier, Web of Science and Springer, that focussed on safety in HRC in SP. In this study, 103 items were then analysed and 53 in the second stage. The key research contributions are developed as the principles for enhancing safety in HRC in industry: (1) improving HRC workspace safety, (2) building mechanisms for monitoring HRC workspaces and (3) managing ethical issues. In line with these trends, secondly, experimental studies were provided. Next, empirical research was conducted to establish the parameters for ensuring the safety of the monitoring system for work in HRC. Therefore, analysis of the video recordings on the efficiency of detecting collision in HRC, when using the Region-Based Convolutional Neural Network (YOLOv8 Tiny), within the vision system were researched. The experimental studies indicate the necessary actions within a monitoring system in HRC in order to ensure workplace safety in the HRC environment. Finally, the research results suggest future research directions as (1) proactive HRC with attention to collaborative robots and (2) integration with the approach, to ensure safety in HRC into SP strategy in industry. This study provides new insights into HRC and gives an outlook for development of safety issues in HRC in industry. Graphical abstract

Nowadays, achieving the objectives of sustainable development (SD) within a manufacturing company, through introducing and integrating sustainability into a development strategy, is a key parameter in gaining a competitive advantage in the market. The objective of this study was to develop a decision-tree based methodology to facilitate SD assessment in a manufacturing company, which consists of five main components: (1) Determination of SD indicators based on literature analysis, (2) Using the Analytic Hierarchy Process (AHP) method which determines the priority of the SD criteria, (3) Collecting data to determine the values of the key objectives SD, (4) Using a decision tree to build scenarios of possible actions to increase the level of SD, (5) Indicating recommended actions for continuous monitoring of progress towards reaching SD objectives. In the proposed approach, the use of the AHP method allowed for indicating the most important SD indicators, which made it possible to limit the number of queries to manufacturers on data from real companies regarding the values of SD indicators. Finally, the methodology was applied and verified within a real manufacturing company in order to assist the Management Board in making projections about future actions regarding an increase in SD level.