To engineer the modern large, complex, interdisciplinary systems-of-systems (SoS), the collaborative world teams must “speak” the same language and must work on the same “matter.” The “matter” is the system model and the communication mechanisms must be supported by standard, flexible, and friendly modeling languages. The evolving model-based systems engineering (MBSE) approach is leading the way and is expected to become a standard practice in the field of systems engineering (SE) in the next decade. As an emerging paradigm for the systems of the 21st century, it seems useful to overview its current state of the art concerning the developing standards, the embryonic formalisms, the available modeling languages, the methodologies, and the major applications.

This systematic literature review investigates the recent applications of artificial intelligence (AI) in supply chain management (SCM), particularly in the domains of resilience, process optimization, sustainability, and implementation challenges. The study is motivated by gaps identified in previous reviews, which often exclude literature published after 2020 and lack an integrated analysis of AI’s contributions across multiple supply chain phases. The review aims to provide an updated synthesis of AI technologies—such as machine learning, deep learning, and generative AI—and their practical implementation between 2021 and 2024. Following the PRISMA framework, a rigorous methodology was applied using the Scopus database, complemented by bibliometric and content analyses. A total of 66 studies were selected based on predefined inclusion criteria and evaluated for methodological quality and thematic relevance. The findings reveal a diverse classification of AI applications across strategic and operational SCM phases and highlight emerging techniques like explainable AI, neurosymbolic systems, and federated learning. The review also identifies persistent barriers such as data governance, ethical concerns, and scalability. Future research should focus on hybrid AI–human collaboration, transparency through explainable models, and integration with technologies such as IoT and blockchain. This review contributes to the literature by offering a structured synthesis of AI’s transformative impact on SCM and by outlining key research directions to guide future investigations and managerial practice.

The topic of digital transformation in supply chain optimization has garnered considerable attention in recent years due to its importance. The purpose of the study was to offer empirical evidence and insights into the advantages and obstacles linked with digital transformation in supply chain management. To investigate the effects of digital transformation on supply chain optimization, the research employs a hybrid methodology and comprehensive approach that includes a thorough literature review, the creation of a theoretical framework, and the presentation of empirical finings through various case studies using the predefined selection criteria. The case analyses highlight crucial elements that support effective digital transformations, including real-time data analytics, teamwork, blockchain technology, digital twin augmented and virtual reality and collaborative robots. The practical implications from the findings of this study, proffers insights that can be extremely helpful for professionals in various industrial sectors and businesses planning similar digital transformation journeys. This empirical study with regards to the implication of Digital transformation 5.0 on supply chain management is novel to the body of literature. It is however necessary to conduct more study to confirm the results, apply them to a wider range of businesses, and investigate different aspects of digital transformation in supply chain optimization.

Purpose: The purpose of this paper is to present an attempt to develop an instrument containing operational measures of lean combined with safety and ergonomic conditions in a workstation or production line. This operational tool aims to help researchers and practitioners to prioritize and evaluate the lean implementations, as well as the ergonomic and safety conditions, in an integrated way. Design/methodology/approach: Lean manufacturing methods and principles, as well as safety and ergonomics aspects, were exhaustively researched with the ultimate goal of finding a way to improve the workplace by taking into account the efficiency and well-being of workers. The instrument was validated in an interactive process between theory and practical insights. At the end, it was tested in several workstations/production areas. Findings: The study reveals that high scores are derived from a good interaction between lean, ergonomics and safety. Research limitations/implications: It would be important to validate it in different companies and different types of industries because each one has its own characteristics. Practical implications: This tool helps practitioners (technicians and ergonomic practitioners from manufacturing companies) assess the implementation of lean principles and the safety issues in their processes. It also allows managers to evaluate their business and identify the priority areas to improve according to the previously defined company’s aims. Originality/value: As Peter Drucker said: “If you can’t measure it, you can’t improve it.” For a successful implementation, managers should start the lean journey with a lean assessment and make it in a regular basis. To the authors’ knowledge, there are various lean assessment tools, but this work is innovative because it provides an assessment instrument to evaluate organizations’ workstations/production areas simultaneously in three dimensions: lean, safety and ergonomic aspects.