Samir Lamouri

This paper introduces a symbolic AI workflow that combines knowledge representation (geometric theories, bridge morphisms) with automated reasoning (SMT solving) to provide machine-checkable verification and obstruction diagnosis for semantic correspondences between heterogeneous data models.The workflow targets semantic interoperability across engineering, manufacturing, and service Bill-of-Materials views in product lifecycle management, where data exchanged between design, manufacturing, and maintenance platforms may be structurally correct yet semantically wrong. Existing schema-matching, ontology-alignment, and model-transformation approaches help construct correspondences, but they rarely provide formal evidence that a transferred fragment is semantically valid or pinpoint why a mapping fails. The proposed method encodes schemas as geometric theories, correspondences as bridge morphisms, and uses SMT solving to decide whether a lifecycle-data fragment preserves meaning under stated applicability conditions or, when it does not, to localize the obstruction.Three layers are evaluated: a pedagogical Part--Material example establishing the formal mechanism; a curated aerospace-style case study covering eBOM/mBOM/sBOM fragments with 16 entity sorts, 77 function symbols, and 33 theory-internal axioms; and a public NIST benchmark used as a reproducible bridge-coverage study on open lifecycle artifacts. The solver-backed campaign comprises 30 verification checks. On the curated industrial case, median runtimes range from 2.8 ms for obstruction checks to 204.9 ms for full consistency, with all checks below 1 s. On the public fragment, the method supports exact manufacturing-to-inspection correspondence for 56 of 60 runs (93.3%) and localizes four unmatched runs to identifier-level obstructions. These results show that symbolic AI can deliver verifiable semantic-assurance evidence on bounded lifecycle-integration corridors.

L’article présente le premier numéro de la Revue Française de Gestion Industrielle, qui a été consacré au thème des méthodes japonaises de gestion de production, notamment le Toyota Production System (TPS). L’article analyse le contenu et l’impact de l’article de Yasuhiro Monden, qui a été le premier à décrire et à modéliser le TPS en 1981. L’article souligne l’importance et l’actualité du TPS, qui repose sur le concept de flux continu de production et qui nécessite un pilotage continu sans calcul. L’article met également en évidence les limites et les adaptations du TPS dans le contexte occidental, ainsi que l’évolution de la recherche de Yasuhiro Monden vers le management fonctionnel de Toyota. Enfin, l’article conclut en rendant hommage à la contribution de Yasuhiro Monden à la compréhension et à la diffusion du TPS

Despite technological progress and a large amount of research on Industry 4.0, digital transformation remains a complex process that most manufacturers are hesitant to invest in. Interest in digital Kanban, for example, remains low compared with traditional Kanban, which is widely used. This applies to the other card-based production control systems, including CONstant Work-In-Process (CONWIP), which is the focus of this paper. In an industrial context where digitization and Industry 4.0 are the main trends, one may wonder why traditional CONWIP is preferred to digital CONWIP. Following a praxeological approach (i.e., study of practice and instrumentation), this article explores the strengths and weaknesses of the CONWIP practice, in both its paper and electronic versions, while taking into account the human dimension. The aim is to motivate potential CONWIP users to implement it in its digital mode and to show them how a Digital Twin-based solution can overcome the managerial problems that arise with digitization while enabling improved performance. As an illustration, experience feedback from several companies using Digital Twin with CONWIP is provided.

In the era of industry 5.0, digital twins (DTs) play an increasingly pivotal role in contemporary society. Despite the literature’s lack of a consistent definition, DTs have been applied to numerous areas as virtual replicas of physical objects, machines, or systems, particularly in manufacturing, production, and operations. One of the major advantages of digital twins is their ability to supervise the system’s evolution and run simulations, making them connected and capable of supporting decision-making. Additionally, they are highly compatible with artificial intelligence (AI) as they can be mapped to all data types and intelligence associated with the physical system. Given their potential benefits, it is surprising that the utilization of DTs for warehouse management has been relatively neglected over the years, despite its importance in ensuring supply chain and production uptime. Effective warehouse management is crucial for ensuring supply chain and production continuity in both manufacturing and retail operations. It also involves uncertain material handling operations, making it challenging to control the activity. This paper aims to evaluate the synergies between AI and digital twins as state-of-the-art technologies and examines warehouse digital twins’ (WDT) use cases to assess the maturity of AI applications within WDT, including techniques, objectives, and challenges. We also identify inconsistencies and research gaps, which pave the way for future development and innovation. Ultimately, this research work’s findings can contribute to improving warehouse management, supply chain optimization, and operational efficiency in various industries.

The purpose of this article is to propose a guide for the digital transformation (4.0) of a manufacturing SME’s medium-term production planning process, the master production schedule (MPS). A model of the current MPS process of a group of SMEs is presented as a starting point toward digitization. The current state of this process reveals a lack of tools to support decision making and the need to increase the reliability of input data and to make the process more agile. Industry 4.0 technologies and process modeling could increase agility in the planning process. However, the digital transformation of medium-term planning activities in SMEs has not been studied. To fill this gap, a group of six experts was consulted. The novelty of this study was to identify the Industry 4.0 technologies that could improve medium-term planning and integrate them into a standardized MPS process model. This model is an ultimate point of digitization that cannot be achieved immediately by any SME, but only after several cycles of planning, deployment, and improvement. Therefore, this research also provides a method to help SMEs determine how to start the digitization of their MPS process.

In recent years, there has been an increasing awareness of sustainable development issues. Supply chain actors have become more and more aware of this situation, especially since the regulatory, social and societal pressures in this area are becoming more and more numerous. This state of affairs is not without consequences on company practices and economic. As a result, the urban warehouse model is emerging as one of the solutions studied in the urban logistics context. The characteristics, constraints and challenges of this model are presented in this article in order to define this new logistics facility. Secondly, automated storage and retrieval systems (AS/RS), today considered as a solution from Industry 4.0, are studied through a case study in order to determine their potential to meet the challenges of urban warehouses. Their ability to optimize available surfaces by densifying stocks in limited spaces is particularly demonstrated.

Unplanned events such as epidemic outbreaks, natural disasters, or major scandals are usually accompanied by supply chain disruption and highly volatile demand. Besides, authors have recently outlined the need for new applications of artificial intelligence to provide decision support in times of crisis. In particular, natural language processing allows for deriving an understanding from unstructured data in human languages, such as online news content, which can provide valuable information during disruptive events. This article contributes to this research strand as it aims to leverage textual data from news through sentiment analysis and predict demand volatility of pharmaceutical products in times of crisis. As a result, (1) a deep-learning-based sentiment analysis model was developed to extract and structure information from medicines-related news; (2) a framework allowing for combining extracted information from unstructured data with structured data of medicines demand was defined; and (3) an approach combining efficient artificial intelligence techniques with existing forecasting models was proposed to enhance demand forecasting in times of disruption. Additionally, the framework was applied to two examples of disruptive events in France: a pharmaceutical scandal and the COVID-19 pandemic. Findings outlined that using sentiment analysis allowed for enhancing demand forecasting accuracy.