Because of their cross-functional nature in the company, enhancing Production Planning and Control (PPC) functions can lead to a global improvement of manufacturing systems. With the advent of the ...Industry 4.0 (I4.0), copious availability of data, high-computing power and large storage capacity have made of Machine Learning (ML) approaches an appealing solution to tackle manufacturing challenges. As such, this paper presents a state-of-the-art of ML-aided PPC (ML-PPC) done through a systematic literature review analyzing 93 recent research application articles. This study has two main objectives: contribute to the definition of a methodology to implement ML-PPC and propose a mapping to classify the scientific literature to identify further research perspectives. To achieve the first objective, ML techniques, tools, activities, and data sources which are required to implement a ML-PPC are reviewed. The second objective is developed through the analysis of the use cases and the addressed characteristics of the I4.0. Results suggest that 75% of the possible research domains in ML-PPC are barely explored or not addressed at all. This lack of research originates from two possible causes: firstly, scientific literature rarely considers customer, environmental, and human-in-the-loop aspects when linking ML to PPC. Secondly, recent applications seldom couple PPC to logistics as well as to design of products and processes. Finally, two key pitfalls are identified in the implementation of ML-PPC models: the complexity of using Internet of Things technologies to collect data and the difficulty of updating the ML model to adapt it to the manufacturing system changes.
Industry 4.0 provides new paradigms for the industrial management of SMEs. Supported by a growing number of new technologies, this concept appears more flexible and less expensive than traditional ...enterprise information systems such as ERP and MES. However, SMEs find themselves ill-equipped to face these new possibilities regarding their production planning and control functions. This paper presents a literature review of existing applied research covering different Industry 4.0 issues with regard to SMEs. Papers are classified according to a new framework which allows identification of the targeted performance objectives, the required managerial capacities and the selected group of technologies for each selected case. Our results show that SMEs do not exploit all the resources for implementing Industry 4.0 and often limit themselves to the adoption of Cloud Computing and the Internet of Things. Likewise, SMEs seem to have adopted Industry 4.0 concepts only for monitoring industrial processes and there is still absence of real applications in the field of production planning. Finally, our literature review shows that reported Industry 4.0 projects in SMEs remained cost-driven initiatives and there in still no evidence of real business model transformation at this time.
In this work, we studied the aptitude to sintering green bodies using γ-Al2O3 transition alumina as raw powder. We focused on the influence of the heating rate on densification and microstructural ...evolution. Phase transformations from transition alumina γ→δ→θ→α-Al2O3 were studied by in situ X-rays diffraction from the ambient to 1200°C. XRD patterns revealed coexistence of various phase transformations during the heating cycle. DTA and dilatometry results showed that low heating rate leads to a significant reduction of the temperature of the α-Al2O3 alumina formation. Around 1190, 1217 and 1240°C were found when using 5, 10 and 20°C/min of heating rate, respectively. The activation energy for θ-Al2O3→α-Al2O3 transformation calculated by Kissinger and JMA equations using dilatometry method were 464.29 and 488.79kJ/mol, respectively and by DTA method were 450.72 and 475.49kJ/mol, respectively. In addition, the sintering of the green bodies with low heating rate promotes the rearrangement of the grains during θ-Al2O3→α-Al2O3 transformation, enhancing the relative density to 95% and preventing the development of a vermicular structure.
En este trabajo, se ha estudiado la capacidad de sinterización de muestras en verde a partir de γ-Al2O3 de transición en forma de polvo. El trabajo se ha focalizado en la influencia de la velocidad de calentamiento sobre la densificación y la evolución microestructural. Las transformaciones de fase de alúminas de transición γ→δ→θ→α-Al2O3 se han estudiado in situ mediante Difracción de Rayos X (DRX) desde temperatura ambiente hasta 1.200°C. Los diagramas de XRD han revelado la coexistencia de diversas transformaciones de fase durante el ciclo de calentamiento. Los Análisis Térmicos Diferenciales (ATD) realizados y los datos de dilatometría han mostrado que velocidades de calentamiento bajas conducen a una reducción significativa de la temperatura de formación de α-Al2O3. Detectándose alrededor de 1.190, 1.217 y 1.240°C cuando se utilizan 5, 10 y 20°C/min como velocidad de calentamiento, respectivamente. La Energía de Activación para la transformación θ-Al2O3→α-Al2O3 calculada mediante las ecuaciones de Kissinger y JMA usando métodos dilatometricos han sido 464,29 y 488,79kJ/mol, respectivamente, y mediante ATD 450,72 y 475,49kJ/mol, respectivamente. Además, la sinterización con velocidad de calentamiento baja promueve la reorganización de los granos durante la transformación θ-Al2O3 → α-Al2O3, el aumento de la densidad relativa al 95% y la prevención del desarrollo de una estructura vermicular.
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.
Si la collaboration digitale autour du BIM est toujours un défi de nos jours, comment cette problématique était-elle traitée dans la période de gestation du BIM, avant les années 2000 ? Le présent ...article mène une revue de littérature rétroactive sur le sujet, à travers 6 thèmes récurrents (la comparaison avec les industries digitalisées, la conjonction de l’organisationnel et du numérique, le bouleversement des temporalités du projet, les nouveaux modèles de données, les processus dynamiques et l’anticipation), dans le but de donner une perspective historique à cette question actuelle.
If BIM digital collaboration is still a challenge today, how was this issue addressed in the BIM gestation period before the 2000s? This article retrospectively reviews the scientific literature on the subject, through 6 recurring themes (comparison with digitalized industries, the conjunction of organizational and digital modes, the disruption of project temporalities, new data models, dynamic processes and anticipation), in order to give a historical perspective to this current issue.
Industry 4.0 is increasingly being promoted as the key to improving productivity, promoting economic growth and ensuring the sustainability of manufacturing companies. On the other hand, many ...companies have already partially or fully implemented principles and tools from the Lean management approach, which is also aimed at improving productivity. While the two approaches use very different strategies, they share some common principles. The objective of this article is to highlight the links between the principles and tools proposed by Industry 4.0 and those proposed by the Lean management approach, with a particular focus on how some of Industry 4.0's technologies are improving the implementation of Lean principles, depending on the technologies' capability levels. As such, this study aims to provide a characterisation of the impacts of Industry 4.0 technologies on Lean principles according to targeted capability levels. The results obtained show strong support for Industry 4.0 technologies for Just-in-time and Jidoka, but little or no support for waste reduction and People and Team work. There is, therefore, a clear need to pursue the deployment of Lean management while improving certain Lean principles using Industry 4.0 technologies.
SMEs, as prominent actors in industry, must meet more and more complex customer expectations. Recently, the concept of Industry 4.0 has emerged. This new approach enables the control of production ...processes by providing real-time synchronisation of flows and by enabling the production of unitary and customised products. Our research goal is to identify Industry 4.0 risks, opportunities and critical success factors with regards to the industrial performance of SMEs. The recent emergence of Industry 4.0 and the inherent difficulty of identifying detailed examples has not yet enabled a satisfactory statistical study to be conducted on Industry 4.0 cases in SMEs. To reach our research goal, we selected 12 experts to conduct a Delphi study supplemented by Régnier's abacuses. Our study demonstrates that the major risks facing the adoption of Industry 4.0 in SMEs include a lack of expertise and a short-term strategy mindset. Our research also indicates that training is the most important factor for success, that managers have a prominent role in the success and/or failure of an Industry 4.0 project, and that SMEs should be supported by external experts. Lastly, Industry 4.0 offers a unique opportunity to redesign SME production processes and to adopt new business models.
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.
In recent years, data analytics in pharmaceutical supply chains has aroused much interest as it has the potential of enabling better supply and management of healthcare products by leveraging data ...generated by modern systems. This article presents the current state, opportunities, and challenges of data analytics in pharmaceutical supply chains through a systematic literature review surveying the Scopus, ScienceDirect, and Springerlink databases. 85 publications from 2012 to 2021 were reviewed and classified based on the research approach, objective addressed, and data used. The contributions of this paper are threefold: (i) it proposes a framework focused on challenges and data resources to assess the current state of data analytics in pharmaceutical supply chains; (ii) it provides examples of techniques exemplified that will serve as inspiring references; and (iii) it gathers and maps existing literature to identify gaps and research perspectives. Findings outlined that despite promising results from machine learning algorithms to address drug shortages and inventories optimisation, the various data resources have not yet been fully harnessed. Unstructured data have barely been used and combined with other types of information. New challenges related to green practices adoption and medicines supply during crises call for further applications of advanced analytics techniques.
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