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.

The fourth industrial revolution, called Industry 4.0, is transforming decision-making through the increasing use of information and digitization technologies. While Industry 4.0 is expanding rapidly in manufacturing industries, its induced transformations are gradually affecting other sectors, including the construction industry. In recent years, the use of 4.0 technologies in the construction industry, termed as ‘Construction 4.0’, has increased, mostly due to the immense potential of Industry 4.0 for improving the performance of construction projects and structuring their underlying management processes. This paper proposes a classification of existing literature on applications of Construction 4.0 technologies to allow for a better analysis of trends and gaps in the research. A total of nearly 200 research papers between 2009 and 2020 were reviewed and analyzed. Overall, the analysis shows that research on Construction 4.0 is closely aligned with the construction phase. Also, the most researched topics seem to be related to the management processes of quality, risk, and health and safety.

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.

In order to meet the increasingly complex expectations of customers, many companies must increase efficiency and agility. In this sense, Industry 4.0 technologies offer significant opportunities for improving both operational and decision-making processes. These developments make it possible to consider an increase in the level of operational systems and teams’ autonomy. However, the potential for strengthening the decision-making process by means of these new technologies remains unclear in the current literature. To fill this gap, a Delphi study using the Régnier Abacus technique was conducted with a representative panel of 24 experts. The novelty of this study was to identify and characterize the potential for enhancing the overall decision-making process with the main Industry 4.0 groups of technologies. Our results show that cloud computing appears as a backbone to enhance the entire decision-making process. However, certain technologies, such as IoT and simulation, have a strong potential for only specific steps within the decision-making process. This research also provides a first vision of the manager’s perspectives, expectations, and risks associated with implementing new modes of decision-making and cyber-autonomy supported by Industry 4.0 technologies.

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.