Raffaele Iannone

The pharmaceutical industry, whose origins date back to the 15th century, has evolved into a complex research-intensive sector characterized by uncertainty in product development, the protection of intellectual property, and high profitability. In today’s globalized and highly competitive environment, the ability to monitor and optimize organizational performance has become necessary. Key Performance Indicators have emerged as strategic tools for translating corporate objectives into measurable outcomes, guiding decision-making processes, and enhancing competitiveness. Therefore, this paper examines the strategic role of KPIs within pharmaceutical companies, highlighting their importance in aligning operations with corporate objectives and distinguishing between continuous manufacturing and discrete manufacturing. A methodological framework is proposed to analyze the interrelationships among KPIs, beginning with the selection of relevant indicators from the literature, the analysis of their mathematical formulations and underlying data, and progressing to the identification of interconnections between Base Data, Intermediate Metrics, and the KPIs. The application of this approach enables a deeper understanding of systemic effects in performance measurement by providing decision-makers with an analytical tool capable of mapping cause-and-effect relationships and predicting the impact of operational decisions on overall production performance.

As manufacturing companies strive for timely and reliable deliveries, being able to accurately predict delivery times to customers represents a key factor influencing their competitivity. This problem, which is entirely dependent on the order Lead Time (LT) prediction, is particularly acute in Small and Medium Enterprises (SMEs). These companies’ processes are mainly labor-intensive and typically require a high degree of customisation. Even though this represents the SMEs’ strength point, this is also an element of extreme variability for the overall assessment of LT. In addition, SMEs generally cannot count on sophisticated digital manufacturing tools, such as Enterprise Resource Planning (ERP) systems, due to their costs and complexity in implementation. Thus, this study aims to contribute a Masterplan-based system for order lead time prediction in SMEs. This system represents the theoretical background needed for the development of an easily implementable decision support system for planning and monitoring in SMEs. Such a project management tool could integrate both the workforce and machines capacity assessment and would represent a solution for improving order planning processes. This would support SMEs to satisfy and respect customer requests.

Predicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and consequently the ability to meet customer deadlines. This paper presents an approach that leverages machine learning to enhance workload prediction with minimal data collection, making it particularly suitable for SMEs. A case study application using supervised machine learning models for regression, trained in an open-source data analytics, reporting, and integration platform (KNIME Analytics Platform), has been carried out. An Automated Machine Learning (AutoML) regression approach was employed to identify the most suitable model for task workload prediction based on minimising the Mean Absolute Error (MAE) scores. Specifically, the Regression Tree (RT) model demonstrated superior accuracy compared to more traditional simple averaging and manual predictions when modelling data for a single product type. When incorporating all available product data, despite a slight performance decrease, the XGBoost Tree Ensemble still outperformed the traditional approaches. These findings highlight the potential of machine learning to improve workload forecasting in manufacturing, offering a practical and easily implementable solution for SMEs.

Human–robot interaction (HRI), in all its different variants, has gained significant attention in industry. The combination of human skills and robotics significantly enhances manufacturing, improving operations quality and efficiency. This is particularly relevant in highly variable contexts like remanufacturing, which aims to preserve the value of products and components over time and involves complex, non-standardized tasks such as inspection, disassembly, reprocessing, and reassembly. Uncertainties related to the conditions of products to remanufacture cannot be managed by pure automation making HRI highly beneficial. Although researchers have focused on the use of HRI in several fields of application (from construction to logistics), no extensive mapping of the research trends has specifically addressed the intersection of HRI and remanufacturing contexts. To fill this gap, this paper presents a comprehensive study based on a bibliometric analysis to explore the current state of research on this topic. Based on 120 articles from the Scopus database, this study points out the main countries, authors, and affiliations involved in the field. In addition, the key topics were analyzed. A predominant focus, above all in the last three years, on Human-Robot Collaboration (HRC) among the different levels of HRI, and disassembly processes was highlighted, while other related activities, such as reassembly, have received comparatively less attention. An unexplored frontier is the integration of Artificial Intelligence and Machine Learning with HRC tasks in remanufacturing. These findings offer valuable insights for researchers, scholars, and industrial professionals aiming to advance HRI in remanufacturing.

The evolving landscape of industrial manufacturing is increasingly embracing automation within smart factories. However, the critical role of human operators, particularly in manual assembly and disassembly tasks, remains undiminished. This paper explores the complexities arising from mass customization and remanufacturing, which significantly enhance the intricacy of these manual tasks. Human involvement is essential in these tasks due to their complexity, necessitating a structured learning process to enhance efficiency and mitigate the learning–forgetting cycle. This study focuses on the utilization of virtual reality (VR) as an innovative training tool to address these challenges. By conducting a systematic literature review (SLR) on the impact of VR on training operators for assembly and disassembly tasks, this paper evaluates the current level of VR application, the used technologies, the operator performance, and the VR benefits and limitations. The analysis reveals a limited but promising application of VR in training, highlighting its potential to improve learning outcomes, productivity, and safety while reducing costs. However, the research also identifies gaps in the practical application of VR for training purposes suggesting a future research agenda to explore its full potential.