Francesco Mancusi

Handwritten Text Recognition (HTR) is an Artificial Intelligence (AI) technology designed to interpret and digitize handwritten text, enabling automated data extraction and reducing the reliance on manual processing. While HTR has been extensively developed in theory, leveraging advanced machine learning and pattern recognition models, its practical adoption remains uneven. Documented applications exist in fields such as historical document digitization, banking, and healthcare; however, its use in industrial and manufacturing contexts is still largely unexplored. This practical case study explores the use of HTR in an industrial setting by redesigning the operational execution of acceptance sampling activities in a pharmaceutical manufacturing company, in which handwritten records are traditionally used. The performance of the HTR software, which employs a patented hybrid stroke-based recognition method combining neural and statistical classifiers with structural matching, was tested and evaluated in terms of recognition accuracy and process efficiency to assess its potential to meet industrial standards. Preliminary findings indicate that while HTR can improve efficiency, limitations in recognition accuracy, and the need for human validation restrict full automation. Although promising, further advancements in recognition models and system adaptability are needed to ensure reliable and seamless integration into industrial workflows. Graphical Abstract

Industrial machine tools are both performance assets and environmental hotspots over their long service lives. Maintenance is traditionally optimized to safeguard availability, quality and cost. However, maintenance choices also determine the energy consumption, footprints, component duration and end-of-life pathways. In this study, we present a decision framework to compare performance-only maintenance (POM) with sustainability-aware maintenance (SAM) for machine tools. The framework integrates degradation and Remaining Useful Life (RUL) estimation, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC). Outcomes are summarized with a Sustainable Maintenance Balance (SMB) index. We test the proposed approach on a horizontal machining center for aluminum, validated by running a Monte Carlo simulation over a 1000 h functional unit. Across empirical data and simulation, SAM—compared to POM—demonstrated an ability to improve availability, reduces downtime and scrap, and lower total LCC while cutting carbon emissions. The proposed method is proposed as readily deployable in real plants, supporting robust sustainable-production decisions.

The rise of sustainability in the luxury sector presents a paradox: for decades, luxury has been seen as antithetical to sustainability, yet younger socially conscious consumers increasingly demand eco-friendly and ethical practices from luxury brands. This study investigates the factors influencing consumer adoption of sustainable luxury products, with a focus on generational differences. We developed a framework identifying five categories of drivers of sustainable purchase intent and generated - using AI techniques - a simulated survey dataset of 1000 luxury consumers spanning four generations (Boomers, Gen X, Millennials, Gen Z). Using this dataset, we trained an ensemble of machine learning models - Random Forest and Neural Network - to test the possibility to predict purchase propensity and identify key drivers of sustainable luxury adoption. Feature importance analysis revealed that ethical sourcing was the top driver of purchase intent, followed by environmental impact and product quality. According to consumer segmentation, younger generations exhibited a higher propensity to adopt sustainable luxury, confirming a generational shift toward stronger sustainability values. Consumer adoption of sustainable luxury hinges on brands delivering superior quality and prestige alongside authentic sustainability commitments. The adaptive AI-driven profiling and forecasting can help tailor strategies to consumer segments, guiding the luxury sector toward more sustainable consumption. Across the identification of luxury value constructs and dimensions, we proposed strategies tailored on specific customer segments to predict luxury consumers behaviors and, eventually, to promote acceptance of sustainable practices.