Enhancing worker-centred digitalisation in industrial environments: A KPI evaluation methodology

Recently, the European Commission announced Industry 5.0 as a strategic initiative toward a value-driven industrial transformation. This new paradigm coexists with previous Industry 4.0 revolution that has guided the efforts towards technology driven industrial digitalisation in the past ten years. As part of this Industry 4.0 strategies, numerous KPI-driven evaluation methods were proposed to cover the multiple pillars of smart industry assessment. However, they do not incorporate human workers and actors in a systematic way as drivers for digitalisation processes, as the new Industry 5.0 paradigm argues. This paper addresses this gap by proposing an evaluation methodology that incorporates multiple human actors in the digitalisation process. The final objective of this methodology is to evaluate the direct and indirect benefits of the technology-driven transformation process to achieve the goals of human workers and other human stakeholders. To this end, our methodology provides the basis for proposing assessment tools and instruments for technological and infrastructure integration, process optimisation, new functionalities and human factors benefits, and four core indicators that have been applied to a real case comparing the digitalisation processes of three different companies.

Artificial Intelligence Algorithm Qualification: A Quality by Design Approach to Apply Artificial Intelligence in Pharma

Quality is defined by the American Society for Quality (ASQ) as "the totality of features and characteristics of a product or service that bears on its ability to satisfy given needs." Therefore, quality is applicable to processes that supply outcomes with values that can be measured. Statistical control is an effective methodology that provides the outcome of quality of goods, bringing an added value that other methods, like quality by inspection, do not offer. The statistical methods applied to process control have been thoroughly developed, and the mathematics that supports them have been broadly demonstrated. Artificial Intelligence (AI) is a field in which mathematics, statistics, and programming play a joint role, and its results can also be applied to disciplines like quality control. Nevertheless, its utilization is subordinate to the qualification of the implemented algorithms. This research presents a standard procedure to AI algorithms, allowing their usage in regulated environments to ensure the quality of the delivered products or services (e.g., in drugs and medicines manufacturing). The regulated principles are defined by the concept of quality by design (QbD), which is a notion introduced in the pharmaceutical industry as a good practice for process management under multivariate analysis. This study intended to provide guidance for qualifying AI algorithms using QbD guidelines as the foundation for this purpose.