Impact of Autonomic Computing on Process Industry

Traditional sustainability frameworks in large scale production systems, such as Process Industry (PI) ones, often overlook operational resilience, creating a “resiliency gap” where systems optimized for efficiency remain vulnerable to disruptions. This study addresses this gap by proposing and empirically validating a Quadruple Bottom Line (4BL) framework that integrates resilience as the fourth pillar alongside economic, environmental, and social goals. The purpose is to evaluate the impact that Autonomic Computing (AC) can imply in this perspective. A Procedural Action Research (PAR) methodology was conducted across four distinct PI industrial cases (asphalt, steel, pharma, and aluminum). This involved the ECOGRAI framework to qualitatively link strategic companies’ objectives to shop-floor Key Performance Indicators (KPIs), guiding the assessment of AC systems. The results show benefits at a business level observed following the introduction of AC systems, which were implemented for enhancing resilience by managing ML model drift. Key findings include reduction in plant downtimes, decreases in waste (steel), reductions in gas consumption, and improved operator trust. This research provides empirical evidence that AC can make resilience an actionable component of industrial strategy, leading to measurable improvements across all four pillars of the 4BL framework. Its contribution is methodological and operational, aiming to demonstrate feasibility and causal plausibility.