AI-Assisted Integrative Workforce and Capacity Management

Manufacturing companies must deal with a high level of volatility and uncertainty. Consequently, the demand for agile and decentralized decision-making in the context of production scheduling becomes apparent, since traditional rigid planning methods are failing to adapt to real-time disruptions. This paper presents a concept and architecture of a Digital Scheduling Dashboard, which is based on an autonomous scheduling process enhanced by an AI-assisted optimizer. The DSD retains Enterprise Resource Planning (ERP) systems as the authoritative baseline but delegates day-level assignment authority to assembly workers. A non-prescriptive AI-based optimization engine runs in the background, serving as a fact-checker by pre-computing complex eligibility constraints and micro-conditions (such as machine readiness, material status, qualification validity, and HSE incompatibilities) that are absent in the ERP’s low granularity. The system presents workers with a pre-selected set of feasible options while reserving the final order selection as the worker’s autonomous choice. By combining employee autonomy with AI-assisted optimization, the use case aims to improve responsiveness, reduce planning overhead, and optimize resource utilization in fluctuating production scenarios.