A multi-level framework for capacity adjustment and flexible decision-making under data constraints in production planning and control

Abstract

Abstract Capacity adjustment (CA) remains a persistent challenge in production planning and control (PPC), especially in workload-controlled environments where firms must regulate effective capacity through labor, machine, material, and inventory interventions. Analytical approaches such as optimization, discrete-event simulation, reinforcement-learning-based scheduling, and digital twin systems have advanced CA research, but they often assume that the data required for implementation are complete, structured, and accessible. In practice, capacity-relevant information is distributed across ERP systems, MES platforms, maintenance databases, HR repositories, manual records, and external sources, with heterogeneous ownership, provenance, and readiness. This paper addresses this gap through a multi-method Design Science Research (DSR) approach that combines systematic review, taxonomy development, and artifact design. Three contributions are made. First, a five-axis taxonomy is developed from 55 studies selected through a systematic Scopus review of CA research published between 2020 and 2025. Second, the Data-Aware Capacity Adjustment Framework (DACAF) is introduced as a seven-step decision-support artifact that screens CA options by assessing data provenance, ownership, and readiness across manual, hardware, and software layers. Third, established flexibility metrics are translated into pre-implementation feasibility lenses, allowing organizations to assess whether the information needed to evaluate adjustment breadth, transition effort, and performance stability is available before implementation. DACAF is demonstrated through a formative illustrative application to a published SME case. This application illustrates the framework logic but does not constitute empirical field validation; testing DACAF’s practical diagnostic utility through expert assessment and industrial field application remains a necessary next step. The paper contributes to PPC and workload control research by embedding data feasibility as a design condition rather than treating it as a downstream implementation barrier.