A Multi-Criteria Decision-Support Framework for Dynamic Workforce Allocation in an Internal Software-Development Service Function Embedded Within a Large Manufacturing Organization

Abstract

Dynamic workforce allocation in project-oriented service environments requires simultaneous consideration of heterogeneous and continuously changing information on employee competencies, availability, workload, expertise, and project requirements. However, allocation decisions frequently remain dependent on fragmented information and managerial judgement, limiting consistency, transparency, and responsiveness. This study develops and empirically evaluates a lightweight multi-criteria decision-support framework for dynamic workforce allocation using an Action Design Research methodology. The framework structures allocation through task characterization, candidate eligibility filtering, and employee ranking based on an Allocation Suitability Score integrating competency matching, availability, normalized workload, and expertise. The weighting configuration was calibrated through four iterative rounds involving ten organizational experts and subsequently examined through sensitivity analysis, demonstrating 100% stability of the top-ranked candidate across 16 weight perturbations and a mean Spearman rank correlation of 0.994. Industrial evaluation was conducted within a project-oriented internal software-development function comprising 102 employees and an organizational portfolio of 284 projects. Quantitative validation used seven purposively selected paired allocation cases evaluated under equivalent conventional and framework-assisted conditions. The framework was associated with an 84.3% reduction in mean allocation time, a 92.9% reduction in workload imbalance, a 12.6% relative increase in resource utilization, a 25.3% increase in allocation consistency, and a 98.0% reduction in manual intervention requirements. These quantitative findings were complemented by favorable managerial assessments of transparency, workforce visibility, responsiveness, and usability. The results provide case-specific empirical evidence that transparent, computationally lightweight multi-criteria decision support can substantially improve workforce-allocation processes while preserving managerial interpretability and implementation flexibility. The proposed framework therefore provides a reproducible foundation for dynamic workforce allocation in project-oriented, knowledge-intensive service environments, while broader generalizability requires longitudinal and cross-organizational validation.