Designing an interactive manpower decision-making interface supporting the workforce optimization under demand fluctuations

This study investigates the challenges of manpower planning and optimization for operational businesses functioning across multiple locations, where fluctuating customer demand and varying workforce capacities create significant planning complexities. To address these issues, several forecasting techniques were applied to predict order volumes for a three-month period, forming the analytical basis for a comprehensive workforce planning model. Based on forecasting results, a mathematical model was developed to determine the optimal number of workers required to meet dynamic demand levels. The model incorporates key operational parameters and automatically adjusts to changes in demand, workforce availability, and processing times, ensuring flexibility and long-term applicability. The analysis revealed recurring overstaffing and understaffing, highlighting the importance of aligning staffing levels with forecasted demand to reduce labour inefficiencies. To support decision-making, a dynamic and interactive planning interface was designed to integrate forecasting outputs, staffing requirements, and other performance indicators. The interface provides real-time visibility into workforce status, enabling identification of overstaffed, understaffed, and optimal staffing conditions. It also visualizes demand patterns in various perspectives, such as by hour, shift, month, and location. The model and the interface were validated through five scenario-based tests, demonstrating their responsiveness to abrupt changes such as seasonal peaks, peak hours, order picking time, and sudden fluctuations in order volume. Overall, the integrated forecasting and modelling interface offers a practical, data-driven solution for enhancing manpower planning and operational efficiency in multi-location business environments.