A Strategic Workforce Planning Framework Using Mathematical Optimisation for Asset-Intensive Industries

This research develops a strategic workforce planning optimisation framework for asset intensive industries. The research addresses the gap between informal workforce practices and strategic, data-driven decision-making. The proposed framework integrates mathematical optimisation techniques into the strategic planning process and recognises the complex challenges faced by the asset-intensive industry, such as long project lifecycles, strict regulations, and skill shortages. A 10-step structured framework is designed to guide organisations to effectively align workforce capabilities with their strategic goals. The framework is validated through semi-structured expert interviews to confirm its clarity, applicability, and potential for practical implementation. A Python-based prototype further demonstrates the framework’s feasibility and effectiveness. Using a systematic literature review, mixed-integer linear programming (MILP) emerged as the optimal algorithm for this prototype due to its flexibility, scalability, and robustness for long-term workforce decisions. Furthermore, the prototype used anonymised data from the energy sector. The integrated approach of the framework enables organisations to make informed workforce decisions, ensuring operational efficiency, strategic alignment, and sustained competitiveness.