An Integrated Markov Chain–Fuzzy AHP Framework for Workforce Planning and Turnover Driver Prioritization: A Case Study at PT X - Rahman Soesilo; Adelia Dwi Valentin; Fitriana Hisnainy Latifah; Salsabila Al Ghifari (Integrasi: Jurnal Ilmiah Teknik Industri, 2026)
This study aims to develop an integrated workforce planning framework by combining the Markov Chain method and Fuzzy Analytical Hierarchy Process (Fuzzy AHP) at PT X, a manufacturing company with relatively high employee turnover. Historical workforce data from 2020–2023 were used to construct inter-grade transition patterns, while assessments from 15 experts were used to prioritize turnover drivers. The Markov Chain method was applied to project workforce distribution and compare it with workforce requirements based on 2024–2025 production targets. The projection results indicate potential workforce shortages of 28 employees in 2024 and 77 employees in 2025 if transition and turnover patterns remain unchanged. The Fuzzy AHP results show that external factors have the highest weight of 0.845, with individual motivation as the dominant sub-criterion, having a local weight of 0.803 and a global weight of 0.679. These findings suggest that integrating Markov Chain and Fuzzy AHP can support decision-making in estimating workforce needs while determining priority areas for retention strategies. The contribution of this study lies in the integrative application of both methods in a case-based manufacturing workforce planning context.