Forecasting Internal Labor Supply at Baganuur JSC, A Strategic Energy Hub of Mongolia: A Markov Chain Analysis

Abstract

Workforce planning and forecasting are critical for enhancing organizational performance, particularly in capital-intensive industries. This study analyzes and forecasts the internal labor supply of Baganuur JSC, a state-owned coal mining company in Mongolia, using a Markov chain model. The analysis draws on historical human resource data spanning 2005–2014, from which transition probabilities across workforce segments—including age, education level, gender, and tenure—are estimated. The findings reveal a pronounced aging trend, with a growing proportion of employees approaching retirement age, signaling a potential risk of labor shortages and loss of experienced personnel. Scenario analysis further demonstrates that variations in hiring and attrition rates significantly influence long-term workforce stability. Based on the quantitative forecasts, the study derives actionable strategic insights: by 2035, approximately 40% of the workforce is projected to be aged 50 or above, necessitating an estimated 60–80 new hires annually to sustain operational capacity. Three scenario-based HR strategy frameworks—baseline, high-attrition, and accelerated recruitment—are presented to support succession planning and human capital sustainability. The findings contribute to the limited body of quantitative workforce forecasting research in developing economies and provide data-driven decision-making tools for HR managers in Mongolia's mining sector.