Effects of Prioritized Input on Human Resource Control in Departmentalized Markov Manpower Framework

Area of Science:

• Operations Research
• Applied Mathematics
• Human Resource Management

Background:

• Traditional Markov manpower models lack mechanisms for retaining experienced personnel during system fluctuations.
• Departmentalized systems face challenges in preserving trained individuals lost due to financial constraints or contract expirations.
• The need for adaptable workforce models that can manage recruitment and retention effectively is critical.

Purpose of the Study:

• To formulate extended Markov manpower models incorporating a novel 'limbo class'.
• To analyze the control aspects and maintainability of manpower structures within these new models.
• To investigate recruitment strategies and their impact on system stability.

Main Methods:

• Development of homogeneous Markov manpower models with an added limbo state for members.
• Mathematical formulation of flow matrices and stochastic conditions.
• Analysis of system expansion and shrinkage scenarios with prioritized recruitment from limbo or outside environments.

Main Results:

• The maintainability of the manpower structure via promotion is independent of the limbo class structure during system expansion (prioritizing outside recruitment).
• Maintainability through promotion is independent of the active class structure during system shrinkage (prioritizing limbo recruitment).
• Established necessary and sufficient conditions for manpower structure maintainability through recruitment in expanding systems.

Conclusions:

• The extended Markov model with a limbo class provides a robust framework for managing departmentalized manpower systems.
• The model offers insights into optimizing recruitment strategies to ensure workforce stability and preserve experienced personnel.
• The findings contribute to the strategic control and long-term sustainability of human resource management in dynamic environments.