human resource planning, fuzzy Markov (periodic), traditional Markov, shortages and surpluses of human resources.

Most experts see human resources as the most important and most valuable asset of any company. As a result, a method that would help create an accurate plan for this asset is highly valued and functional. This study aimed to improve challenges and gaps in human resource planning using a Markov chain model.  There are two fundamental problems with Markov chain model in predicting human resources, which are respectively failing to account for future uncertainties and generalization of the data from one basic period in the prediction of the future needs. These problems are embedded in the Markov chain model. In order to deal with these problems, a fuzzy Markov chain model (periodic or temporal) was proposed in this study. This study presented a fuzzy Markov model for predicting human resources. This was a descriptive, sectional study and the data were analyzed through a quantitative method. The application of the fuzzy Markov model in human resource planning was presented in five consecutive steps. The application of the traditional Markov model in human resource planning was also examined for comparison with the proposed model. The study findings suggest that the prediction of human resources using the periodic fuzzy Markov model offers a powerful tool that bridges the gaps in the traditional Markov planning. It considers three states, namely optimistic, positivist, and pessimistic, for determining human resources surplus and shortage, and does not generalize data pertaining to one period to future periods.