HR UTILITY FRAMEWORK MARKOV EMPLOYEE TRANSITION HR UTILITY

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HR UTILITY FRAMEWORK & MARKOV EMPLOYEE TRANSITION

HR UTILITY FRAMEWORK & MARKOV EMPLOYEE TRANSITION

HR UTILITY FRAMEWORK Utility analysis measures the economic contribution of a program according to

HR UTILITY FRAMEWORK Utility analysis measures the economic contribution of a program according to how effective the program was in identifying & modifying behavior, hence future service contribution of employees. Utility is a function of the duration of a training programs effect on employees performance, number of employees trained, validity of training program, value of job for which training was provided & total program cost.

DOLLAR VALUE OF A TRAINING PROGRAM ∆U=T*N*dt*Sdy-N*C Where ∆U= monetary value of training program

DOLLAR VALUE OF A TRAINING PROGRAM ∆U=T*N*dt*Sdy-N*C Where ∆U= monetary value of training program T=duration in no of yrs of training programs effect on performance N= number of employees trained dt=true difference in job performance between the average trained & the average untrained employees in units of standard deviation Sdy=standard deviation of job performance of untrained group in dollars C=cost of training per employee-

MARKOV EMPLOYEE TRANSITION Predicting internal supply of labor at some future time. It is

MARKOV EMPLOYEE TRANSITION Predicting internal supply of labor at some future time. It is to describe and forecast the process of human resource flows or movements within, into, and out of the organization. Including promotion , demotion, transfer, exit, new hire, etc. Heart of markov analysis is transition probabaility matrix. . describes probabilities of an incumbent staying in his or her present job for the forecast time period(1 yr) , moving to another job in the organization or leaving the organization.

DEVELOP TRANSITION PROBABILITY MATRIX Specify mutually exclusive & exhaustive set of states that include

DEVELOP TRANSITION PROBABILITY MATRIX Specify mutually exclusive & exhaustive set of states that include all jobs between which people can move & an exit state for those who quit, retire or are fired. Gather data on transition rates Develop stable, reliable estimated of expected future transition rates

Transition probability matrix MARKOV(Time 2) ANALYSIS Time 1 Job A Job B Job C

Transition probability matrix MARKOV(Time 2) ANALYSIS Time 1 Job A Job B Job C Job D Exit Job A 0. 70 0. 10 0. 05 0 0. 15 Job B 0. 15 0. 60 0. 05 0. 10 Job C 0 0 0. 80 0. 05 0. 15 Job D 0 0 0. 05 0. 85 0. 10 Matrix applied to incumbents Initial staffing level Job A Job B Job C Job D Exit Job A 62 44 6 3 0 9 Job B 75 11 45 4 8 7 Job C 50 0 0 40 2 8 Job D 45 0 0 2 38 5 55 51 49 48 29 Predicted end of yr

Markov analysis describes what is expected to happen if existing transition rates remain the

Markov analysis describes what is expected to happen if existing transition rates remain the same. Longer range forecasts Find irregularities in flow of employees Forecast internal supply Identifying career paths & mobility patterns that may be helpful in career planning & development.