Human Resource Planning

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System Dynamic Modeling of Human Resource Planning for a Typical IT Organization Narahari N. S. ” and Narasimha Murthy H. M ” Abstract This paper is a report on the study conducted to investigate the changing dynamics of the Human Resource Planning (HRP) systems in a typical Information Technolog}’ (IT) industries. Human Resource Planning subsystem in an organization involves analyzing and forecasting the talent requirements ofthe organization. The main focus in the study was to construct and de’elop a system dynamic model for the human resource planning process.

System Dynamics is used as a research vehicle to provide useful insights for human resource planners in arriving at the Strategic plans in achieing the strategie plans for the organization. The variables ami factors included in the modeling process have been derived based on inputs obtained from the Human resource planners of leading IT organizations. The systematic methodology adapted in the study and presented in this paper details the assumptions in modeling, the construction ofthe causal hop diagrams involving the feedback loops and testing the model with inputs using the ? ld data. The analysis of this initial conceptual mode! of the human resource planning problem is then performed using a system dynamic simulation tool namely /- think. The HR planners can now simulate the effect. ^ ofthe uncertainties in . supply and demand, using the system dynamics model. This model will help a HR planners in generating implementable human resource plans. Key words: Human Resource Planning System, System Dynamic Modeling, Causal Loop Diagi-am. Stock flow diagram. Feedback loop. Work force level, Flow Variables, Converter Variables, 1.

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Introduction Information Technology (IT) industry is a growth oriented mdustry and is continuously developing both m terms of scale and stze of operations. Tlie industry is now venturing into new areas in a globalized business environment. IT organisation requires professionals with right skills and right attitude to be deployed for positions based on global business opportunities. The human resource planning subsystem in an IT organization is faced with the challenge of matching supply and demand.

The human resource planners need to address cultural and technological issues in a global, market driven, flexible strategic planning for the human resource. Integration of the IT organization with the eco system is cmcial for deriving optimal plans for „managing human resources. In order to be profitable and sustainable in a immensely competitive environment, the planners have to devise responsive and robust planning models with the integrated perspectives of all the levels in the organization and with the consideration of the right set of influencing variables, that impacts the planning process, Research Scholar, Avinashilingam University, Coimbatore and Head – lEM Department, R V College of Engineering, Bangalore 560 059, India, E-mail: nsnarahari@gmail. com ” Department of Mechanical Engineering, R. V. College of Engineering, Bangalore 560059, E-mail: hnmdatta@yahoo. com * Author for correspondence 2009,1’oL 2, ! Mp. S 33 ‘Bm, ‘Mani System Dynamic Modeling of Human Resource Planning for a typical IT organization

The top three concerns of the top management in a Infomiation Technology product and services companies are managing the labour cost, managing risk and rapid pace of technology changes requiring quick skill transformation and adaptation. “Human Resource Planning can be defined as a systematic analysis of Human Resource needs in order to ensure that correct numbers of employees with the necessary skills are available when tbey are required”. Human resource forecasting of demand and supply, using statistical techniques or mathematical models are commonly used.

However, the existing methodologies, due to their fundamental limitations, fall short of enabling dynamic structural analysis or identification of delayed feedback effects. Actions undertaken based on inaccurate demand forecasts can occasionally produce results tliat are opposite to the intended ones. As a result of forecast inaccuracies and potential mismatches in decisions many system dynamics practitioners desiie to shift managerial emphasis away from forecasting and towards understanding and policy design.

There has been a number of attempts by both academia and the industry practitioners to develop human resource planning methodologies and approaches to address the specific issues of planning in organizations. The techniques ofthe human resource planning reported in the literature use the static view of Ihe planning process especially with regard to demand and supply uncertainties. The dynamic features such as feedback delays and non linear relationships are considered, by very few modelers.

In order to study the behavior of a Dynamic HR planning systems. System dynamics modeling is an appropriate tool. This research report is a case example on the application of System Dynamics modeling for Human Resource Planning systems. 2. Literature Review **Workforce management is usually defined as to manage the workforce supply chain, and co-ordinate the demand and supply of human resource’*. Human resource management problems are often solved by qualitative approaches traditionally (Dellacca and Justice 2007, Malone 2004).

However, mathematical models have been playing crucial roles in workforce management during the last several decades, from the use of linear programming in staff scheduling (Haxmeier 1991) to the application of queuing techniques in workforce configuration (Kevin et al. 2002). Various publications on Operations Research (OR) applications in workforce management using various models, collected from books and published proceedings (Bryant and Niehaus 1978. Jessop 1966, Ward et al. l994) and Wang (2005) classified the OR techniques applied in workforce management into four major categories i. e. models based on Markov chain, computer simulation, optimization and system dynamics. These techniques are aimed at different aspects of workforce planning processes. System Dynamics, first bom with the name “Industrial Dynamics” through the work of Jay Fonester in 1961, originated from the theory of non-linear dynamics and feedback control of mathematics, physics and engineering (Forrester 1961 ). System dynamics is a method for developing “management fiigfti simulator” to help people leam about dynamic complexity and understand the sources of resistance to design more clTective policies (Sterman 2000).

The method allows to study and manage complex feedback systems by creating models representing real world system. An efficient human resource or intellectual capital investment strategy demand a good understanding of the dynamics of recruitment and training issues Hafeez, K. , and H. Abdelmeguid. (2003). , VoL 2, iVc. I 34 ‘BITS, li System Dynamic Modeling of Human Resource Planning for a typical IT organization Ermina Topintzi and Manos Nistazakis (2004) have identified that System Dynamics (SD) as a powerful method for studying and managing complex feedback systems, such as the one we find in business and other social systems.

In their paper, the authors consider a basic Human Resources Management System for a consultancy firm. Andrej Skraba, Miroljub Kljajic, Andrej Knafiic, Davorin Kofjac and Iztok Podbregar (2007) authored a paper titled Development of Himian Resource Transition Simulation Model in Slovenian Armed Forces to describe the development of continuous and discrete model of human resources transitions in large organization. The model considers eight different ranks. The calibration of the model was performed with the historical data to determine time constants of transitions and fluctuations.

Basic simulation runs were performed in order to complete predictive vahdation ofthe model. System dynamics is widely used to explore dynamics of human resource management and project management. Coyle(1996) explored a typical problem existing in consulting firms i. e. , how to recruit the right numbers of trainees and consultants upon the market potential. These models are mostly highly simplified and intuitive in nature. Human Resource Planning is the absolutely a vital function that looks firstly at strategy, and then deploys the necessary human capital (people) where it s called for in the organization as dictated by the stiategy. Human Resource Planning therefore falls into the wider area of employee resourcing (planning for, acquiring and allocating the desired human resources for the organization). 4. System Dynamic Modelling process System dynamics models expose the dynamic characteristics of a project or a set of projects. Important variables associated in the system are eaptured in the modeling process. The variables vary along the timeline and the system behavior would consequently change.

To respond to such changes, one has to thoroughly understand quantified impacts of the changes of leading indicators (variables). The different variables in the system are interrelated to each other in the feedback struclxire. These relationships can be represented diagrammatically to portray the system structure. The System Dynamics model can be developed with the help of the diagramming aids available. The dilTercnt diagramming aids available with System Dynamics are as follows: a. Causal Loop diagramThe primary purpose of the causal loop diagrams is to depict the causal hypothesis during model development. so as to make the presentation of the structure in an aggregate form. TTie causal loop diagrams help the modeler to quickly communicate the feedback structure and underlying assumptions. These are also known as influence diagrams. The causal loop represents the way in which a system works. Shovm in Figure 1 is an example of a causal loop diagramThis diagram includes elements and arrows (which are called causal links), linking these elements and also includes a sign (either + or -) on each link. 3. Problem Definition

Human Resource Planning can be defined as a systematic analysis of Human Resource needs in order to ensure that correct numbers of employees with the necessary skills are available when they are required. When a company prepares its planning program, it should bear in the mind that their staff members have their objective they need to achieve. This is the reason why employees seek employment. Neglecting these needs would result in poor motivation that may lead to unnecessary poor performance and even Industrial strifes.

CWiJ’L, 2009, Voi 2, 35 , -Mam System Dynamic Modeling of Human Resource Planning for a typical IT organization A typical causal loop diagram is given below in Figure 1: Casual Loop Diagram of Dynamic Manpower supply demand model. Figure 1: Casual Loop Diagram of Dynamic Manpower Supply— Demand Model b. Stock and Flow Diagram Stock and Flow diagrams- As with a causal loop diagram, the stock and flow diagram shows relationships among variables which have the potential to change over time.

Tlie main purpose of the flow diagram is to represent the detailed flow structure of the system in terms of the fine policy structures so as to facihtatc the development of the mathematical model for simulation. It is the most detailed diagram and overcomes the limitations of all the previous diagramming aids. It distinguishes the physical and information subsystems, and also classifies all the types of variables and functions. A typical stock and flow diagram is given in below figure 2: Stock and Flow diagram of Manpower planning model

Current Workforce level Future Workforce level Figure 2 Stock and Flow diagram of Manpower planning model 2009. 5S). 3 36 •BHS, System Dynamic Modeling of Human Resource Planning for a typical IT organization 4. 1 Modeling approach: The following sections describe the modeling approach adapted in the case study: i. Generating information for System dyiiamic modeling inputs: A suitable questionnaire was designed and administered to the selected human resource executives.

The design objective of the questionnaire was to generate input on the following: Existing model to predict HR requirements, career paths for an individual in the organization, whether specific problems are identified, how many to be recruited at the Junior most level, promotion of an individual from one level to another, how many individuals need to be hired for higher levels in the structure, uncertainty factors from supply and market side, level of attrition and retention strategies the organization is adopting. ii.

Assumptions The following assumptions were made for the purpose of constructing the model. Demand for Human Resources in a company is generated from the present projects and the planned projects being handled by the organization. Demand is expressed in terms of the number of employees required for at different levels. The demand for planned projects is assumed to follow a distribution pattern. Number required at any point of time is taken as the incremental number with reference to the current workforce or employee levels.

The loss in stock of employees are treated as loss due to the attrition of employees from the organization. The first five levels of a typical IT company and the mobility structure is considered for the purpose of modeling the now of human resources in the organization witli respect to time. The planning for human resources is done on a quarterly planning horizon. A one year time bucket is split into four planning horizons. The changes in the variables are tracked with one year period as the basis for analysis. ii. Notations Used a. Stock Variables NOLi : Number of employees present at Level i. where i = I to 5 b. Flow Variables NOHi : Number of employees being hired into Level i at a given time period, where i= I to5 NO Pij: Number of employees being promoted from Level i to Level j at a given time period i = I to 5, j = I to 5 and necessarily j > i NO Qi: Number of employees quitting or leaving the company from Le’el i at any given time period (where i = I to 5) c. Converter Variables

Rij: Number of employees required for present projects at level i and project j where i = I to 5 andj = I to 2 RAij: Ratio of number of employees at Le’el i to number of employees at Level j where i = I to 5 andj = 1 to 5 and necessarily j < i A Li: Attrition at Level i (where i = I to 5) H to Pj: Hiring to Promotion Ratio for Level] ( where j = 2 to 5) System Dynamic Model The model developed in this paper considers the stocks variables as the initial stock of personnel at different grades and the final stock at specific periods of time due to human resource dynamics. The No. f hires of different grades, promotion numbers from grade to grade and quitting or leaving numbers at different grades, through resignation or voluntary separation are defined as the fiow variables. The Converter variables arise due to Cltf{J% 2009. •Uoi. 2,9^. 3 37 , ‘. Piumi System Dynamic Modeling of Human Resource Planning for a typical IT organization the demand of personnel for different levels, as a result of flow of present projects as well as proposed projects. In addition, the ratio of employees from level i to level j is detlned as a regulating flow variables.

The attrition rate and the hiring to promotion ratio are also used to control the number of personnel at different grades at any point in time. Causal loop diagram indicates the manpower ilow at any instance of time and is able to model the dynamic changes in the manpower stoeks. The final causal loop diagram for the tlve cadre system taken up for study in this research is displayed in the Figure 3: Causal Diagram with feedback loops. X”X Figure 3 Causal Loop Diagram with Feedback loops The logic captured in the Causal loop diagram helps in studying the behavioral dynamics of the system considering the forward flows and the feedback loops.

Feedback loops help in regulating the numbers at different grades and maintaining the required stock equilibrium between supply and demand. The complex dynamics of the human resource flows can be completely captured in the Causal loop diagram. Tlie fuiat causal loop diagram for the , Z009, ‘i’oL Z, 9>u>. 3 38 System Dynamic Modeling of Human Resource Planning for a typical IT organization problem under investigation is depicted in Figure 3: Causal loop diagram with feedback loops. Next step is to build the actual model based on the relationship already established and activate the logic of relationship.

In this direction, a Stock and Flow Diagram is constructed to generate the logical sequencing of stock alterations and to build tlie flow dynamics as a result of the converter and fiow variables. Final stock flow diagram for the five cadre level organization assumed taken up for study in this research is show in the Figure 4: Stock and Flow diagram for a typical five cadre organization. Causal loop diagram and stock flow diagram together collectively have helped in building up the simulation experiments and obtain the output for different scenarios considered. 0 Figure 4 Stock and Flow diagram for a typical five cadre organization . Simulation Runs The assumed values for stock variables, flow variables and converter variables are shown in Table 1 : considering these stock levels as the base case scenario 2009, Vof. Z, 9^. 3 39 System Dynamic Modeling of Human Resource Planning for a typical IT organization Table 1 Base Case scenario Variables Requirements for Planned projects Levels 1 Mean Standard Deviation 2 Mean Standard Deviation 3 Mean 4 Stand-ard Mean Devia-tior Standard Deviation 5 Mean Standard Deviation 0,25 25 Present Projects Hiring to promotion ratio Initial stock levels 5 10 — 6. 25 0. 5 0. 5 2 1. 25 No, of employees for each level 5 )0% .25 2 40% 0. 25 4 Percentaiie 0 30% 50% Slock Level in numbers 1000 250 50 20 10 This paper presents simulation for liniited scenarios for two variations with respect to base case scenario. In the first scenario, the attrition values were varied for different levels by taking two trail runs. Every other input to the model was kept constant and the normal distribution values were taken as repeatable values by specifying a constant with the mean and standard deviation. The value of the converter variable, which is used to perturbe the model from the base case was the attrition rates at differetit levels.

The attrition rates assumed in two trails are given in the Table 2 below : Table 2 Attrition Rates with respect to Base Case Scenario Trial Cases 1 2 LEVELS 1 20% 18% 2 18% 15% 3 15% 12% 4 10% 8% 5 5% 4% The results of tbe simulation validate the system dynamic model and demonstrate the dynamics of the human resource planning system. Tbe crucial variables that are considered in developing tbe model include current values of human resources at different levels as stocks variables, hiring rate at different levels, promotion rate from level to level, no. f employees quitting or leaving the organization at different levels as flow variables and requirement of human resources at different levels for present and planned projects at different levels, ratio oi’ human resources from cadre to cadre (cadre ratio), attrition rate and hiring to promotion ratio for different levels are considered as Converter variables. Tbe steady solution for the human resource planning problem has been obtained by running trials on the constructed model. The initial inputs to the Model were tbe values depicted in the base case scenario table ! as above.

The requirements for the man power were assumed to be normally distributed, with means & standard deviations. In order to study the influence of exogenous variables on the stock levels at various cadres, the model was perturbed from the equilibrium position and investigated. In tbe first set of experiments. , on the variation of cadres. , the effects were studying using the attrition rate as the factor of perturbance. Tbe output variations as a result of the influence varying of attrition percentages at various levels were studied. The system responses to the variation have been captured as tbe graphical output in figure.

The recommended hiring levels and promotion levels were available as a ready recokner. , 2009. , ‘UaC. 2. 40 ‘BITS. Tdani System Dynamic f^odeling of Human Resource Planning for a typical IT organization In the second set of experiments, the assumption that the demand for platined projects as being normally distributed was relaxed and the demand was assumed to be a non repeatable noniial distribution. The output is displayed as an excel sheet and provides the values for hiring and promotion levels as a ready reckoner for the human resource planner. 6. Discussion of the results

The Model constnicted for a typical IT organization with 5 Cadre levels, was simulated wilh the initial values in the Base case scenario. The stocks of the model in terms of the employees at level i at a given period was assumed reasonably. The Jhw variables were defined based on the processes of hiring, promotion. quitting through voluntary separai ? on and resignaiion. The converters variables arise from the demand for projects at different levels. The demand seenario for the human resource considering the present projects and planned projects were created, using typical values for the converter variables.

The control variables were denned . to stipulate the cadre structure in the typical organization expressed as ratio of stocks at various levels with reference to other levels. The attrition rates at different levels were assumed based on typical percentage values observed as a general trend in the IT industry. The liiiing to promotion ratio was considered as the converters in the model. The results of the model, for the input data set as depicted in the scenario I, were obtained graphically aner the simulation run. The graph (a) indicates the linear growth of the stocks, at different levels on a quarter to quarter basis.

The model output in (b) gives the number of hires at different levels, considering the dynamics of the human resource. The pattern of hiring in a quarter to quarter can be desired as policy variable using this output. Similarly, the output figure 3 give the policy input for HR planners, to decide on the numbers to be promoted. This is based on the dynamics of flow of the humati resource stocks from level i to level/ The figure 4 indicates the outflow of stocks in terms of number of quitting on a quarter to quarter periods.

Tliis grapliical pattern at levels 1 and level 2 show an increasing trend, as would be expected in a typical IT organization. This is to be expected as the attrition rates are higher at lower cadres. This corroborates the practicalities in the Industries. The attrition rates being lower at the higher grades, the figure 4 indicates constant slope. This again corroborates the evidences as seen in practice in tlie typical IT organizations. The graph patterns for stocks, hires, promoted and leaves of human resource were identical for all tbe sets of input data.

The experiments were repealed for trial runs, for input condition of scenario 2 (The assumed values of variables given in table 2 : Attrition Rates Assumed With Respect To Base Case Scenario) The results for Scenario 1 and scenario 2 for all the runs are tabulated as an excel output sheet. The excel output sheet shown. , gives the stocks and fiows at the end of the simulation run. The graphical output and the excel output sheets will be of great help to the decision makers in studying the effect of the dynamics of the human resource flow in the organization.

The stock status of the human resources, at equilibrium is depicted in the output graphs. Tle results of the study will be useful for the HR plamiers in framing the strategic human resource plan. The system dynamics model once constructed will help the planners in repeatedly studying the effects of supply and demand uncertainties on the organizational human resources. 7. Conclusion and Scope for future work This paper has demonstrated the capability of the System Dynamics modeling framework to model the human resource planning process. The System dynamic model developed in this case study can help any medium IT 2009. ‘Uai 2,3O). 3 41 , ‘PUani System Dynamic Modeling of Human Resource Planning for a typical IT organization organization in the dynamic human resource planning, in response to Supply and demand uncertainties. Further, simulation runs will be carried out using different sets of data. The Model developed in this study, will be improved by including variables from the operational tTianagement or the project management side, System Dynamic approach has been adapted for the sake of understanding the general supply / demand behavior in a typical information technology organization and its structural issues.

Future research will therefore will be in the direction of adding precision to the model and expand the inputs to the model by considering demand and supply information based on domain technologies and business knowledge segmentation. Annexure I: Excel Output Sheet for scenario 1 i s i i i B tnuliiitSMl c u ! E 1 F G i H t J l K 1 L 1 M 1 K 1 0 1 p U fl S I 1 luHlnHOLI H Kl O 1 1 m tOBS imz M0U1 H0P12 nu NO Kl tt 1 6 100 NOPS 4IOt3 tt 11 G E| 6 HO« NOCQ B B B K)P3I NOU 1 2 2 2 3 ^ 3 3 3 K K H I IMOOI i 3 2i 0 0 0 0 NONS M t OS 1 t 1 1 29 ? 9SI K 7 3 2 a 9 1 3 s 1» w s ; 1 Mm V » R e 11 1 H O )MlitoSK2 1 13 U QuHimNOLI KHI NUOI NOra HOU N0H2 H0Q2~^ Nora NDL3 NOtfi K i ? N0P3I Wt;l NOM SB X 16 t: 10! 09 m » C It 3EI 17 21 SW 9! 6: Si 9 1 3 » S 1 3 V S a S 82 < 1 3. S fl « 4 7 IB 5 n 5 it: K t e tB’ K 77 6 2 3 « S P 49 11 288. IG SB m S s 1) t2′ 2n as n a NOM HOQG tB 121 1 0 1 H 1 Oi 1. 0 IE U 1 H i J i ] i m m m n. m u s : MQP4S N0t5 HOK HOOS 1 ID. 1 B; 1 t; B 1 t’ 0 1 tt H 1 tl ! ! I I [ l 6 1 2? bieiiiS«3 : 11 1 m » IUMMINOLI nom NOOI MPI? lOU lOtO 1 10 Q3 M0P23 H0t3 Nois N C mm MOU W H I OQ 1 a , 9 Z 6 1 3 3t 2: HW ff 37 10 30. 1! B 9 1 2 on 74 3 B S 3r3 1 3 7 S « s’ 1 3 1 3 7 i ivD S S n: m u S 9 1 3 1 ; ; 7 SI n SI ( I S V 41 1 21 & 1 3 X : 3 B 1 SI 31 31 WHT^HOOI Nora »12 ‘mtQ NOtJ aDIfl «O? ” IH0rari(UU”1I0Hl” «Tu 3t QutfM 1 91 a ^ S 0 3 », 2i : 74 2 9 ]B U X s B 3 a 2 i G Si 7 ! 1 i 71 5 B 3 3Z : 31 me 7 1 X. 9 as 1! SI 5; 77 s t 3 IL 13 11 72 7 i as M m mu now1wts 10 HO« 1HOOG l 0 0 s 9 t 1 1 1 12 1ti H 1 1 t t B I t ( s n i»u “ID «”Mis ” 1 IB~ NOCG ^ U 0 t t 1 1 1 1 B t: 1 4 G I IB 1 1 1 B 0 t o B tn o w m . m 1 a n i m : i • s a T 1 a 1 , ‘VaC. 2, 42 System Dynamic Modeling of Human Resource Planning for a typical IT organization

Annexure II : Excel Output Sheet for scenario 2 1 3 4 OM M I nu NGH1 WO » P U NOU » H INOS MOra HOU INOH fi 1 no a J ^ 1 3 mi 1 1 X ff! II 6 H n: s / i SI E II in Si •» ns n •L 1 4 or «G II X 3 S; 0. n: s S IIX 1 3 e 77 an n 5 B c 0 i E ^ F 6 M 1 K L K N 0 P — 0 fl S T ‘)”•. MdB ! « 6 1 ; 3 i MPM «OU _J(OH( KO (M NOM 3 jl Nm o 1 1 1 1 s’ X t, ]. a 3 3 3 al »1 J 3 – KQI 0 0 Q Q .MOK NOIE % t 1 tl M; 1 (1 1 0 tl 1 0 i « taA h 1 1 3 i – -iNoa 1 7 17 1 1 t1 1 3 1 ^ a DtMMllOU H m ID3 Nom «oei KiPt] MO 13 9S K KB un nos t? na s li M E i: t t zc 3B 2« mm Hou «OM mm «OPM mu i V 6, 57. U 71 —L_ 1 1; 3 • • iWMS 0 0 1 1 1 1 »15 ” T N O W m no. as a n u s, »i fi ( ? 7r 1 i 3 3 a X 7 31 31 3 3 3 m! i; ? , 9 m« 1 1 1 1 0 a 3 a a 0 a i ta 1 Si » Ilk Mal N O U [MO» UG 9) S U 0 B il 1 3 1S 14 17′ 17 M UnntnMLt 2 1 Km it m N ? Noro wi3_ w w o 11 11 U s SE ,NOffi S S i NOW NO« J 3 B 9 S NDPtf NOUS 1 1 y I 10′ 13 11 M’ 1» HOW NOS T 1 1 1 0 0 a ? il -i S m «r n nz k. ! Nom Noo • ” ” »; ” s s- a 731 n V a s 1 n I? Rn«ib ta4 33 II0P12 HOU l 11 SI 311; «te V 1 9 H 1« 1 1 3 4 1 J 1 fNOW ilOOt HO PC 3r i m nrz nu ItE NBI W J wa II II 1] « P B | I O a IN0K3 1 f 1 SI. n wca 4 S i B 1 3 WPH NOU 3 21 31 «lis 1 a s s Si n u D t •1 7^ a 2, 3 It’ mw 1 1 1 1 mor 0 0 D 3|| 31′ “f a ir 0 D “i T ; J I 1 1—1*1 il «1 _i Q; Ml 0 / ttM !«• i! « m i Graphical output of scenario 1 (Trial 1) Graphical output of scenario 2 (Trial 1) Cll^I’E, ZQ09, 43 ‘BITS. Ti System Dynamic Modelinp of Human Resource Planning for a typical IT organization ^ V tel J. i um I 5 sw r Graphical output of scenario 2 (Trial 2) maa. m*’ Graphical output ot scenario 1 (Trial 2) Acknowledgements Authors would like to take the opportunity to thank the Human Resource Professionals from leading teclinology companies, who have supported the authors in providing the necessary data and inputs for building up of the System Dynamic Modeling for the research work.

The data or the input was obtained by authors by personally interviewing them visiting some of the HR professionals of leading technology companies and the data consolidated to obtain the inputs for models. the and few was the Model in Slovenian Armed Forees, www. systemdynainics. org/ conferences/2007/proceed/index. htm, date of Browsing 15. 9. 2008. 2. Bryant, D. T. , and R. J. Niehaus, (1978). Manpower planning and organization design. New York: Plenum Press. Coyle, R. G. (1996). System dynamics modeling: a practical approach. London: Chapman & HalL ppI02-I07. David Dellacca , Connie Justice. (2007). Building Tomorrow’s Information Assurance Workforce through Experiential Learning”, Proceedings of the 40th Annual Hawaii International Conference on System Sciences, p. 27Ic, January 03-06, 2007. Ermina Topintzi and Manos Nistazakis. (2004). “‘Using System Dynamics to Analyze Human Resources Management Problem”, Centre for Systems and Modelling – School of Engineering and Mathematical sciences, City University, 3. 4. The Authors would also take this opportunity to thank all the HR professionals for spending their valuable time atid providing the necessary data required for the research. 5. References: . Andrej Skraba, Miroljub Kljajic. Andrej Knaflic. Davorin Kofjac and Iztok Podbregar. (2007). Development of Human Resource Transition Simulation . VoL 2, Ko- 3 44 System Dynamic Modeling of Human Resource Planning for a typical IT organization Nortliarapton Square. , London EClV OHB, page 11. 6. 7. Forrester, J. W. {1961 ). Industrial dynamics. Cambridge, MA: MIT Press. Hafeez, K. , and H. Abdelmeguid. (2003). “Dynamics of human resources and knowledge management”. Journal of the Operation Research Society 54: 153–164. Haxmeier, P. ( 1991). “Linear programming for optimization of nurse scheduling”.

Computers in Nursing 9: 149-151. John D Sterman. (2000). Business Dynamics: Systems thinking and Modelling for a complex world http://web. mit. edu/jstemian/w’w/SDG/pr oiect. pdf. date of browsing: 11. 09. 2008. 12. Kevin Y. K. Ng, A. Ghanmi, M. N. Lam, and R. E. Mitchell. (2002). “Workforce configuration and work flow analysis of an information technology organization: a queueing network approach”, IEEE transactions on systems, Man and Cybernetics – part A Systems and Humans 32 (6) : 724-732. 13. Malone. T. W (2004). “Bringing the market inside”. Harvard Business Review April: 107-114. 14.

Rodrigues, Lewlyii with Morvin Martis and G. Krishnamurthy. (2005), Modeling Engineering Competence Pool: System Dynamics Based Implications for KM & HRM Integration www. svstemdvnamics. org/confereDces/20 05/proceed/prpc_ee_d.. pdf Date of Browsing : 18. 9. 2008. 15. Ward, D. , T. P. Bechet and R Tripp. (1994), Human resource forecasting and modeling. New York: The Human Resource Planning Society. 8. 9. 10. Jun Wang. A Review of Operations Research Applications in Workforce Planning and Potential Modeling of Military Training, www. dsto. defence. iiov. aii/publications/435 4/DSTQ-TR-1688. pdf. ate of browsing: 10. 09. 2008. U. Jessop, W. N. (1966). Manpower planning: operational research and personnel research. New York: American Elsevier publishing company, ine. www. dsto. defcnce. gov. au/publications/435 4/DSTQ-TR-1688. pdf. date of browsing : 11. 09. 2008. CLLK/1, 2009, .3 45 . ‘PUani Copyright of CURIE Journal is the property of BITS (Birla Institute of Technology & Science) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email articles for individual use.

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