HUMAN RESOURCE PLANNING AND ENGAGEMENT DECISION SUPPORT THROUGH ANALYTICS

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1 HUMAN RESOURCE PLANNING AND ENGAGEMENT DECISION SUPPORT THROUGH ANALYTICS Janaki Sivasankaran 1, B Thilaka 2 1,2 Department of Applied Mathematics, Sri Venkateswara College of Engineering, (India) ABSTRACT In human resource intensive industry sectors, data relating to human resource captured and maintained by organizations could be effectively utilized for decision support in planning and management. There are observable patterns in the growth of skill categories over time. This paper proposes an approach to estimate human resource requirements considering the growth patterns of skill categories over time. The approach would ensure a better match of supply and demand for human resources. Decision Bands facilitate control, engagement and management of human resources. The attributes of employees that impact the membership in a Decision Band has been proposed and the Naïve Bayesian Classifier approach is adopted to classify and place employees in Decision Bands. Also, the sojourn time distribution in each of the decision bands is derived under specific assumptions. Keywords: Human Resource Planning, Decision Bands, Naïve Bayesian Classifier, Skill Category Growth Patterns, Sojourn Time. I. INTRODUCTION Traditionally, the focus of human resource planning has been in matching the count of human resource requirements with the available skill pool acquired through recruitment and internal promotion. Recently, there is an observable shift in the approach to Human Resource planning towards Human Capital planning. In Human Capital planning, the focus is on capability acquisition, engagement and development. An approach to human resource planning through a capability focus is to facilitate a better skill match to human resource requirements. Also, there is a need to estimate the average duration of stay of resources in each of the grades. The human resource acquired through recruitment would need to be placed in grades in a hierarchical organization. In order to enable an objective basis for the placement of human resources in hierarchical grade structures, there is a need for decision support systems. Objective placement of human resources into grade structures is a step to enhance human resource engagement. Research studies in Work Force planning have introduced the concept of Capability Planning. A capability based Work Force planning is introduced by Todor Tagarev et al., [1] wherein, an optimization problem seeking a balance among four variables, Objectives, Strategy, Means and Environmental risks in Work Force planning has been proposed. The proposed approach to human resource planning is through a focus on Work Force 5 P a g e

2 capabilities. The shift from Human Resource planning to Human Capital planning is emphasized by Fitz-Enz [2]. The aspects of Human resource capability, engagement and development planning are put forth in the discussion. In human resource planning, there is a need to work with homogeneous subgroups that demonstrate homogeneity with respect to internal transition and wastage probabilities. Bartholomew et al., [3] have observed the possibility of heterogeneity in a human resource system and the need to divide it into homogeneous subsystems. Classical Markov manpower models necessitate detailed subdivision of the employee population. A challenge for statistical estimation of the parameters in this approach could be posed by non-availability of adequate data points. Several other approaches have been proposed to divide the heterogeneous population into homogeneous subgroups to overcome this challenge. Guerry, M-A [4] has proposed an approach of profilebased homogeneous subgroups of human resources. A recursive binary sub-division algorithm generates the homogeneous subgroups. The wastage and internal transition probabilities are then estimated. Quinlan J R [5] summarizes the synthesizing of decision trees used in a variety of real world systems and describes ID3. De Feyter [6] has described a splitting procedure based on multinomial logistic regression, cluster analysis and classification based on algorithms C4.5, CTree or CART. Guerry, M-A [7] has proposed the use of EMalgorithm to address latent sources of heterogeneity. The heterogeneity is addressed under observable and hidden sources. Human resources with different types and levels of skills are placed in Decision Bands for management and control. The Decision Band Method (DBM) discussed by Ryerson University [8] classifies employee roles into one of six different Decision Bands based on the characteristics of the decision that an employee s role in the organization necessitates. While the work of De Feyter aims at a splitting procedure to derive homogeneous groups, this paper assumes homogeneity in the Decision bands. Further, Bartholomew et al., [3] have derived the sojourn time in a K-grade manpower system. A semi-markov model assumption has been used to derive the length of stay in a grade, conditional on eventually making the transition to other grades. The objective of this paper is an exploration into the use of quantitative techniques for decision support in human resource management. The first section of the paper proposes an approach to estimate the human resource requirements recognizing the growth pattern of skill categories over time. The second section of the paper describes an approach for classification and placement of human resources in Decision Bands using Naïve Bayesian Classifiers. The third section of the paper proposes an approach to derive the distribution of sojourn time in a Decision Band under certain assumptions. II. ESTIMATION OF THE HUMAN RESOURCE CAPABILITY POOL 2.1 Terminologies and Notations In human resource intensive industry sectors with projects as work units, the patterns of skill category size over time are observable. At the organizational level, human resource estimates can be computed taking into consideration the projects, human resource capabilities required to execute the projects and the skill categories. In a typical Information Technology (IT) Service organization focused on Software development and maintenance, the skills are labeled as Programmers, Database architects, Designers, Business analysts, Quality 6 P a g e

3 specialists, Project managers, Program directors and so on. A project is the smallest unit of accountability from a business point of view. In a given time duration, there would be projects in varying stages of completion. Let P 1, P 2, P 3 P k be the projects assumed to be executed during the period t. Let C 1, C 2. C n be the capabilities that an organization holds for the execution of the projects. We assume that these capabilities are mutually exclusive. The skill vector for project P j during a period t is where represents the number of units of the skill C i in project P j where i = 1, 2, 3 n and j = 1, 2, 3.k. The skill matrix of projects P 1, P 2, P 3.P k assuming that there is no change in the number of units of a skill during the period t, is given by: The number of human resources in the skill category C i is given by summing the count in each of the projects P j j = 1, 2, 3.k. We have: (1) Let represent the total number of employees during the period t. We have: and (2) The growth pattern of will depend on the skill category C i. 2.2 Growth Patterns of Skill Categories and Estimates for the Capability Pool Typically, senior managerial skill category size is almost a constant independent of the time duration. Junior management, Database experts and Business Analysts categories have a linear growth pattern. Technical and programming skills have exponential growth that has been observed during a business boom time. It has also been observed that some of the programming skills have a decreasing demand trend and would have a gradual linear decreasing function. Representing the skill size growth patterns mathematically, we have for a: a) Constant skill count category for t = 2, 3 b) Linear growth skill count category for t = 2, 3, and is a constant depending on i. c) Exponential growth skill count category for t = 2, 3, and is a function of. Grouping C 1, C 2. C n skill categories into the respective growth pattern buckets, we can compute the estimate for N(t). Let the super script c, l and p represent the constant size skill, linear growth and exponential growth categories. Aggregating the total resources through the skill category grouping we have:. (3) For the initial period, the count for i = 1, 2 n are known. The estimates of k i and can be computed from the human resource database over a period of time. In the IT Service sector, a period of three years is considered stable with respect to the impacts of the external environment. The external environment impacts could be typically due to taxation policies, human resource cost structures, emerging countries for specific skill 7 P a g e

4 availability and so on. The above equation can be used to estimate the human resource pool based on the skill categories. In practice, all of the skill category patterns may not be present at the same time period. 2.3 Illustration An illustration of the growth patterns in skill categories during a three year period and estimates for the skill category size for period 2 and 3 are shown in Table 1. The skill category A follows the exponential growth pattern while categories B and C follow a linear growth pattern. The size of skill category D has remained a constant over the three-year period. The values of k 1, k 2, k 3 computed from the data are 0.4, 1.52 and 2.14 respectively. The values for f(1) and f(2) are 0.1 and 0.2 respectively. Table 1: Size of Skill Categories Size of Skill Categories Period A B C D Total f(t-1)=(t-1)/ III. Classification of Human Resources into Decision Bands 3.1 Decision Band The previous section has proposed an approach to estimate the size of the total skill pool through the skill category growth pattern route. The available skill pool can then be placed in Decision Bands for human resource management and control. Decision Bands are a form of hierarchical structures. A Decision Band can be considered to be homogeneous with respect to transition and wastage probabilities. The Decision Bands and the prime focus proposed by Ryerson University [8] are: Band F Policy Making Decisions Band E Programming Decisions Band D Interpretive Decisions Band C Process Decisions Band B Operational Decisions Band A Defined Decisions 3.2 Naïve Bayesian Classifier Bayesian classifiers are statistical classifiers based on Bayes theorem. Bayes theorem: Let A 1, A 2... A n be a set of mutually exclusive and collectively exhaustive events of the sample space S. Let B be any event such that P(B) > 0. Then, 8 P a g e

5 P( A k ) P( B A k ) P( A k B ) = P( A 1 ) P( B A 1 ) + P( A 2 ) P( B A 2 ) P( A n ) P( B A n ) Naive Bayesian Classifiers are used to predict class membership probabilities. If, i = 1, 2 k are the k classes, the vector (encompassing the set of employee characteristics belonging to a decision band) would belong to class when the probability ) is the maximum. Naïve Bayesian classifiers assume that the effect of an attribute value on the membership class is independent of the values of other attributes. Jiawei Han and Micheline Kamber [9] have dealt with Naive Bayesian classifiers extensively. In this paper, the Naïve Bayesian Classifier algorithm is used to describe the impact of employee characteristics (attributes) on the membership into a Decision Band. The algorithm can also be used to predict the Decision Band of new recruits using the set of attribute values that form the employee characteristics. The set of attributes that impact the membership into a Decision Band can be chosen to be independent. The attributes can also be chosen to address the sources of hidden heterogeneity in human resource systems. 3.3 Model Description Decision Bands are divided into grades and sub-grades in practice. For the purpose of this study, we restrict the division of a heterogeneous employee population to the Decision Band stage. We assume that there are k distinct decision Bands of employees in an organization. The human resource system in a Technology Service sector organization is characterized by the following: 1. There are two broad categories of employees: Technology and Techno-managerial group (T), and Support staff (S) that include general facility administration, human resource management, systems and technical infrastructure management, purchase, finance and accounting. 2. The controls over proportion of staff at various Bands/grades/levels operate within T and S through recruitment and promotion. 3. The structure and proportion of employees in the Band/grades/levels are different in the categories T and S. 4. The internal transition is always to the next Band/grade/level. 5. The opportunities for recruitment and wastage are prominent in category T and not in category S. The system S is generally stable with stocks and transition probabilities that can be considered deterministic. The system T experiences volatility of employees and a stochastic treatment would be realistic. The illustration in this paper is from a Technology group T. Human resource management practices enable the identification of employee attributes that typically impact a Decision Band placement. The Decision Band characteristics include age, academic qualifications, job skills, relevant work experience, decision capability required for a role and importance of the employee in the organization. The human resource database of an organization would reveal patterns of characteristics in Decision Bands. 3.4 Illustration An illustration from the Information Technology Service sector is presented. The identified attributes are: 1. Gender (X 1 ) 2. Age (X 2 ) 9 P a g e

6 3. Education (X 3 ) 4. Relevant work experience (X 4 ) 5. Perceived skill level of the employee in the Band/Grade/Level (X 5 ) 6. Perceived importance of the employee role in the Organization (X 6 ). The attributes have been considered in a set of 241 employees from an IT Services organization. The Band F described in the Decision Band method has one entity in the organization and therefore has not been included in the analysis. Band A to E are represented in the set of data used for the analysis. All the attributes (predictors) characterizing an employee in a Decision Band have been discretized. Table 2 summarizes the approach for the discretization of attributes. The response variable is the Decision Band. The attributes have been assumed to address the class conditional independence criteria of the Naïve Bayesian Classifier. The R Statistical tool has been used to support the statistical analysis. Table 2: Attributes and the Categories X i Attributes Attribute Values Attribute Categories X 1 Gender M / F M: Male F: Female X 2 Age A to F A: B: C: D: E: F: > 45 X 3 Formal Educational A/B/C A: BSc, BCA Qualification B: BE, B Tech, MCA, MSc C: M Tech, MBA, MS X 4 Relevant work experience A to F A: <= 2 years B: 3-5 years C: 6-10 years D: years E: years F: > 20 X i Attributes Attribute Values Attribute Categories X 5 X 6 Perceived skill relevance in the Band/Grade/Level L/M/H L: Low M: Medium H: High Perceived importance of the A to F A: easily replaceable employee role in the B: replaceable organization C: important in the current assignment D: important in the organization 10 P a g e

7 E: very important in the organization F: requires succession planning 3.5 Naive Bayesian Classification Results The results of the Naïve Bayesian Classification algorithm on the set of 241 employee data are presented in Table 3 and 4. The prediction accuracy considering only the true positive classification, demonstrates the effectiveness of the Bayesian Classifier for dividing the employee population into Decision Bands. The a-priori and conditional probabilities are shown in Table 5 and Table 6 through Table 11 respectively. These probabilities can be used to predict the Decision Band of new recruits, using the set of attribute values that form the employee characteristics. Table 3: Classification Matrix Observed Predicted Band Band --> A B C D E A B C D E Table 4: Prediction Accuracy Band Percentage A 91 B 72 C 63 D 66 E 100 Table 5: A-Priori Probabilities A B C D E P a g e

8 Table 6: Gender Category (X 1 ) F M A B C D E Table 7: Age Category (X 2 ) A B C D E F A B C D E Table 8: Education Category (X 3 ) A B C A B C D E Table 9: Relevant Work Experience Category (X 4 ) A B C D E F A B C D E Table 10: Perceived Skill Level Category (X 5 ) H L M A B C D E P a g e

9 Table 11: Perceived Employee Role Importance in the Organization (X 6 ) A B C D E F A B C D E IV. SOJOURN TIME IN DECISION BANDS We assume that there are k distinct Decision Bands (S 1, S 2 S k ) of employees in an organization. The probability of promotion (internal transition) to the next band and the attrition (wastage) probability are time dependent and considered to be homogeneous for each Decision Band. The transition and wastage probabilities can be estimated for each of the decision bands from the employee database maintained by an organization. Employees stay in Decision Bands for a certain period of time before they move either to the next higher Decision Band or leave the organization. A comparison of the time spent in a Decision Band against an organization s average time in a Decision Band would support decision making during the promotion process. Let (t) denote the employee recruitment rate at at time t and t) denote the number of employees in at time t. A period of one year is considered to represent a unit time period. Let the vector N(t) = [,,, ] represent the state of the system at time t. Let represent the observed number of employees in Decision Band at the start of the period t. The observed number of employees who transition from to is denoted by. The estimated transition probability from to is given by. The estimated wastage probability from is given by. At the decision band, we have three distinct flows: recruitment, internal promotion to the next decision band and wastage. Beginning until, we have four flows: recruitment from outside the system, internal promotion from the immediate prior band, internal promotion to the immediate next band and wastage. At, we have recruitment flows, internal promotion from the immediate prior band and wastage. We assume that there would be no transition from a higher band to the immediate lower band and transitions that skip bands. We discuss the departure rate from the Decision Band in the following two cases. Case 1: Let the sojourn time at be independent of the recruitment rate at. The sojourn time of an employee in is a function of the internal transition rate and the wastage rate. Let be the rate of departure from to, j = i+1, k+1. We have + as the instantaneous rate of departure from at time t given survival in until t. Case 2: Let the sojourn time at be dependent on the recruitment rate at. This takes place whenever a forced attrition decision is made in the organization. The underlying thought is that additions to the resource pool from the outside environment impact the time of wastage and hence the wastage rate. In this case we have, + * as the instantaneous rate of departure from at time t given survival in until t, where 13 P a g e

10 division of case 1. * = +. This case can be treated as a granular 4.1 Density Function of the Sojourn Time Let the sojourn time in, conditional on making the transition to or have a distribution function and the probability density function, j = i+1, k+1. The density function of the sojourn time [3] is given by:. (4) When for all t, is the density function of the length of stay in prior to transition to where. The sojourn time at is independent of the destination. 4.2 Illustration The sojourn time distribution for the illustration data are shown in Table 12. The sojourn time at is conditional on the transition to. For the illustration data at Decision Bands D and E, since the departure rates are zero, the sojourn time in the Decision Bands is the sum of the time periods. Table 12: Sojourn Time Distribution in Decision Bands Decision Band A B C D E Total N(t-1) Recruitment Attrition Transition 6 N(t) Attrition probability Transition probability Rate of Departure Sojourn time distribution 28e -28t e -t 3e -3t - - V. CONCLUSION The approach presented in the first section is a refinement to approaches that estimate human resource requirements without recognizing skill categories and their growth patterns over time. The refined approach would facilitate a better skill match between human resource demand and supply in organizations. The estimates of sojourn time support human resource planning. The average length of stay in a Decision Band enables an estimate of the number of employees in a Decision Band at specific periods of time. The paper also illustrates a method to classify human resources into Decision Bands and an approach to place newly recruited employees into Decision Bands. This approach could ensure a better placement and therefore a better engagement of employees in an organization. A Decision Band can be considered homogeneous from the perspective of the promotion probability to the next Band and the attrition (wastage) probability. The transition probabilities, the wastage probabilities and the departure rates can be computed for each of the Decision Bands from the employee database of an organization. 14 P a g e

11 REFERENCES [1]. Todor. Tagarev, Tsvetomir. Tsachev, Nikolay. Zhivkov, Formalizing the optimization problem in long term capability planning, Information & Security, An International Journal. 23(1), [2]. Fitz-Enz. Jac, The New HR Analytics: Predicting the economic value of your company s Human capital investments (AMACOM Div American Management Association 2010). [3]. Bartholomew. D J, Forbes. A F, McLean. S I, Statistical Techniques for Manpower Planning (Chichester: Wiley 1991). [4]. Guerry. M-A, Profile based push models in manpower planning, Applied Stochastic Models in Business and Industry. 24(1), 2008, [5]. Quinlan. J R, Induction of decision trees, Machine Learning, Kluwer Academic publishers, Boston: 1, 1986, [6]. De Feyter. T, Modelling heterogeneity in manpower planning: dividing the personnel system into more homogeneous subgroups, Applied Stochastic Models in Business and Industry, 22(4), 2008, [7]. Guerry. M-A, Hidden heterogeneity in manpower systems: A Markov switching model approach, European Journal of Operational Research, 210, 2011, [8]. Ryerson University, Decision Band Method Overview and a Guide to completing the Position analysis questionnaire, f. [9]. Jiawei. Han, Micheline. Kamber, Data Mining: Concepts and Techniques (Second Edition, Elsevier 2006). 15 P a g e