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Planning human resource requirements to meet target

customer service levels Khalid Hafeez

Hamdan Bin Mohamed e-University, Dubai, United Arab Emirates, and

Izidean Aburawi Higher Institute of Industrial Technology, Tripoli, Libya

Abstract

Purpose – Effective human resource planning allows management to recruit, develop and deploy the right people at the right place at the right time, to meet organizational internal and external service level commitments. Firms are constantly looking out for strategies to cope with skill shortages that are particularly acute in the “knowledge intense” industries due to high staff turnover. The purpose of this paper is to describe how system dynamics modeling allows management to plan to hire and develop right level of skills and competencies in the organizational inventory to meet desired service level targets.

Design/methodology/approach – An integrated system dynamics framework is used to develop various feedbacks and feed forward paths in the context of competence planning and development. The model is mapped onto an overseas process industry company’s recruitment and attrition situations and tested using real data.

Findings – Strategies for human resource planning are developed by conducting time-based dynamic analysis. Optimum design guidelines are provided to reduce the unwanted scenario of competence surplus and/or shortage, and therefore, to reduce disparity in between service level needs and availability of right competencies.

Research limitations/implications – System dynamics type of modeling is usually suited for medium to long range timescale (two to five years scenarios). There is a need for the model to be tested in a high turnover industry such as IT to test its efficacy in short-term time scale, where shortage in required talent is more acute. Also this model is tested for measuring the generic skill-sets in here. There is a need to test the model for a mixture of generic and specialized skills-set in a specific business operation.

Practical implications – The authors anticipate that system dynamics modeling would help the decision makers and HR professionals to devise medium to long-term human resource planning strategies to anticipate and meet the service level expectations from the internal and external customers.

Social implications – Such planning exercise will avoid the situation of customer dissatisfaction due to right competence shortages. Also this will reduce the staff surplus scenario that usually leads to knee-jerk reaction to lay-off unwanted skills, which is usually a costly exercise and impacts negatively on staff morale.

Originality/value – Use of the systems dynamics model introduced here is a novel way to analyze human resource planning function to meet the target service level demands. The idea that an organization can estimate the service level requirements for medium to long-term situations, and conduct what-if scenarios in a dynamic sense, can provide valuable information in strategic planning purposes.

Keywords Human resource management, Customer service management, Recruitment, Competences, Skills, Human resource planning, Strategic planning, System dynamics, Scenario planning

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1756-669X.htm

International Journal of Quality and Service Sciences Vol. 5 No. 2, 2013 pp. 230-252 q Emerald Group Publishing Limited 1756-669X DOI 10.1108/IJQSS-04-2013-0020

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1. Introduction Human resource planning (HRP) needs to respond to a greater demand for new talent due to increased competition in the knowledge economy. In much earlier studies Walker (1974) had suggested that through HRP, management is able to develop and deploy the right people at the right places at the right times to fulfill both organizational and individual objectives. Firms are constantly looking out for strategies that will help them to cope with competition and diversification through building a linkage between HRP and the corporation’s long-term business objectives. Most organizations feel the need to predict future human resource levels in order to forecast recruitment and training needs to ensure that sufficient experienced people are rising through the rank to fill vacancies at higher levels (Brian and Cain, 1996).

The dynamics of market forces and job opportunities is becoming a challenge for many organizations to retain their core staff. Companies are losing critical business knowledge as employees walk out from their doors. Also, the recent transitions from the industrial market to the knowledge economy dictate an immediate and wholesale retraining scenario for many organizations to remain at the cutting edge of technology. An efficient human resource or intellectual capital investment strategy demands a good understanding of the dynamics of recruitment and training issues.

The link in between strategy and human resource management functions has been emphasized by many academics and practitioners (Bird and Beechler, 1995; Boxall and Purcell, 2003). There are studies those establish a direct link in between human resource strategies and organization performance (Tyson, 1997; Huang, 1997; Chandler and McEvoy, 2000; Huselid et al., 1997). Others provide insight of the HRM practices in the entrepreneurial and SMEs (Heneman et al., 2000; Kok and Uhlaner, 2001; Bacon and Hoque, 2005; Dabic et al., 2011). Huselid and Becker (2010) analyse the strategic HRM micro and macro perspective for a diverse workforce.

In order to attain sustainable competitive advantage companies needs to manage their organizational competences (Hafeez et al., 2002a, b; Hafeez et al., 2010), as well individual competencies (Hafeez and Essmail, 2007). MNEs are often confronted with the challenge on what HRM policies and practices to adopt in their subsidiaries abroad. There are studies that evaluate HR management in the international perspective in the context of multi-national enterprises (MNEs) (Scullion et al., 2007; Briscoe et al., 2009; De Cieri and Dowling, 2006; Caligiuri and Stroh, 1995). Bartlett and Ghoshal (1998) argue that human resource management (HRM) policies are key mechanisms for co-ordination and control of international operations. The question remains to be answered whether to regulate by adopting parent country practices or to adapt these to fit to the local environment. Adler and Bartholomew (1992) suggest that global business strategies should be informed by HRM practices as they often pose a major barrier whilst operating in multi-country operation. Others have looked into country of origin effects in HRM in MNs (Ferner, 1997; Bae et al., 1998). Gooderham et al. (1999) study the institutional factors that impact the HRM practices in the European firms.

Skill, knowledge and competence, as a measure of improvement, cannot be bought and delivered instantly. These are proving to be the ingredients of attaining competitive advantage in the knowledge society (Hafeez et al., 2002a, b, 2007; Hafeez and Essmail, 2007). It takes a considerable amount of time to develop and support polices and infrastructures to develop such competencies in the organizations. Therefore, the responsibility of human resource functions has many fold increased now not only

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to improve morale and productivity and therefore, help minimize staff turnover. HR is now expected to facilitate companies make effective use of employee skills, provide training opportunities to enhance those competencies, and boost employee satisfaction with their job and working conditions. Training includes employer sponsored efforts to improve the skill and competences of employees through education, work-shadowing, and apprenticeship programs for personal development. On the other hand, HRP concerns forward looking analysis of current and future human resource development needs, issues and challenges facing a particular occupation such as the supply and demand of skilled people (Hafeez and Abdelmeguid, 2003), the impact of changing technology, the need for skill upgrading and the efficiency of the existing training.

Miles and Snow (1984) provided earlier guidelines for designing dynamic human resource management models. There are some recent examples of the use of quantitative models in the expatriations and repatriation area (Black, 1992; Black and Gregersen, 1991; Gomez-Mejia and Balkin, 2007; Suutari and Brewster, 2003). However, this study is about developing a quantitative model in the HRP and management field.

There are other pioneering studies such as Parasuraman et al. (1985) where they have introduced the SERVQUAL model to valuate different aspects of service quality by measuring the gap between customer expectations and experience. The SERVQUAL model is a subjective construct that measures customer perceptions with their expectations. Hafeez et al. (2006) and Hafeez (2007) have devised a tool to measure the gap in financial and non-financial measures while companies are adopting TQM practices. They have extended this work further to beyond one single company to a supply chain perspectives to analyse the gaps in perception and expectations on various, financial, technical and behavioural measures (Hafeez et al., 2010). There is some introductory work by Hafeez and Abdelmeguid (2003) where they have modelled the dynamic behaviour of skills and knowledge in the organization. However, this research address the gap in the literature by providing a tool to improve service level by development appropriate human resource policies to acquire and develop required competencies to reduce such service level gap.

2. Review of models in HRP Models may be descriptive, representing what is, or normative, representing what should be. Models in HRP are both descriptive and normative. The models in this paper are divided into two types:

(1) policy models; and

(2) mathematical and statistical models.

Policy models are both normative and descriptive. Mathematical and statistical models are descriptive.

2.1 Policy models Policy models in HRP aim to provide a comprehensive framework for the evaluation of the organization, that emphasize the interrelationship between programs (e.g. between recruitment and training) and the relationship of each program to external factors. Furthermore, policy models need conceptual models for assessing organization roles in support of HRP development and should be grounded in forecasting to provide clear descriptions of the mechanism.

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Tichy and Devanna model. Tichy and Devanna (1984) are among the few who have attempted to integrate forecast of the demand for skills and forecast of the internal supply of skills, and relate them to HRP, emphasizing the interrelatedness and the coherence of human resource activities. HRP, in their cycle model, consists of four key constituent components (selection, appraisal, development and reward).

Harvard model. The Harvard model recommends that all managers must take greater responsibility for HRP. The Harvard model proposes that many of the diverse personnel and labour relations activities should be taken into account in HRP. The model asks that its manager should ask to what extent the polices they implement will: enhance the commitment of people to their work and the organization; attract, retain, and develop people with the needed competence; sustain congruence (compatibility) between management and employees, and be cost effective in terms of wages, employee turnover, and risk of employee dissatisfactions (Walker, 1992).

Walker model. Walker (1980) explained that most tools being used in HRP do not appear to be as suited as they should be in order to meet the needs of management for proper strategic planning and evaluation of HRP practices. He recommended that human resource researchers examine the validity and predictability of his model to predict and validate HRP practices within large organizations.

Rizzo (1984) found that most companies using Walker’s (1980) model generally fell within the two lowest levels of Walker’s typology. Therefore, Rizzo’s evaluation of Walker’s model indicated that there was a dependency by major corporations on rudimentary forecasting and placement techniques in the HRP process. Walker suggested that effective HRP is a process of analysing an organization’s human resource needs under changing conditions and developing the activities necessary to satisfy these needs.

2.2 Mathematical and statistical models The major concern of mathematical and statistical models in HRP is to investigate system behaviour over time (Georgiou and Tsantas, 2002). These models can be very sophisticated and their use helpful to an organisation. Mathematical models can help decision makers to understand and explore inter-relationships between variables of interest.

Markov model. Most organizations feel the need to predict future human resource levels in order to forecast recruitment and training needs, and to ensure that sufficient experienced people are rising through the ranks to fill vacancies at higher levels. The nature of the problem seems ideally suited to the use of Markov analysis as it clearly involves probabilistic transitions from a set of known initial states.

The Markov HRP model in essence describes the relation between stocks and flow of manpower in the various levels of the organization and seeks to describe their variation over time (Nilakantan and Raghavendra, 2004). These same authors defined Markov analysis as a descriptive technique that falls within the family of mathematical modeling techniques known as stochastic process models. The technique is used to describe the behaviour of a system in a dynamic situation over time and has numerous applications including replacement analysis Raghavendra (1991), HRP, brand loyalty, investment evaluation and stock market analysis. Markov analysis is highly mathematical in nature, being a derivative of probability theory. However, despite this mathematical underpinning, it purports to be part of the practicing manager’s portfolio of techniques. On the other hand, Markov modeling is a mathematical modeling approach used to help solve business problems.

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Spreadsheet modeling is a practical demonstration that modern computing power and associated software packages now enable many business problems to be analyzed. Using spreadsheet techniques, non-mathematical managers can use the armoury of techniques, which were previously the preserve of the “expert”, and apply them to give rich descriptions of their real-life problems (Brian and Cain, 1996).

The use of spreadsheet modeling in the context of HRP shows how decision makers, who may well lack mathematical ability, can now bring to bear the power of mathematical analysis to their subject (Parker and Caine, 1996). Indeed, a rich and flexible set of models can be produced, eliminating many of the limitations inherent within stochastic type models, for example. This often provides the decision maker(s) with greater insights into the problem situation.

Systems dynamics. Forrester (1961) conducted some pioneering work by combining the fields of feedback control theory, computer and management sciences as early as 1961 in order to shape the systems dynamics discipline. System dynamics is a method for developing management “flight simulators” to help us learn about dynamic complexity and understand the sources of resistance to design more effective policies (Sterman, 2000, 2001). The method allows us to study and manage complex feedback systems by creating models representing real world systems. System dynamics is part of management science that deals with the controllability of managed systems over time, usually in the face of external shocks (Sterman, 1994). However, successful intervention in complex dynamic systems requires technical tools and mathematical models. This process is fundamentally interdisciplinary, because it is concerned with the behaviour of the complex system, and is based on the theory of non-linear dynamics and feedback control developed in mathematics and engineering to model inventory and order based production control system (IOBPCS) typical behavior (Coyle, 1996). Systems dynamics is a modeling approach that considers the structural system as a whole, focusing on the dynamic interactions between components as well as behaviour of the system at large.

More recently, tools such as systems thinking have made many gains in soft systems problem structuring as advocated by Senge (1994). In other examples, Morecroft (1999) has used system dynamics to examine the management behavioural resource system to analyse a diversification strategy based on core and non-core business. Winch (1999) has used system dynamics to introduce a skill inventory model to manage the skill management of key staff in times of fundamental change. Coyle etal. (1999) has used system dynamics to manage and control assets and resources in major defence procurement programmes. Warren (1999) defines tangible and intangible resources for system dynamics model development. Hafeez et al. (1996) has used system dynamics modeling to re-engineering a supply chain. Mason-Jones et al. (1995) have extended the work of Hafeez et al. (1996), to show its applicability in an Efficient Consumer Response (ECR) environment by linking it to point of sale inventory triggers. More recently Al-Qatawneh and Hafeez (2012) have used system dynamics model to analyse the logistics supply chain of a UK National Health Service (NHS) provider to optimize the inventory KPI’s.

Hafeez (2003) has developed a skill pool model (SKPM) based on IOBPCS as described by Coyle (1977), to help understand the dynamics of skill acquisition and retention, particularly during times when a company is going through some major change. The model, which is based on system dynamics principles, links with the organization environment to show how new (or improved) skills could enhance organization productivity and innovations. Also, it aims to respond to the future training and learning

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needs, as a result of present skill loss rate, by incorporating a feed forward path. It aims to properly manage the skill pool level and recruitment and training performance by incorporating a goal seeking (feedback) loop.

A literature review indicates that there are a number of models/methodologies are in use for HRP purposes. Some of these models are often highly mathematical, demanding considerable mathematical sophistication for successful implementation. Some models look specifically at the linkage between business planning and HRP strategies. All of the models we have considered address various but different aspects of the HRP process. In summary the literature identify that HRP model should address attributes such as, training and recruitment, forecasting human resource needs, responding to external conditions, human resource flow, promotion, staff turnover, job analysis, human resource development. In addition to the above, it is worth considering further attributes that apply to mathematical models. The fact that a mathematical model can be manipulated to furnish numerical information, for example, is very important in HRP. The additional characteristics of HRP models listed below, that are relevant to mathematical models (and in some cases to policy models), need therefore to be added to the above list when evaluating the effectiveness of any HRP model: ease of understanding and use – modelling of complexity – forward planning feedback. In Table I we have carried out an assessment against the above characteristics of the policy and mathematical models reviewed in this paper together with system dynamics modeling. All we have done, by way of an assessment, is to allocate two stars in the table if the model addresses the characteristic well, one star if the characteristic is addressed, and no stars (a blank) if the characteristic is not addressed. Nowhere is there any assumption that the star ratings are linearly related or additive. However, the more stars a model has the more useful we may claim the model is (as the model addresses more characteristics).

Table I clearly demonstrates the usefulness of the system dynamics type of models which is explored in this paper. In addition, comparison of the system dynamics models (characteristic by characteristic) with other models is also indicate the strengths of this type of modeling in HRP area.

Characteristic

Tichy and Devanna

model Harvard model

Walker model

Markov model

Holonic model

System dynamics modelling

Training and recruitment * * * * * * * * *

Forecasting human resource needs * * * * * * * * * *

External conditions * * * * * * * * * *

Human resource flow * * * * * *

Promotion * * * * * * * * * * *

Staff turnover * * * * * * * * *

Human resource development * * * * * *

Hiring and firing * * * * * * * * *

Complexity * * * * * *

Feedback * * *

Forward plan * * * * * * * *

Ease of understanding and use * * * * * *

Controllability and optimisation * * * *

Notes: * – addressed; ** – addressed very well; blank – not addressed

Table I. Assessment of the HRP

models against the characteristics listed

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3. An integrated system dynamics framework The system dynamics model presented in this paper is constructed by adopting an integrated system dynamics framework developed by Hafeez et al. (1996), which is shown in Figure 1. The framework has been successfully used for the modeling and analysis of a number of supply chains to address the “Bull-Wip” impact arising from demand amplification taking place in a multi-echelon supply chain. Essentially, it consists of two overlapping phases, namely qualitative and quantitative. The quantitative phase is associated with the development and analysis of the simulation model. The main stages involved in the qualitative phase are system input-output analysis, conceptual modeling, and block diagram formulation. The first step towards the quantitative model building is to transform the conceptual model into a block diagram. The simulation model is to be verified by relevant personnel and validated against the field data (Hafeez et al., 1996).

Qualitative system dynamics is based on creating cause and effect diagrams and to create and examine the feedback loop structure of the system using resource flows, represented by level and rate variables and information flows. It provides a qualitative assessment of the relationship between system process and system behaviour and enables the system modeller to postulate strategy design changes to improve behaviour.

Figure 1. Integrated system dynamics framework for supply chain management

Real world supply chain

Business objective

System input - output analysis

Conceptual model

Block diagram formation

Computer simulation techniques

Verification/validation

Dynamic analysis

Structural redesign What if business

scenarios

Fine Tune existing Parameters

Statistical techniques

Control theory techniques

T ec

hn ic

al pr

ob le

m C

on ce

pt ua

l P

ro bl

Q ua

nt it

at iv

e P

ha se

Q ua

li ta

ti ve

P ha

Source: Hafeez et al. (1996)

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System dynamics is centered on the use of diagrams as a medium for transmitting mental models and discussing change (Bowers, 1996). More recently this framework is used to model healthcare industry supplcy inventory behaviour (Al-Qatawneh and Hafeez, 2012).

3.1 System input-output analysis Input-output analysis has been found to be a powerful and comprehensive tool in system analysis and system investigation work. The use of input-output analysis is helpful in building up both a conceptual as well as more concrete, block diagram model (Parnaby, 1979). Once the conceptual model has been produced, the next step of producing the block diagram is made that much easier. The most common use of input-output analysis is to evaluate the impact of exogenous changes in the external components on the interdependent (internal) components. Input-output analysis has most frequently been used in the study of economic systems (Correa and Craft, 1999).

Figure 2 shows the input-output block diagram of the case company’s competence capacity development. The planning methods used by HRP managers were investigated by means of interviewing and observing the managers at work. The philosophy of our approach summarised in the input-output diagram, is divided under three categories: system inputs (design constrains), system inputs (optimization process) and system outputs (recommended policy design settings), as shown in Figure 2.

3.2 Influence diagram representation of service-competence model in Ithink The influence diagram for service-competence model (S-CM) is shown in Figure 3 using the standard Ithink software package, which allows even a non-expert with elementary control theory knowledge to construct an equivalent model to be presented in time- based dynamics (Coyle, 1977). In order to anticipate the competence loss rate (staff leaving) replacement requirements, some kind of averaging is useful. We have used exponential smoothing to average the present competence loss (staff leaving) rate over

Figure 2. Input-output analyses

indicating the sources of company data for the

system analysis process

S Y S T E M

A N A L Y S I S

P R O C E S S

Production change

Training and recruitment

Competence (Staff)

Competence (staff) loss

Company budget

Competence Building human resource planning

Transfer function analysis

Simulation software

Objective function

System inputs (Design constraints)

System inputs (Optimisation

process)

Time to adjust Competence (staff)

gap

Competence loss (Staff leaving) Averaging time

System outputs (Recommendation for policy design)

Competence Building (Recruitment/training)

delay

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time Ta and fed this back to the original competence development (recruitment) rate to reflect the competence loss history in the recruitment planning.

Based on IOBPCS structure as explained earlier (Coyle, 1977), the company competence development rate comprises two parts, one the competence gap (staff deficit) to meet the desired service level, and the other the forecast competence loss (staff leaving) rate. Recruitment rate is therefore effectively controlled via the average time to determine the forecast staff leaving rate (Ta), and the time over which the present staff gap is to be recovered (Ti). The difference between the present staff leaving rate and recruitment rate is accumulated to give the present actual staff level in the pool. Therefore, the model as shown in Figure 3 consists of two parts; feed-forward control based on the forecast staff leaving rate, and feedback control based on the staff gap. In order to analyse the dynamic response of the S-CM, recruitment process delay is represented by a time delay Tr (recruitment lead time) and the time over which staff leaving rate is averaged by Ta.

Towill (1982) suggests using exponential delay for industrial dynamics simulation. We have used the discrete time feed forward and feedback difference equations giving the relationship between the main variables, and these are presented in equations (1)-(5) to explain the relationships in between different variables of the model. Furthermore, it is important to recognize how to manage the actual competence (staff) pool in an organization. To reach the target value, a simple and appropriate policy is proportional control, where information concerning the magnitude of the actual competence (staff) level is fed back to control the recruitment rate. The recruitment demand rate is calculated by dividing the discrepancy between the target level and actual level by a time factor, which represents the average delay in performing the recruitment rate.

In this research, inspired from SKPM as described by Hafeez and Abdelmeguid (2003), we develop a S-CM; and have evaluated it using staff pool data from a large overseas process industry company. The company operates in a relatively stable “push market” with low to moderate staff turnover. Due to lack of opportunities the majority of the workforce considers their work a “job for life”. However, there is a tendency of employing a pool of contract worker requiring manual to specialist’s skills for various projects.

Figure 3. Influence diagram of the S-CM

Competence Development (Recruitment) Rate

Competence Development (Recruitment) Completion Rate

Tl Tp

Competence Gap

Target Competence Level

Predict Competence Loss (Staff Leaving) Rate

Present Competence Loss (staff leaving) Rate

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A block diagram representation of the case company recruitment and training system is shown in Figure 4. In this format the S-C model is developed to improve our understanding of the dynamics of staff turnover and related service quality issues in a company when it is operating in a steady state. Also it allows us to see the impact of going through some major changes leading to enlarging service gaps. This model is implicitly link with the organization environment to develop new policies for hiring and training right competencies and skills to meet the expected service levels. Also it aims to respond to the training and hiring needs as a result of present competence loss (staff leaving) rate (feed forward) as well as actual competence (staff) level and training completion rate (feedback). One main assumption made in this model is that there is one to one match in between availability of right competencies (staff skills) and delivering the service level target. Therefore, the main aim of using system dynamics models is to find the optimum polices to manage company recruitment and training policies effectively in the face of shocks experienced due to changes in its external environment such as if service quality level becomes the competitive advantage or right competencies are in short supply due to shortage in the available talent pool.

It is customary to use abbreviations for the various rates, level, and operations met in planning dynamics simulation. Those used in Figure 4 are defined in Table II.

By employing Figure 4 and Table II abbreviations, equations (1)-(5) outline the main constructs of the S-CM and help to establish feed forward and feedback structures and associated transfer functions:

FCLRðk þ 1Þ ¼ FCLRðkÞ þ aaðPCLRðk þ 1Þ 2 FCLRðkÞ ð1Þ

where aa ¼ 1/(1 þ Ta*S) Laplace Transform equivalent for the first order delay used for averaging:

SGðk þ 1Þ ¼ DSLðk þ 1Þ 2 ASLðk þ 1Þ ð2Þ

CDRðk þ 1Þ ¼ SGðk þ 1Þ

ð Ti þ FCLRðk þ 1ÞÞ ð3Þ

CDCRðk þ 1Þ ¼ CDCRðkÞ þ arðCDRðk þ 1Þ 2 CDCRðkÞÞ ð4Þ

where, ar ¼ 1/(1 þ Tr*S) is again Laplace Transform equivalent for the first order delay used for averaging:

Figure 4. A block diagram

representation of the S-CM

Present Competence Loss Rate (PCLR)

Target Service Level (TSL)

Service Gap (SG) Actual

Service Level (ASL)

Competence Development Completion Rate

(CDCR) +

- +

Competence Demand Rate

(CDR)

Forecast Competence Loss Rate (FCLR)

+ +

1/Ti 1/S

αa

αr

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ASLðk þ 1Þ ¼ ASLðkÞ þ CDCRðk þ 1Þ 2 PCLRðk þ 1Þ ð5Þ

4. The case study The case company is of the medium sized process industry based in the Middle East. Several interviews were conducted with the human resource management to understand their planning operations and underlying policies used to meet the particular competence gaps (skill shortages). The managers’ confirmed that a bulk of their time and management efforts were spent to meet the supply and demand side, i.e. to bridge the gap between target and actual staff (skills) levels to meet their production and service targets. The management welcomed the idea to develop structured systemic procedures to implement good recruitment and training program in the company. Interviewing the

Terms Abbreviations Description

Present competence loss rate

PCLR The units of competence loss (staff leaving rate) is staff unit/month and it is refers to present staff leaving rate

Forecast competence loss rate

FCLR It is the time average of competence loss (staff leaving) rate and it is refers predicts staff leaving rate. The unit’s staff units/month

Desired service level DSL It is the target service level company wish to define on annual basis. The unit of target service level is service unit (or linked to competence units for delivering that particular service)

Service gap SG It is the difference between desired service level and actual staff level. The unit of staff gap is service unit

Competence development rate

CDR It is the demand for competence recruitment rate and it is refers to staff gap. The units of recruitment rate are staff units/month

Demand (competence development) completion rate

CDCR Staff recruitment completion Competence Development rate it is refers to the acquired competence and it is units are staff/month

Actual service level ASL It is the target service which company needs to maintain. This is directly linked with the actual (competence) competence unit

Ti Time to reduce competence gap to zero Ta Time over which competence loss (staff leaving) rate is

averaged Tr Recruitment process delay (competence development) 1/Ti 1/Ti It is the proportional constant to deal with the discrepancy

between target competence level and actual competence level

1/S 1/S This represent the actual (staff) competence level accumulated over time through the recruitment and training development and is affected by the present competence loss (staff leaving) rate

1/(1 þ Ta*S) aa Multiplier used in simulation to take account of Ta to average the competence rate (staff leaving) rate over the service demand average time

1/(1 þ Tr*S) ar Multiplier used in simulation to take account of Tr, and it is the competence development recruitment process to acquire staff

Table II. Glossary of terms used in the block diagram

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company Chairman and two Board of Directors help to illicit the main challenges and benefits with regards to the company HRP policy as below:

. Provide a coordinated process of recruitment, training, promotion and other actions.

. Forecasting future competencies needs.

. Building a linkage between HRP and the corporations’ long-term business objectives.

. Forecast recruitment and training needs to ensure that sufficient experienced people are rising through the rank to fill vacancies at higher levels.

. Manage production planning and control to meet service level targets better.

. Increase internal organization efficiency during staff absence.

. Improve overall productivity through organization of work activities.

. Have a centralized database of organizational competencies.

The S-CM suggested can meet many of the above requirements. The relevant data to staff recruitment, total staff pool, recruitment time, training time, and staff targets was collected. Experiments were designed to study the system behaviour against the given design parameters Ti, Ta and Tr as explained earlier and explained in form of Laplacian transfer function (Section 5). As mentioned earlier, the purpose of the simulation analyses presented here t to find optimum HR policy parameters for the company to maintain its target competence (staff) pool to meet its desired service target levels. The experiments were designed to change the parameters Ti, Ta, Tr systematically in a given range to observe and record the dynamic response in order to determine their optimum setting. Once selected, the system would determine staff recruitment automatically governed by Ta and Ti according to a present staff leaving rate and staff gap. Table III shows the performance index of the S-CM and describes the related system behaviour.

Figure 5 shows the dynamic response of the actual competence (staff) level and competence development (staff recruitment and training completion) rate for varying recruitment/training lead times (Tr). As shown in Figure 5(a) the increasing recruitment delay Tr would increase system oscillation. As shown in Figure 5(b), reducing the value of Tr improves the competence pool deficit. Figure 6(a) and (b), respectively, shows the response of actual competence level, and recruitment completion rate for the range of Ti values. The larger Ti values lead to a larger droop in the competence pool, indicating the company is unable to recover from the competence shortages over a period of time. In a worst-case scenario (Figure 6(b)), the company faces competence shortages for about 42 months for Ti ¼ 18 months, and therefore, would have problems in meeting the desired service level targets. On the other hand, small Ti values lead to large oscillation indicating surplus competence (over staffing)over a longer period, which is not desired either due to cost implications. Clearly, in control theory terminology, this is a bad system design. In reality, this shows a very aggressive hiring and firing human resource policy for the case company that will lead to unwanted cost and managing more than needed staff and going through lay-off procedures leading to anxieties and impact on staff morale.

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Figure 7 shows the dynamic response of the competence level and staff recruitment completion for varying values of Ta. Ta is gradually varied between one and 18 months for fixed values of Tr and Ti. As shown in Figure 7(a), increasing Ta slows down the recruitment process slightly. However, as shown in Figure 7(b) it would mean that the company would move from a short period of over staffing to a relatively prolonged period of staff shortages. Section 5 and Table III give the overall summary of the effects of varying Ti, Ta and Tr on the human resource polices.

Table IV illustrates the choice of parameter values that result in optimum system behavior. Table III also, are present the criteria for optimum human resource policy design behavior to minimize cost and impact of hiring and firing on the organization.

5. Transfer function for S-CM In classical control theory, the transfer function of a system represents the relationship describing the dynamics of the system under consideration (Towill, 1982). It algebraically relates a system input and system output. Figure 4 shows the block diagram representation of the key variables of the model and their interactions.

S-CM dynamic behaviour

Performance index (the HR policy design parameters)

Ti (time to reduce service gap to zero)

Ta (time over which competence loss (staff leaving) rate is averaged)

Tr (recruitment/ competence development process delay)

Competence development (recruitment/ training completion) rate measurements

Rise time (time needed to meet competence shortages) (months)

Increasing Ti increases slightly the rise time

Increasing Ta increases the rise time

Increasing Tr increases the rise time

Peak competence surplus (overshoot) (percentage from the nominal value)

Increasing Ti slightly increase the peak overshoot

Increasing Ta decrease the peak overshoot

Increasing Tr increases the peak overshoot

Duration of competence surplus (overshoot) (months)

Increasing Ti slightly increases the duration of overshoot

Increasing Ta increases the duration of overshoot

Increasing Tr increases the duration of overshoot

Service level measurements

Initial service level droop (percentage from the desired value)

Increasing Ti increases the initial service droop

Increasing Ta increases the initial service droop

Increasing Tr increases the initial service droop

Duration of service level deficit (months)

Increasing Ti increases the settling time

Increasing Ta increases the settling time

Increasing Tr increases the settling time

Peak service overshoot (percentage from the nominal value)

Increasing Ti decreases the peak service (surplus of competence) overshoot

Increasing Ta decreases the service level (competence surplus) overshoot

Increasing Tr increases the peak service level (competence surplus) overshoot

Table III. Performance index and associated dynamic behaviour for the S-CM

IJQSS 5,2

242

Figure 5. Dynamic response of

S-CM for (Ti ¼ Ta ¼ 4 months) for varying

values of Tr

Time (Months)

1,940

1,900

1,920

1,960

1,980

2,000

2,040

2,020

A ct

u a l S

e rv

ic e le

ve l (

ta rg

e t U

n its

)

Tr ( M

on th

)

145

1 6

16 21

130

135

140

150

155

C o m

p e te

n ce

D e ve

lo p m

e n t C

o m

p le

tio n R

a te

(U n its

/M o n th

)

Tr (M

on th

)

(b)

Time (Months)

(a)

1 6

11 16

2651 56 61

1 6 11 16 21 26 31 36 41 46

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65

Notes: (a) Competence development(staff recruitment) completion rate behaviour; (b) service target level behaviour

Planning HR requirements

243

Figure 6. Response of S-CM for (Ti ¼ Ta ¼ 4 months) and varying values of Ti

51 56 6 1

Time (Months)

1,950

1,940

1,930

1,920

1,960

1,970

1,980

1,990

2,000

2,010

A ct

u a l S

e rv

ic e le

ve l (

ta rg

e t U

n its

)

T i (

M on

th )

1 6 11 16 21 26 31 36 41 46

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65

1 6

11 16

21 26

130

135

140

150

155

C o m

p e te

n ce

D e ve

lo p m

e n t C

o m

p le

tio n R

a te

(U n its

/M o n th

)

Ti (M

on th

)

(a)

Time (Months)

(b)

1 6

11 16

21 26

Notes: (a) Competence development(staff recruitment) completion rate behaviour; (b) service target level behaviour

IJQSS 5,2

244

Figure 7. Response of S-CM for

(Ti ¼ Tr ¼ 4 months) and varying values of Ta

1 6

11 16

21 26

1,950

1,960

1,970

1,980

1,990

2,000

2,010

A ct

u a l S

e rv

ic e le

ve l (

ta rg

e t U

n its

)

Time (Months)

Ta ( M

on th

)

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65

1 6

11 16

21 26

132

134

136

138

140

142

144

146

148

150

152

154

C om

pe te

nc e

D ev

el op

m en

t (R

e cr

u itm

e n t)

C o m

p le

tio n R

a te

( U

n its

/M o n th

)

Time (Months)

Ta (M

on th

)

(a)

(b)

Notes: (a) Competence development(staff recruitment) completion rate behaviour; (b) service target level behaviour

Planning HR requirements

245

S -C

M d

es ig

n p

a ra

m et

er s

P er

fo rm

a n

ce in

d ex

(t h

e H

R p

o li

cy d

es ig

n p

a ra

m et

er s)

T i ¼

1 ,

T a ¼

2 ,

T r ¼

T i ¼

2 ,

T a ¼

4 ,

T r ¼

T i ¼

1 ,

T a ¼

4 ,

T r ¼

T i ¼

2 ,

T a ¼

2 ,

T r ¼

T i ¼

2 ,

T a ¼

4 ,

T r ¼

T i ¼

2 ,

T a ¼

4 ,

T r ¼

T i ¼

3 ,

T a ¼

2 ,

T r ¼

T i ¼

3 ,

T a ¼

4 ,

T r ¼

T i ¼

3 ,

T a ¼

4 ,

T r ¼

C o m

p et

en ce

d ev

el o p

m en

t (r

ec ru

it m

en t/

tr a in

in g

co m

p le

ti o n

) ra

te m

ea su

re m

en ts

R is

e ti

m e

(t im

e n

ee d

ed to

m ee

t co

m p

et en

ce sh

o rt

a g

es (m

o n

th s)

2 2

2 4

3 5

2 4

P ea

k co

m p

et en

ce su

rp lu

s (o

v er

sh o o t)

(p er

ce n

ta g

e fr

o m

th e

n o m

in a l

v a lu

4 .7

2 .7

4 .0

5 3 ..3

7 3 .5

7 2 .7

2 .7

3 .3

D u

ra ti

o n

o f

co m

p et

en ce

su rp

lu s

(o v

er sh

o o t)

(m o n

th s)

7 8

1 1

1 3

1 1

1 4

1 7

S er

v ic

e le

v el

m ea

su re

m en

ts In

it ia

l se

rv ic

e le

v el

d ro

o p

(p er

ce n

ta g

e fr

o m

th e

d es

ir ed

v a lu

1 1

1 0 .9

1 .6

5 1 .7

1 .0

5 1 .4

1 .8

D u

ra ti

o n

o f

se rv

ic e

le v

el d

efi ci

t (m

o n

th )

8 9

7 9

1 0

1 4

1 0

1 3

1 7

P ea

k se

rv ic

e le

v el

o v

er sh

o o t

(p er

ce n

ta g

e fr

o m

th e

n o m

in a l

v a lu

0 .1

5 0 .1

0 .3

5 0 .1

5 0 .3

5 0 .7

0 .1

0 .3

0 .6

Table IV. Performance index and associated dynamic behaviour for the S-CM for the case company, where the shaded region indicate the optimum HR policy design parameters

IJQSS 5,2

246

Equation (1) calculates service gap as the discrepancy between target service and actual service. As mentioned earlier we assume one to one relationships in between availability of a staff (skills) to meet the service target level. Equation (2) calculates the forecast staff leaving rate in terms of the smoothing function aa of the present staff leaving rate and equation (3) shows the schedule recruitment rate which aims to meet the forecast staff leaving rate. In order to meet this target we need to undertake some adjustment in staff gap as given by the function (1/Ti).

Equation (4) calculates the recruitment completion rate (or competence development rate if in house) and it is given in terms of the delaying function ar. The actual service level relates to competence level of the staff is calculated in equation (5) in terms of its previous level and the difference between the recruitment completion rate and present staff leaving rate.

Equations (1)-(5) may be used to develop the associated transfer functions that relate actual service level (relates to staff level) and recruitment completion rate to the present staff leaving rate. These two transfer functions are shown in equations (6) and (7), respectively:

Actual Service LevelðASLÞ

Present Competence Loss RateðPCLRÞ ¼

2Ti½ðTr þ TaÞ · S þ TrTa · S2�

ð1 þ Ta · SÞð1 þ Ti · S þ TiTr · S2Þ ð6Þ

Competence Development Completion RateðCDCRÞ

Present Competence Loss RateðPCLRÞ ¼

1 þðTi þ TaÞ · S

ð1 þ Ta · SÞð1 þ Ti · S þ TiTr · S2Þ

ð7Þ

Transfer functions are useful in understanding how the parameters Ti, Ta, and Tr, affect the time response behaviour of the actual staff level and the recruitment completion rate in terms of the present staff leaving rate. It is clear that changes to any of the control parameters will affect the behaviour of the two system outputs. Thus, in optimising the system behaviour by changing the parameters (Section 5) we will have to consider the effects on both the actual service (staff/competence) level and the recruitment completion rate together.

Equations (6) and (7) are useful in understanding how the parameters Ti, Ta, Tr, to be set by the decision maker to study the time response behaviour and determine human resource management policy guidelines.

Rates and levels appear as abbreviations at the start and finish of the arrow link lines. The signs associated with the arrow tips are extremely important in establishing the correct behaviour of the system, especially with regard to stability.

6. Conclusions HRP needs to respond to a greater demand for “talent” due to increased competition in the global market. The current developments in the resource based and core competence theories (Hafeez et al., 2002a, b, 2007; Hafeez and Abdelmeguid, 2003) have made practitioners increasingly aware of the importance of maintaining soft “core” skills within the company opposed to traditional tangible asset based strategies. Therefore, management needs to understand the dynamics of human resource policy within the company. System dynamics modeling can provide management with a tool to explore

Planning HR requirements

247

the impact of different human resource policies and to determine the key influencing parameters.

The model considered here is a S-CM to study the dynamics of the competence pool by tuning the design parameters associated with recruitment/training time, recruitment averaging time and a proportional control parameter to reduce the competence pool shortages. Based on the defined performance indices, the decision maker can choose to minimise the current and future competence shortages by selecting appropriate recruitment policies. The S-CM was tested using real data, and the outcome strategies were discussed with the relevant people in case company. The management of the company confirmed the usefulness of the model and the what-if scenarios generated to help them in planning medium to long term recruitment efforts.

This study confirms that the dynamic analysis based on the simulation model greatly improves the understanding of competence pool and human resource system behaviour. By tuning human resource policy parameters Ti, Ta and Tr the human resource practitioners should be able to optimize the target recruitment pattern while looking at current competence shortages. Also it is possible to reduce the current and future service gap by devising an appropriate recruitment and training program. Furthermore, such models can guide management to develop improved human resource policies for “hiring” and “firing” which, if excessive, is proven to be costly and have negative impact on staff morale.

One limitation of this study is that this model is tested in a relatively stable environment where the staff turnover was low. Future studies should look at testing the suitability of this model in a “competence intensive” industry such as IT, that relatively experience much staff turnover rates, and meeting the service level is very critical to remain competitive in the market.

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