Intelligent Human Resource Planning System in a Large Petrochemical Enterprise

102 1541-1672/13/$31.00 © 2013 IEEE IEEE INtEllIgENt systEMsPublished by the IEEE Computer Society

C Y B E R - P H Y S I C A L - S O C I A L S Y S T E M SEditor: Liuqing Yang, Colorado State University, liuqingyang.ieee@gmail.com

Intelligent Human Resource Planning System in a Large Petrochemical Enterprise

Yizheng Wang and Lefei Li, Tsinghua UniversityLiuqing Yang, Colorado State University

as the great likelihood of talent outfl ow, especially for high-level personnel; the shortage of qualifi ed technicians while having too many low-level person-nel; and a diversity in age levels that doesn’t always balance well with a team’s hierarchical structure.

Furthermore, the uncertainties of internal and ex-ternal environments also impact human resources. The internal environment includes changes in the production plans, organizational structure, human resource strategies, and capacity structure. The ex-ternal environment includes competitors’ strategy, job market situation, and other economic condi-tions. With all of these challenges, the critical de-cisions are, however, made either empirically or based on myopic rules.

Here, we use Petrochemical enterprise M, abbreviated as PM, as a case study to show how artifi cial systems have the potential to support human resource planning in large and comprehen-sive enterprises.

PM is one of the largest refi nery and chemical companies in China, with more than 9,000 employ-ees in 20 subsidiary units. Through human resource structure analyses as well as fi eld interviews and surveys, we found that imbalances in the age struc-ture and unexpected employee resignations are two major problems that could cause signifi cant risk.

Structure of Human Resources in PMHuman resources in PM is comprised of three tracks: skilled, professional, and management teams, with various subsidiary roles (see Figure 1). Around

90 percent of employees are working in positions di-rectly related to the daily operation. Here, we target a key workshop and focus on the skilled team; pro-fessional team; and the device manager, the work-shop vice director, and the workshop director from the management team. (For others’ work, see the re-lated sidebar.)

In general, for any employee, the main factors affecting his/her income and other benefi ts usually involve the employee’s position and grade (profes-sional/skill level). We illustrate an employee’s po-tential career paths in Figure 2.

The promotion between positions is highly re-lated to the change of grade level. A novice can choose one of two tracks: skilled or professional, as in Figure 2b. Each track has multiple levels that have clear promotion requirements in between.

An Artifi cial Human Resource SystemWith our understanding of the human resource struc-ture, we built an artifi cial human resource system that uses an agent-based simulation method to model and simulate human resource dynamics in PM.

Using the basic elements that comprise the hu-man resource system, employees are modeled as the main agents. Each agent has its own properties, such as age, gender, working experience, grade, and so on. Accordingly, each agent has fi ve types of be-haviors: recruitment, retirement, grade promotion, position promotion, and leaving.

Recruitment behavior is decided by human re-source planning while retirement behavior is de-cided by the agent’s age property. In most cases, male employees will retire at 60 and female employees will retire at 55.

Grade promotion behavior is related to the agent’s educational background and working experience. For

Although Chinese petrochemical enterprises

have devoted considerable effort to human

resource management, several challenging situa-

tions can arise that cause serious problems, such

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those who meet the requirements and want to obtain the promotion, they will trigger the promotion behavior through certain promotion rates, which corre-spond to passing skill tests in reality.

Position promotion behavior is not only related to the agent’s educational background and working years, but is also based on the agent’s grade. The promotion mechanism is simi-lar to the grade promotion. Only the trigger rules are different.

Separation behavior is usually related to an individual’s characteristics. Con-servative employees tend to be satis-fied with their current grade and won’t leave, while ambitious employees tend to leave when they aren’t promoted to the expected grade in a certain period of time. We use Figure 3 to describe the agent’s separation behavior.

In Figure 3, E represents the agent’s promotion motivation and P represents the expected value provided by each position to meet the agent’s promotion motivation. If the current position’s

Figure 1. Human resource composition structure. There are three main tracks (skilled, professional, and management teams), each with various subsidiary roles.

Humanresource

Skilled teamProfessional

teamManagement

team

DevicemanagerClerk

Outsideoperator

Boardoperator

Three types oftechnologists

Vice monitor

Monitor

Workshop vicedirector

Workshopdirector

Officemanagement

position

Since the 21st century, talent has gradually become the most important strategic resource for an enterprise, even for a country. In recent developments of human re-

source management, the “human-centered” concept has be-come the core guiding ideology, and employees are regarded as the critical resource to enterprises and organizations.

In human resource management, human resource planning is the key decision process at the strategic level, and it has a critical impact on maintaining stable human resource develop-ment. This process includes planning recruitment, promotions, and training. Researchers’ have been focusing on two types of problems: the workforce forecasting problem1,2 and the man-power/workforce planning problem. For the planning prob-lem, heuristics,3 stochastic programming,4 goal programming,5 optimal control,6 and dynamic programming7–9 approaches have been employed to get the optimal decision or policy.

Although current studies have considered the human resource planning problem with constraints in recruitment capacity, the training budget, office space, and so on—while also consider-ing demand fluctuations and other uncertain factors—how to explicitly incorporate the employee’s career-related behaviors (such as promotions and demotions) and the underlying motiva-tions in the planning process is still a challenging problem.

In this article, we focus on recruitment and promotion planning, to demonstrate that decision support can be facili-tated by an artificial human resource system.

References 1. D. Ward, “Workforce Demand Forecasting Techniques,” Human

Resource Planning, vo. 19, no. 1, 1996, pp. 54–55. 2. H. Jantan, A. Hamdan, and Z. Othman, “Classification Tech-

niques for Talent Forecasting in Human Resource Manage-ment,” Advanced Data Mining and Applications, LNCS 5678, Springer, 2009, pp. 496–503.

3. N. Nakamura and T. Shingu, “A Model for Recruiting and Train-ing Decisions in Manpower Planning,” The Int’l J. Production Research, vol. 22, no. 1, 1984, pp. 1–15.

4. X. Zhu and H.D. Sherali, “Two-Stage Workforce Planning under Demand Fluctuations and Uncertainty,” J. Operational Research Soc., 2007, vol. 60, no. 1, pp. 94–103.

5. D.A. Goodman, “A Goal Programming Approach to Aggre-gate Planning of Production and Work Force,” Management Science, vol. 20, no. 12, 1974, pp. 1569–1575.

6. A.M. Mouza, “Application of Optimal Control in Man Power Planning,” Quality & Quantity, vol. 44, no. 2, 2010, pp. 199–215.

7. P.P. Rao, “A Dynamic Programming Approach to Determine Optimal Manpower Recruitment Policies,” J. Operational Research Soc., vol. 40, no. 10, 1990, pp. 983–988.

8. C.M. Khoong, “Some Optimization Models for Manpower Planning,” Information Knowledge Systems Management, vol. 1, no. 2, 1999, pp. 159–171.

9. A. Mehlmann, “An Approach to Optimal Recruitment and Transition Strategy for Manpower System Using Dynamic Programming,” J. Operational Research Soc., vol. 31, no. 11, 1980, pp. 1009–1015.

Related Work on Human Resource Planning

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104 www.computer.org/intelligent IEEE INtEllIgENt systEMs

value is higher than or equal to the agent’s expectation, this agent will stay in the current grade, or else—judg-ing by whether the agent is qualified for promotion conditions—the agent will get promoted to a certain promo-tion rate. The agents promoted success-fully will reach a higher grade, and the agents not promoted will leave with a certain separation rate. Generally, the separation rate is related to the differ-ence between agent’s promotion moti-vation and the expected value provided by the position; the bigger the differ-ence is, the higher the separation rate.

Applications of the Simulation ModelAfter defining the agent’s proper-ties and behaviors, we construct the simulation model using AnyLogic, a popular agent-based simulation soft-ware package. To validate the simula-tion model, we use the human resource data of PM with a minor modification for confidential purposes. We select a typical joint workshop in the refining

Figure 2. Potential employee career paths. (a) Human resource position structure in PM. (b) Human resource grade structure in PM.

Professional team Skilled team

Newentry

Outsideoperator

Freshman

Member levelengineer

Assistantengineer

Engineer

Senior engineer

Professor seniorengineer

End

Intermediatelevel

Senior level

Technician

Seniortechnician

Junior level

Professional qualifications Skill levelClerk

Managem

ent team (office)

Boardoperator

Vice monitor

Monitor

Leave/retire

Workshopdirector

(a) (b)

Workshopvice director

Devicemanager

Three types oftechnologists

Management team (workshop)

Figure 3. Employee agent’s separation behavior.

Employee

Stay in current level If E > P,

If meetpromotion

qualification?

LeaveSeparation rate

Promoted to next level

Ifpromotion

?

Promotion rate

N

N

N

Y

Y

Y

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division as the subject of this case study and simulate 20 years of its hu-man resource development, under the condition of three new hires per year (which is the workshop’s current re-cruitment strategy). The simulation model can assist the enterprise with the following four types of analyses.

Human Resource Development PredictionThe main job of the human resources department is to predict the change of human resource development for each position, and then fix the cor-responding recruitment or training strategies for the entire company. In our simulation model, each agent will choose its own development route ac-cording to the predefined behavioral rules. Then, with the simulation of aging, promotion, leaving activities, and so on, we’ll be able to predict the general situation of human resources.

As Figure 4 shows, at the beginning the head count grows steadily. How-ever, around 2021, the head count starts to decline rapidly because of the influence of the retirement peak and increasing separation. This phe-nomenon verifies the influence of un-balanced ages on the human resource structure.

Recruitment Strategy EvaluationOccasionally, managers must pro-pose new recruitment strategies to match the enterprise’s development plan. Our simulation model is then a perfect tool to evaluate such strate-gies prior to their actual deployment. For example, PM’s mother company announces a new regulation that the total annual recruitment quota must not exceed 90 percent of the retire-ment and separation numbers of the preceding year. Figure 5 compares the changes in the head count under the new strategy versus the current one. Under the current recruitment

strategy, the head count increases in the beginning and decreases later. For the new recruitment strat-egy, the head count fluctuates and decreases asymptotically. Although

both strategies will drop below the head count requirement (140 peo-ple), the new strategy quickly leads to understaffing, which means the new strategy might restrict the head

Figure 4. Simulation output of human resource development in next 20 years. Around 2021 there’s a retirement peak because of the age imbalance of employees.

Head count

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Retirement number Leaving number Recruitment number

Figure 5. Developing changes to the head count under two recruitment strategies. The new strategy might restrict the head count growth in the near future, but will cause serious understaffing in the long run.

155New strategy Current strategy

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Figure 6. Changes in the number of monitors under two regulations. If the company’s regulation requires that the monitor be at the senior level, then the company’s needs are met, but if the company requires monitors to be qualified as actual technicians, then there’s a shortage.

20Technician regulation Senior level regulation

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count growth in the near future, but will cause serious understaffi ng in the long run.

Impact Assessment of New RegulationNew HR regulations usually have a signifi cant impact on the human resource structure. For example, a monitor (a person who leads a session of the production unit) is required to be senior level, but when the enterprise wants to further improve a team’s op-eration abilities, the monitor’s grade is required to be at least that of a technician’s.

Figure 6 compares the changes in the number of eligible monitors un-der these two regulations. For the “senior level” regulation, the moni-tor number is always above the head count requirement (16 people), while for the “technician” regulation, there won’t be enough eligible monitors at the latter period of simulation. There-fore, to avoid a shortage of eligible

monitors, we suggest more training efforts from the senior level to the technician level.

In this article, we adopted agent-based modeling and simulation to evaluate human resource strate-gies to assist human resource plan-ning. The simulation model describes the properties and behavior rules of the  human resource management, with the consideration of the relations between position promotion, grade, age, and education.

However, it’s extremely diffi cult to quantitatively describe complex hu-man behavior; further efforts are re-quired to simulate human behaviors in a more precise and accurate manner. On the other hand, the availability of a simulation model that can evaluate different alternatives signifi cantly helps algorithm development in solving the human resource- planning optimiza-tion problem.

AcknowledgmentsThis work was supported partly by the Min-istry of Technology (MOT) of China grants 2012-364-X03-104 and the National Nat-ural Science Foundation of China grant 61172105.

yizheng Wang is a master’s student in

the Department of Industrial Engineering

at Tsinghu a University in Beijing, China.

Contact him at wangyz1987528@sina.

com.

lefei li is an associate professor in the De-

partment of Industrial Engineering at Tsin-

ghua University in Beijing, China. Contact

him at lilefei@tsinghua.edu.cn.

liuqing yang is an associate professor with

the Department of Electrical and Computer

Engineering at Colorado State University.

Contact her at liuqingyang.ieee@gmail.com.

Selected CS articles and columns are also available for free at

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