AHybridModelingandSimulationApproachfortheStrategicWorkforcePlanningProblem

JoachimBlockarchibald@ieee.org

The36nd InternationalConference oftheSystemDynamicsSocietyAugust6– 10,2018– Reykjavik,Iceland

memberof

StrategicWorkforcePlanning(initsbasicform)

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Modelingaworkforce systemconcerns threetypesofflows,namelytherecruitment flows,theinternalpersonnel flowsbetween thedifferent

personnelcategories(amongotherspromotionflows),andthewastage.[GUER2011]

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StrategicWorkforcePlanning(initsbasicform)

3[STER2000]

Associate Professors300

225

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01930 1942 1954 1966 1978 1990 2002

Time (Year)

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Associate Professors : current

Whataretheleverpointstocontrolthesystem(intowhichdirection)?

4KPI’s:turnover,vacancies,demography,performance,…

StrategicWorkforcePlanning(revisited)HRpoliciesandpractices,externalenvironment

Designingahighperformanceworksystem

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Individual behavior

Patterns of behavior

General rules of the system(HR policies)

workforce(ABMS)

internal decision making

(SD + DES)

an employee

workforce system

Stockoutflowinflow

auxaliary1

auxaliary2

q1 q2 q3

q0

ModelingtheComplexAdaptiveSystem

Exploitingstrengthsbyovercoming

barriers

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BehaviorofanEmployee

P=f(A,M,O)[APPE2000],[JIAN2012]

TheHybridModelofanAgent(anemployee)

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Motivation

health

workloadexperience in

current job

gratification/market valuematch

promotion/marketvalue match

time in current job

market value

labor market

age

compensation

+

-

-

--

+-

+

+++

promotionprospects

+

Opportunity

socio-technicalsystem design

+Ability (KSA)

learning

+

training effort

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organisationaltransformation

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<health>

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+

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<age>

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Performance+

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<Opportunity>

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R 1

R 2

B 3

B 1

B 2+

job challenge

++

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-

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deviation fromoptimum

optimalworkload

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<Time>

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DES subsystemSD subsystem

internal eventquit

discreteoutputquit

discreteinput

promotion

continuousinput

HR policies,overall state

continuousoutput

performance,age etc.

A14A13 A15 A16

Exit

[ZEIG2000], [BLOC2014], [BLOC2016]

Basedonwell

establishedDEV&DESSformalism

RunningtheSimulation(PromotionbySeniority)

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Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502 Vol. 1(6), 45-54, June (2012) Res. J. Recent Sci.

International Science Congress Association 53

Table-1 Variables name and values for Multivariate Sensitivity Analyses in the case hospital

Variable name Base run value maximum minimum Delay in hiring employee (years) 1.48 2 1 Delay in hiring contract employees (years) 0.6 1 0.3 New contract employees quit rate (person/year) 0.2 0.5 0.1

Total employee

Potential output

Potential of Employee Productivity

Figure-4 Some model variable behaviors in the Multivariate Sensitivity Analyses in the case hospital

a. Total employee

b. Time spent on Training new employee

c. Effective Experienced Employee

d. Potential output

e. Potential of employee productivity

Figures-5

The behavior of five variables of the model in policy analysis

References

1. Mathis R.L. and Jackson J.H., Human Resource Management, Thomson South-Western (2010)

2. Izadi A., Designing A Dynamic BSC Model for Iranian Social Security Organization Hospitals. PH.D. Dissertation. Islamic Azad University, Sciences and Research Branch, Tehran, Iran, (2010) [Text in Persian]

3. Mawdesley M.J. and Al-Jibouri S., Modelling construction project productivity using systems dynamics approach, Int J Prod Perform Manag, 59(1), 18 – 36 (2009)

4. Som V.C., Exploring the human resource implications of

clinical governance, Health Pol, 80(2), 281–96 (2007)

5. Kang S. and Snell S., Intellectual capital architectures and ambidextrous learning: a framework for human resource management, J Manag Stud, 46(1), 65-92 (2009)

6. Ordóñez de Pablos p., Human resource management systems and their role in the development of strategic resources: empirical evidence, J Eur Ind Train, 28(6), 474-489 (2004)

Figure:N

asiripour

A.etal.

(2012)

SD model

- completedifferentapproach,butverysimilarpatterns- SDmodellimitedtofewKPI(e.g.nodemography)andpolicies- attritionfractionhastobesetbyuser(fixed)inSDmodel

Averageperformance/productivityperemployeeHybridmodel

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form

ance

(%)

Month

70

80

90

100

110

120

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0 40 80 120 160 200 240 280 320 360 400 440 480

Promotion by seniority

NoCalibrationtoSDModel!

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Someimportantresults:

− differentHRpolicies revealdifferent results

− a trainingpolicyhastobeflanked withotherpolicies

Per

form

ance

(%)

Month

70

80

90

100

110

120

130

0 40 80 120 160 200 240 280 320 360 400 440 480

Promotion by seniority

Promotion by performance

Promotion by seniority with increased training

Promotion by performance with increased training

Voluntaryturnover

Figure_5_Results2.pdf

Figure 5: Average waiting times for ’promotion by seniority’ (left) and ’promotion byperformance’ (right) with a training policy in place

by a mismatch between job expectations and KSA. Fair promotion prospectscan compensate this only partially. Now, with being unsatisfied some agentsdecide to quit well trained also in grade A14. Altogether, voluntary turnoverrates nearly tripple (table ??).

We observe similar e↵ects for ’promotion by performance’. Average wait-ing times in A13 fall as a result of higher voluntary turnover rates in gradesA13 and A14. In contrast, waiting times in grade A16 increase and the ex-isting promotion jam into A16 is reinforced. Average age in this grade isvery low and so are retirement rates. What follows is that agents in lowergrades not only quit due to a job challenge mismatch but also due to very lowpromotion prospects. Without a realistic chance for promotion even somehigh qualified agents in grade A15 decide to turnover voluntarily.

Table 4: Voluntary turnovers of the simulated scenarios

years seniority seniority + training performance performance + training

1 - 5 22 24 39 426 - 10 24 53 36 8711 - 15 4 8 15 2316 - 20 0 0 15 5921 - 25 0 0 12 4126 - 30 0 37 16 7331 - 35 8 30 36 10236 - 40 8 18 22 54

Sum 66 170 191 481

5.3. Discussion

Modeling the individual agents of the workforce system enables us tocapture and study the feedback mechanisms between micro level behaviorand macro level flows. Reduced flows mainly forces younger and well trainedcivil servants to leave the organization for better career prospects elsewhere.In turn, high voluntary turnover results in new vacancies and continuingflows. A cyclic pattern of stop and go respectively quit and stay evolves.

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RunningtheSimulation(DifferentPolicies)

EvaluationoftheHybridApproach

10age between HRM and operational and financialoutcomes.

Moreover, this study embraced the multidimen-sionality of performance as well as the potential fordifferent relationships with proximal and distaloutcomes. Researchers have recently called forstudies to simultaneously examine multiple out-come variables that have only been studied inde-pendently before (Lengnick-Hall et al., 2009). Withthe help of meta-analytic techniques, we tested acomprehensive mediating model and provided em-pirical support for the theoretical proposition thatHRM first relates to proximal outcomes, which fur-ther relate to distal outcomes (Becker & Huselid,1998; Delery & Shaw, 2001; Dyer & Reeves, 1995;Guest, 1997) and revealed that the relationshipsbetween HRM and distal outcomes (e.g., opera-tional and financial outcomes) could be mediatedthrough multiple pathways (e.g., through humancapital and employee motivation). Moreover, as weexpected, there were direct relationships betweenskill-enhancing HR practices and motivation-en-hancing HR practices and financial outcomes thatcould not be explained by the mediating process.This is consistent with prior research suggestingthat HRM can improve organizational effectivesthrough alternative approaches such as affectinginternal interaction within organizations (Evans &

Davis, 2005; Gittell et al., 2010) and enhancing thesocial capital of organizations (Collins & Clark,2003). The findings of the current study and otherssuggest that it is meaningful for future research tofurther explore other mediators of the relationshipbetween HRM and organizational outcomes.

One major contribution of this study to the stra-tegic HRM literature is that the results suggest dif-ferential effects of the three dimensions of HR sys-tems. This finding is important both in theory andin the methodology of measuring HR systems. The-oretically, this finding challenges previous re-search, in which the assumption has been that allHR practices in an HR system function in the samepattern. Our findings remind researchers that dif-ferent dimensions of HR systems may have uniquerelationships with specific organizational out-comes. For example, skill-enhancing HR practiceswere more effective in enhancing human capital,whereas motivation-enhancing HR practices andopportunity-enhancing HR practices were morelikely to improve employee motivation. This resultis also consistent with recent research suggestingthe heterogeneous effects of the components of HRsystems on organizational outcomes (e.g., Batt &Colvin, 2011; Gardner et al., 2011; Gong et al., 2009;Liao et al., 2009; Shaw et al., 2009; Subramony,2009). HR practices are not only distinct, but also

FIGURE 4Effects of HPWS on Organizational Outcomesa

.67**

Skill-Enhancing HR Practices

Motivation-Enhancing HR

Practices

Opportunity-Enhancing HR

Practices

Human Capital R2 = .34

Employee Motivation

R2 = .38

Voluntary Turnover R2 = .15

Operational Outcomes R2 = .22

Financial Outcomes R2 = .27

High Performance

Work Systems

.67**

.64**

.58**

.62**

.21**

–.10**

.03

–.28*

.16**

–.05**

.37**

–.08*

.34** a Standardized coefficients are presented; n ! 3,714.

* p " .05** p " .01

2012 1277Jiang, Lepak, Hu, and Baer

Modelvalidationisachallenge

Simulationresultsresemble

empiricalstudies(withoneexception)[JIAN2012]

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• differentperspectives• morerealism• reducedmodelcomplexity• facilitated interactionwith

stakeholders

Per

form

ance

(%)

Month

70

80

90

100

110

120

130

0 40 80 120 160 200 240 280 320 360 400 440 480

Promotion by seniority

Promotion by performance

Promotion by seniority with increased training

Promotion by performance with increased training✓✓✓

✓✓

✓

✓✓

Maindisadvantages: • datasource• multimethodskillsnecessary

Predictiveand explanatorypower

[ADAM2017], [Santa Fe Institute 2017 – What are the limits of scientific understanding?]

EvaluationoftheHybridApproach

SelectedReferences[ADAM2017]AdamC.andGaudon B.;2017;BDIagentsinsocialsimulations:asurvey; KnowledgeEngineeringReview,Vol.31,No.3,pp.207-238.[APPE2000]Appelbaum E.,BaileyT.,BergP.,and Kalleberg A.L.;2000;ManufacturingAdvantage;CornellUniversityPress,Ithaca,NY,USA.[BLOC2014]BlockJ.andPickl S.;2014;TheMysteryofJobPerformance:ASystemDynamicsModelofHumanBehavior;32ndInternationalConferenceoftheSystemDynamicsSociety,20July- 24July2014,Delft,TheNetherlands.[BLOC2016]BlockJ.;2016;Ahybridmodelingapproachforincorporatingbehavioralissuesintoworkforceplanning;ProceedingsoftheIEEEConferenceonSystems,Man,andCybernetics(SMC)2016,pp.1-6.[GUER2011]GuerryM.-A.;2011;Hiddenheterogeneityinmanpowersystems:AMarkov-switchingmodelapproach;EuropeanJournalofOperationalResearch,Vol.210,pp.106-113.[JIAN2012]JiangK.,Lepak D.P.,HuJ.,andBaerJ.C.;2012;HowDoesHumanResourceManagementInfluenceOrganizationalOutcomes?AMeta-AnalyticInvestigationofMediatingMechanisms;AcademyofManagementJournal,Vol.55,No.6,pp.1264-1294.[NASI2012]Nasiripour A.,Afshar KazemiM.,andIzadiA.;2012;EffectofDifferentHRMPoliciesonPotentialofemployeeProductivity;ResearchJournalofRecentSciences,Vol.1,No.6,pp.45-54.[STER2000]StermanJ.D.;2000;BusinessDynamics– SystemsThinkingandModelingforaComplexWorld;McGraw-Hill,Boston,USA.[ZEIG2000]ZeiglerB.P.,PraehoferH.,andKimT.G.;2000;TheoryofModelingandSimulation- IntegratingDiscreteEventandContinuousComplexDynamicSystems;Secondedition,AcademicPress,SanDiego,CA,USA.

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