Workforce modeling: Difference between revisions

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'''Workforce modeling''' is the process of aligning the demand for skilled labor with the availability and preferences of workers. It utilizes mathematical models to perform tasks such as sensitivity analysis, scheduling, and workload forecasting.

{{Use mdy dates|date=October 2024}}{{peacock|date=January 2014}}

This approach can be used in industries with complex labor regulations, certified professionals, and fluctuating demand — such as healthcare, public safety, and retail. Workforce modeling tools can include software that helps determine staffing needs based on workload variations across times of day, days of the week, or seasonal cycles.

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==Definition==

The term can be differentiated from traditional staff [[Schedule (workplace)|scheduling]].<ref>{{Cite journal |last=Ernst |first=A. T |last2=Jiang |first2=H |last3=Krishnamoorthy |first3=M |last4=Sier |first4=D |date=2004-02-16 |title=Staff scheduling and rostering: A review of applications, methods and models |url=https://www.sciencedirect.com/science/article/pii/S037722170300095X |journal=European Journal of Operational Research |series=Timetabling and Rostering |volume=153 |issue=1 |pages=3–27 |doi=10.1016/S0377-2217(03)00095-X |issn=0377-2217|url-access=subscription }}</ref> Research indicates that traditional static planning models result in 60% of operating hours being either understaffed, or overstaffed, while modern workforce modeling implementations have achieved substantial cost reductions.<ref name=":1">{{Cite web |title=AI workforce planning for travel and logistics {{!}} McKinsey |url=https://www.mckinsey.com/industries/travel/our-insights/ai-can-transform-workforce-planning-for-travel-and-logistics-companies |access-date=2025-06-24 |website=www.mckinsey.com}}</ref> Staff scheduling is rooted in [[time management]].<ref>{{Cite journal |last=Pinedo |first=Michael L. |date=2022 |title=Scheduling |url=https://link.springer.com/book/10.1007/978-3-031-05921-6 |journal=SpringerLink |language=en |doi=10.1007/978-3-031-05921-6|url-access=subscription }}</ref> Besides demand orientation, workforce modeling also incorporates the forecast of the workload and the required staff, the integration of workers into the scheduling process through interactivity, and analysis of the entire process.<ref>{{Cite journal |last=Algethami |first=Haneen |last2=Martínez-Gavara |first2=Anna |last3=Landa-Silva |first3=Dario |date=2019-10-01 |title=Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem |url=https://doi.org/10.1007/s10732-018-9385-x |journal=Journal of Heuristics |language=en |volume=25 |issue=4 |pages=753–792 |doi=10.1007/s10732-018-9385-x |issn=1572-9397}}</ref> The evolution from traditional scheduling to workforce modeling demonstrated quantitative benefits and reflects broader technological advancement in organizational management.<ref name=":1" />

The term can be differentiated from traditional staff [[Schedule (workplace)|scheduling]]. Staff scheduling is rooted in [[time management]]. Besides the aspects of demand orientation, workforce modeling also incorporates the forecast of the workload and the required staff, the integration of workers into the scheduling process through interactivity, as well as analyzing the entire process.

==Complexity of model==

Many workforce modeling solution applications use the [[linear programming]] approach to create the Workforce Model. Linear methods of achieving a schedule are generally based on assumptions that demand is based on a series of independent events, all of which have a consistent, predictable outcome.<ref name="Clancy, Thomas R. 2008">Clancy, Thomas R. ''Managing Organizational Complexity in Healthcare Operations.'' The Journal of Nursing Administration 38.9 (2008): 367–370. Print.{{Citation not found|date=October 2024}}</ref> [[Heuristics]] have also been applied to the problem, and [[metaheuristics]] have been identified as the best method for generating complex scheduling solutions.<ref>{{Cite journal |last1=Burke |first1=Edmund |last2=Causmaecker |first2=Patrick De |last3=Berghe |first3=Greet Vanden |last4=Landeghem |first4=Hendrik Van |date=2004 |title=The State of the Art of Nurse Rostering |url=https://lirias.kuleuven.be/bitstream/123456789/123829/1/JOS_ |url-status=dead |journal=Journal of Scheduling |volume=7 |issue=441–499 |pages=441–499 |doi=10.1023/B:JOSH.0000046076.75950.0b |archive-url=https://web.archive.org/web/20160304113501/https://lirias.kuleuven.be/bitstream/123456789/123829/1/JOS_ |archive-date=March 4, 2016}}</ref><ref name="Clancy, Thomas R. 2008" />

==Complexity of the model==

Workforce modeling solutions can be created using a [[software]] solution for demand-oriented workforce management.

Many applications providing workforce modeling solutionsolutions applicationsmight use the [[linear programming]] approach to create the Workforce Model. Linear methods of achieving a schedule are generally based on assumptionsassume that demand is based on a series of independent events, alleach of which havewith a consistent, predictable outcome. Modeling the uncertainty and dependability of such events is a well-researched area.<ref name="Clancy, Thomas R. 2008">Clancy, Thomas R. ''Managing Organizational Complexity in Healthcare Operations.'' [[The Journal of Nursing Administration 38.9 (2008): 367–370. Print.]]</ref> Modeling approaches such as [[system dynamics]] have been employed in workforce modeling to address interdependencies and feedback loops within large organizations, such as [[NASA]].<ref name=":0">{{CitationCite notjournal |last=Marin |first=Mario |last2=Zhu |first2=Yanshen |last3=Meade |first3=Phillip |last4=Sargent |first4=Melissa |last5=Warren |first5=Jullie found|date=October2007 2024|title=Workforce Enterprise Modeling |url=https://www.jstor.org/stable/44719519 |journal=SAE Transactions |volume=116 |pages=873–876 |issn=0096-736X}}</ref> [[Heuristic|Heuristics]] have also been applied to the problem, and [[metaheuristics]] have been identified as theeffective best methodmethods for generating complex scheduling solutions.<ref name="Clancy, Thomas R. 2008" /><ref>{{Cite journal |last1=Burke |first1=Edmund |last2=Causmaecker |first2=Patrick De |last3=Berghe |first3=Greet Vanden |last4=Landeghem |first4=Hendrik Van |date=2004 |title=The State of the Art of Nurse Rostering |url=https://lirias.kuleuven.be/bitstream/123456789/123829/1/JOS_ |url-status=dead |journal=Journal of Scheduling |volume=7 |issue=441–499 |pages=441–499 |doi=10.1023/B:JOSH.0000046076.75950.0b |archive-url=https://web.archive.org/web/20160304113501/https://lirias.kuleuven.be/bitstream/123456789/123829/1/JOS_ |archive-date=March 4, 2016|url-access=subscription }}</ref><ref name="Clancy, Thomas R. 2008" />

==NotesReferences==

{{reflist}}

<references/>

==Further reading==

*Sterman JD. ''Business Dynamics: Systems Thinking and Modeling For a Complex World.'' Boston, Massachusetts: McGraw-Hill Publishers; 2000.

*Taleb NN. ''The Black Swan.'' New York, New York: Random House; 2007.

*West B, Griffin L. ''Biodynamics: Why the Wirewalker Doesn't Fall.'' Hoboken, New Jersey: John Wiley & Sons, Inc;., 2004.

[[Category:Management systems]]

[[Category:Human resource management]]

[[Category:Workforce]]