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Workforce modeling

Workforce modeling aligns the demand for skilled labor with worker availability and preferences using mathematical models for tasks like sensitivity analysis, scheduling, and workload forecasting. It’s essential in industries with complex labor regulations and variable demand, including healthcare, public safety, and retail. Specialized workforce management software helps organizations optimize staffing by analyzing workload fluctuations across different times and seasons, ensuring efficient and compliant labor allocation.

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Definition

The term can be differentiated from traditional staff scheduling.1 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.2 Staff scheduling is rooted in time management.3 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.4 The evolution from traditional scheduling to workforce modeling demonstrated quantitative benefits and reflects broader technological advancement in organizational management.5

Complexity of the model

Many applications providing workforce modeling solutions use the linear programming approach. Linear methods of achieving a schedule generally assume that demand is based on a series of independent events, each with a consistent, predictable outcome. Modeling the uncertainty and dependability of such events is a well-researched area.6 Modeling approaches such as system dynamics have been employed in workforce modeling to address interdependencies and feedback loops within large organizations, such as NASA.7 Heuristics have also been applied to the problem, and metaheuristics have been identified as effective methods for generating complex scheduling solutions.89

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.

References

  1. Ernst, A. T; Jiang, H; Krishnamoorthy, M; Sier, D (February 16, 2004). "Staff scheduling and rostering: A review of applications, methods and models". European Journal of Operational Research. Timetabling and Rostering. 153 (1): 3–27. doi:10.1016/S0377-2217(03)00095-X. ISSN 0377-2217. https://www.sciencedirect.com/science/article/pii/S037722170300095X ↩

  2. "AI workforce planning for travel and logistics | McKinsey". www.mckinsey.com. Retrieved June 24, 2025. https://www.mckinsey.com/industries/travel/our-insights/ai-can-transform-workforce-planning-for-travel-and-logistics-companies ↩

  3. Pinedo, Michael L. (2022). "Scheduling". SpringerLink. doi:10.1007/978-3-031-05921-6. https://link.springer.com/book/10.1007/978-3-031-05921-6 ↩

  4. Algethami, Haneen; Martínez-Gavara, Anna; Landa-Silva, Dario (October 1, 2019). "Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem". Journal of Heuristics. 25 (4): 753–792. doi:10.1007/s10732-018-9385-x. ISSN 1572-9397. https://doi.org/10.1007/s10732-018-9385-x ↩

  5. "AI workforce planning for travel and logistics | McKinsey". www.mckinsey.com. Retrieved June 24, 2025. https://www.mckinsey.com/industries/travel/our-insights/ai-can-transform-workforce-planning-for-travel-and-logistics-companies ↩

  6. Clancy, Thomas R. Managing Organizational Complexity in Healthcare Operations. The Journal of Nursing Administration 38.9 (2008): 367–370. Print. /w/index.php?title=The_Journal_of_Nursing_Administration_38.9_(2008):_367%E2%80%93370._Print.&action=edit&redlink=1 ↩

  7. Marin, Mario; Zhu, Yanshen; Meade, Phillip; Sargent, Melissa; Warren, Jullie (2007). "Workforce Enterprise Modeling". SAE Transactions. 116: 873–876. ISSN 0096-736X. https://www.jstor.org/stable/44719519 ↩

  8. Clancy, Thomas R. Managing Organizational Complexity in Healthcare Operations. The Journal of Nursing Administration 38.9 (2008): 367–370. Print. /w/index.php?title=The_Journal_of_Nursing_Administration_38.9_(2008):_367%E2%80%93370._Print.&action=edit&redlink=1 ↩

  9. Burke, Edmund; Causmaecker, Patrick De; Berghe, Greet Vanden; Landeghem, Hendrik Van (2004). "The State of the Art of Nurse Rostering". Journal of Scheduling. 7 (441–499): 441–499. doi:10.1023/B:JOSH.0000046076.75950.0b. Archived from the original on March 4, 2016. https://web.archive.org/web/20160304113501/https://lirias.kuleuven.be/bitstream/123456789/123829/1/JOS_ ↩