Workforce modeling

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.

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.

## Contents

* 1 Definition
* 2 Complexity of the model
* 3 References
* 4 Further reading

## 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.[2]

## Complexity of the model

Many applications providing workforce modeling solutions might 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.[5] Modeling approaches such as system dynamics have been employed in workforce modeling to address interdependencies and feedback loops within large organizations, such as NASA.[6] Heuristics have also been applied to the problem, and metaheuristics have been identified as effective methods for generating complex scheduling solutions.[5][7]

## References

1. ↑ Ernst, A. T; Jiang, H; Krishnamoorthy, M; Sier, D (2004-02-16). "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. ↑ 2.0 2.1 "AI workforce planning for travel and logistics | McKinsey". https://www.mckinsey.com/industries/travel/our-insights/ai-can-transform-workforce-planning-for-travel-and-logistics-companies.
3. ↑ Pinedo, Michael L. (2022) (in en). Scheduling. doi:10.1007/978-3-031-05921-6. ISBN 978-3-031-05920-9. https://link.springer.com/book/10.1007/978-3-031-05921-6.
4. ↑ Algethami, Haneen; Martínez-Gavara, Anna; Landa-Silva, Dario (2019-10-01). "Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem" (in en). 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. ↑ 5.0 5.1 Clancy, Thomas R. (2008). "Independence". The Journal of Nursing Administration 38 (9): 367–370. doi:10.1097/01.NNA.0000323962.37168.a0. PMID 18791419.
6. ↑ 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.
7. ↑ 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. https://lirias.kuleuven.be/bitstream/123456789/123829/1/JOS_.

## 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.

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