Content deleted Content added
Spookydonuts (talk | contribs) ce: fix deleted punctuation and half-word |
m Reverted edit by 2601:2008:FCE:9E91:E255:315E:9DA1:F227 (talk) to last version by OAbot |
||
(25 intermediate revisions by 17 users not shown) | |||
Line 1:
'''Workforce modeling''' is the process of aligning the
This approach
▲'''Workforce modeling''' is the process of aligning the [[demand]] for skilled labor with the availability and preferences of [[skilled workers]] ([[Supply and demand|supply]]). It uses mathematical models to support tasks such as [[sensitivity analysis]] , scheduling, and workload forecasting.
▲This approach is commonly applied in industries with complex labor regulations, certified workers, and varying levels of demand, including healthcare, public safety, and retail. Workforce modeling solutions often include software tools that help determine staffing needs based on workload volume across different periods, such as times of day, days of the week, or seasonal cycles.
==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" />
==Complexity of the model==
Many applications providing
==
{{reflist}}
==Further reading==
Line 24 ⟶ 19:
[[Category:Management systems]]
[[Category:Human resource management]]
[[Category:Workforce]]
|