Workforce modeling

Workforce modeling is the process of aligning the demand for skilled labor with the availability and preferences of skilled workers (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 scheduling. Staff scheduling is rooted in time management. 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.

Complexity of the model

Many applications providing a workforce modeling solution 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, each with a consistent, predictable outcome. However, modeling the uncertainty and dependability of these events is a well-researched area.^[1] Modeling approaches such as system dynamics have also been employed in workforce modeling to address interdependencies and feedback loops within large organizations, such as NASA.^[2] Heuristics have also been applied to the problem, and metaheuristics have been identified as effective methods for generating complex scheduling solutions.^[1]^[3]

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

Incorporation of AI and Machine Learning

AI's Impact on Employment:

Anthropic CEO Dario Amodei warned that AI could eliminate up to 50% of entry-level white-collar jobs and raise unemployment to 10–20% within five years. This highlights the significant impact AI could have on workforce dynamics. [Source]

Workforce Management Software Market Growth

The workforce management software market is projected to grow by USD 3.67 billion between 2025 and 2029, driven by regulatory compliance and AI-powered market evolution. [Source]

Integration with Financial Planning and Strategic Objectives

Michigan's AI Workforce Plan:

Michigan's Department of Labor and Economic Opportunity released an AI and Workforce Plan aiming to create up to 130,000 good-paying jobs and gain up to $70 billion in economic impact over the next 5 to 10 years. The plan focuses on integrating AI into workforce planning and economic strategies. [Source]

Real-World Applications and Case Studies

Media Industry:

Business Insider laid off approximately 21% of its workforce, aiming to restructure the company towards a more AI-driven future. CEO Barbara Peng stated that over 70% of employees already use Enterprise ChatGPT, with a goal of 100% adoption. [Source]

Notes

^ ^a ^b Clancy, Thomas R. Managing Organizational Complexity in Healthcare Operations. The Journal of Nursing Administration 38.9 (2008): 367–370. Print.
^ Marin, Mario; Zhu, Yanshen; Meade, Phillip; Sargent, Melissa; Warren, Jullie (2007). "Workforce Enterprise Modeling". SAE Transactions. 116: 873–876. ISSN 0096-736X.
^ 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.