Speaker
Description
Traditional workforce analytics relies on aggregate metrics that obscure individual-level dynamics and wash out critical correlations between employee characteristics, career trajectories, and compensation outcomes. This talk presents a Workforce Digital Twin methodology using individual-level stochastic simulation to model strategic gender pay equity interventions in a large global organisation.
The digital twin simulates year-by-year workforce dynamics where employees probabilistically experience turnover, hiring, promotions, lateral moves, and demotions based on empirically-derived transition probabilities from organisational data. By constructing enhanced Markov chains that capture cross-sectional correlations across multiple workforce dimensions simultaneously—gender, career group, tenure, remuneration—the model compensates for limited longitudinal history, enabling robust strategic planning despite shorter historical periods.
Applied to gender pay gap compliance planning under Australian workplace equality regulations (WGEA), the simulation projects workforce composition and compensation trajectories over a 12-year horizon. Multiple intervention scenarios are tested by adjusting transition probabilities to reflect strategic hiring, advancement, and retention initiatives. The simulation reveals counterintuitive intervention opportunities that remain hidden when using traditional aggregate workforce metrics.
This evidence-based approach provides organisational decision-makers with quantifiable pathways to achieve regulatory compliance targets, informing strategic resource allocation for equity programmes. The presentation demonstrates how R enables enterprise-scale workforce simulation that bridges statistical rigour with practical strategic planning. This methodology is transferable to other organisational challenges including succession planning, skills development, and workforce transformation, offering the R community a framework for individual-level organisational modelling.
If you used AI tools or services to support the preparation of this submission, please state the name and reason for using each of them.
Yes, I used Claude to summarise modelling and outcomes of the use case and to obtain the abstract for this presentation.
Additional Material or Paper
An abstract on related methodology has been submitted to Rencontres R 2026 (under review). This represents the first presentation of this work.
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | Workforce Digital Twin, Stochastic Simulation, Gender Pay Equity, Markov Chains, Organisational Modelling |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Video Recording | Please don't share recordings of my talk |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |
| Interested in serving as reviewer? | No |