Mining Workforce Digital Twin: Simulate Before You Commit
- Maturity
- L5
- Domain
- Plan & Cost
- Analytics
- prescriptive
- Scenario feasibility
- 2 of 4
- Sponsor
- Chief Operating Officer
- Confidence
- High
The situation
Under a given production, roster, contractor or automation scenario, what workforce does IronPeak need, can it get there, and does it remain available, safe and fatigue-sustainable throughout?
The recommendation on the table
Make the twin a required gate for major production, roster, contractor and automation decisions
Decisions sequenced to stay feasible, avoiding availability/safety/fatigue failures discovered post-commitment.
Trade-offAdds a step before commitment and depends on the upstream MIN-01–04 data being in place.
The evidence
IronPeak's largest workforce decisions — production ramps, new remote sites, roster redesign, contractor strategy, automation rollout — are made with assumed, not simulated, consequences for availability, safety, fatigue and continuity. The integrative capstone and the portfolio's fourth L5 workforce twin, MIN-05 consumes MIN-01 (baseline), MIN-02 (availability), MIN-03 (safety/fatigue) and MIN-04 (capability) to simulate the workforce under operational scenarios before the capital and roster commitment — with availability, safety and fatigue as hard constraints. It found the fastest contractor-reduction scenario breaks availability mid-transition, a compressed-roster ramp tips two crews past the fatigue threshold, and a scenario fragile under a modelled weather/churn shock.
Mining Workforce Digital Twin
Simulate the workforce under scenarios with hard availability, safety and fatigue constraints.
Key takeawayOnly the base and staged scenarios are feasible; aggressive paths break a hard constraint.
| Availability | Safety | Fatigue | Cost | |
|---|---|---|---|---|
| Base | 1 | 1 | 1 | 1 |
| Staged contractor | 2 | 1 | 2 | 1 |
| Fast cut | 3 | 2 | 2 | 1 |
| Compressed ramp | 2 | 2 | 3 | 2 |
Key takeawayBreach severity (3 = infeasible). The fast cut breaks availability; the compressed ramp tips fatigue.
Key findings
Simulating IronPeak's options shows the fastest contractor-reduction scenario breaks availability mid-transition and a compressed-roster ramp tips two crews past the fatigue threshold — failures that, without the twin, would surface only after the capital was committed.
What we can’t claim
The most cost-attractive scenarios are often the ones that breach a hard constraint, and some are feasible only in calm conditions — losing production under a modelled weather/churn shock. The uncomfortable truth the twin enforces is that availability, safety and fatigue are not tradeable for savings: a scenario that breaches any of them is infeasible regardless of its financial appeal, and must be re-sequenced rather than forced.
Recommendations
Make the twin a required gate for major production, roster, contractor and automation decisions
high priorityDecisions sequenced to stay feasible, avoiding availability/safety/fatigue failures discovered post-commitment.
Trade-off
Adds a step before commitment and depends on the upstream MIN-01–04 data being in place.
Commit only constraint-feasible scenarios; stage the rest
high priorityTransformation paths that hold availability and safety throughout, not just at the endpoint.
Trade-off
The most cost-attractive scenario is sometimes infeasible and must be slowed, which requires discipline.
Analytical framework
How we reached this
Prescriptive simulation — model the workforce consequence of production/roster/contractor/automation scenarios (feasibility, availability, safety, fatigue, continuity, cost) before commitment.
ConfidenceMedium
Analytical framework
How we reached this
Prescriptive simulation — model the workforce consequence of production/roster/contractor/automation scenarios (feasibility, availability, safety, fatigue, continuity, cost) before commitment.
Methods applied
Statistical techniques
Algorithms
Data sources
Outputs generated
Why this confidence
The most advanced method in the Mining portfolio, yet bounded by assumption load; outputs are always ranges against stated assumptions (sophistication and confidence are independent). The three hard constraints are non-negotiable.
The reasoning
Business context
The most advanced Mining project, consuming all upstream Mining work, sponsored by the COO because its outputs gate the largest production, roster, contractor and automation decisions. It composes the upstream outputs into scenarios and adds only simulation and constraint-trajectory facts — the no-orphan rule expressed in the twin.
Expected value
De-risked ramp and roster capital, avoided mis-sequenced transformations, and avoided availability/safety/fatigue failures that would otherwise surface only after commitment. A scenario that meets production by endangering or exhausting the workforce is not a feasible scenario.
Workforce landscape
The fastest contractor-reduction scenario breaks availability mid-transition (infeasible as drawn); a compressed-roster ramp meets output but pushes two crews past the fatigue threshold (unsustainable); a scenario holds in base conditions but loses production under a modelled weather/churn shock (fragile). The base scenario is feasible and resilient.
The analytics journey
Level 5, prescriptive. Discrete-event/operations simulation with Monte Carlo and optimisation under three hard constraints — availability, safety, fatigue. It compares scenarios rather than predicting one future; every output is a range with assumptions, and a scenario breaching any constraint is infeasible regardless of cost benefit.
Under the hood
A discrete-event/operations simulation models production and crews across sites and roster cycles against available, qualified capacity; Monte Carlo over uncertain drivers (availability disruption, fatigue, contractor churn, weather) produces outcome distributions; linear programming finds feasible roster/mobilisation paths under hard availability, safety and fatigue constraints, with bio-mathematical fatigue as a simulation input.
Confidence & evidence
Why you can rely on this
The inconvenient truth
The most cost-attractive scenarios are often the ones that breach a hard constraint, and some are feasible only in calm conditions — losing production under a modelled weather/churn shock. The uncomfortable truth the twin enforces is that availability, safety and fatigue are not tradeable for savings: a scenario that breaches any of them is infeasible regardless of its financial appeal, and must be re-sequenced rather than forced.
Method
Confidence is a deterministic read of KPI strength, target and benchmark coverage across this project — shown on an illustrative reference dataset, computed the same way it would be on live data.
Take this further
Where this project connects
Related projects
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