Method
Closed-loop learning
Use observed outcomes to propose and evaluate improvements.
Closed-loop learning explained
What it is
Closed-loop learning feeds actuals, forecast error and decision outcomes back into the models and decision rules.
Why it matters
Systems that never learn from their mistakes repeat them; the value is in how fast the system notices it is wrong and recovers.
Where planning teams fail
Forecasts and decisions are made, but outcomes are rarely fed back systematically, so the same errors recur.
How RabbitHawk applies it
RabbitHawk retains forecasts, decisions and observed outcomes to propose and evaluate improvements. Material changes are validated and versioned before deployment, with no-change and rollback options.
Related methods
Keep exploring
- probabilistic forecasting
Probabilistic forecasting
Represent ranges and likelihoods alongside the central forecast.
- hierarchical reconciliation
Hierarchical reconciliation
Make forecasts coherent across the specified aggregation structure.
- forecast bias diagnostics
Forecast bias diagnostics
Find the systematic errors quietly distorting your plans.
Apply this to your data
See how RabbitHawk would use this method to improve your decisions.
Request a decision-intelligence briefing