Decision and consequence
We start with the last real decision: what triggered it, who prepared the analysis, who approved the plan and what happened when the plan was weak.
How we work
The most valuable work is not drawing a dashboard. It is defining the decision, constraints and validation test that show whether the model reflects operational reality.
We start with the last real decision: what triggered it, who prepared the analysis, who approved the plan and what happened when the plan was weak.
We define the objective, resources, alternatives, constraints, dependencies, uncertainty and data that actually affect the decision.
We document how the decision is made today. Without a baseline, we cannot honestly claim that the new model is better.
We select optimisation, simulation, MCDA or a combination only because it fits the problem. We test scenarios and trade-offs.
We validate outputs with users, test sensitivity to changing inputs, document model limitations and identify conditions under which the recommendation no longer holds.
If the decision repeats, we connect the model to data and a focused interface. If it does not repeat, we do not build software without a reason.
Validation discipline
The model should be compared with the current process, not only with an idealised scenario.
We need to know how the conclusion changes when assumptions, priorities or data change.
We explicitly document where the model should not be used and which assumptions must remain valid.
The accountable person retains the final decision. The model is a testable basis, not an autonomous authority.
XAIOR
For important or contested decisions, natural language can help users understand drivers, trade-offs, sensitivities and reasons behind the output. AI does not replace the solver, optimisation model or simulation.
The current workflow is the best starting point for a model that can be tested.