Planning for uncertainty with simulation and optimization
How companies can move beyond point forecasts by combining scenarios, predictive models and optimization to improve decisions under volatile conditions.
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Analytics, data and decision systems
Turn data, analytics and decision systems into clearer insight, stronger choices and more effective execution.
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Articles
From dashboards to decision systems
Why the next frontier in analytics is not more reporting but better decisions�supported by integrated data, explicit decision logic and continuous performance feedback.
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What is actually driving the number?
Advanced analytics should separate the factors behind performance rather than provide increasingly sophisticated descriptions of the outcome.
Does the model understand how your industry works?
Analytical performance depends on representing the economics, constraints and relationships that actually determine outcomes within the sector.
Strategic challenges
More external data can create less certainty
Alternative datasets often contain hidden sampling, coverage and methodological limitations that become dangerous when their precision is overstated.
Most performance systems look backward
Historical reporting remains dominant even when the decisions managers face depend on drivers, scenarios and changing future conditions.
POV
A KPI without a decision is just a number
Performance management improves when every important measure has a clear purpose, owner and consequence for action.
Moving data is easy. Engineering it well is not.
A data platform earns its value through reliability, adaptability and consumption, not through the number of technologies in its architecture.
Strategic impact
Optimization makes trade-offs explicit
Objectives and constraints can be represented directly, allowing competing uses of resources to be evaluated within the same analytical problem.
Domain knowledge improves analytical specification
Understanding the sector helps identify relevant variables, relationships and constraints before statistical methods are applied.
What we observe
Novel data is often mistaken for useful data
We frequently see unusual datasets valued for originality before anyone tests whether they improve explanation, prediction or decisions.
Too many dashboards compete for attention
We frequently see different teams reporting similar outcomes through inconsistent measures, definitions and interpretations.