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Simulation

As explained in Data for All

Simulation is the technique of representing a real or hypothetical system and running it under varying conditions to evaluate possible outcomes.

What might happen if you change one decision, several decisions, or an entire plan? Simulation represents a real or hypothetical system, varies its conditions, and runs possible futures so you can compare outcomes before acting. section 7.2.5 section 7.3.5

A simulation turns assumptions into testable scenarios. You can adjust prices, promotions, products, time boundaries, customers, or other variables, then observe how the simulated operation responds. Repeating this process can reveal an optimal outcome without exposing the real operation to every experiment. section 7.2.5 section 7.3.5

A practical simulation workflow involves:

  • Define primary, secondary, and possibly tertiary objectives. section 7.2.5
  • Set the variables and combinations that represent alternative plans. section 7.2.5
  • Run the scenarios repeatedly, compare their outcomes, and identify where attention or investment should begin. section 7.2.5
  • Keep the results, not only the selected optimum, because every scenario contains potentially valuable data. section 7.2.5

Planning a store network

A company can simulate how many stores to open, which markets to enter, what store mix could maximize profit, and how quickly to invest and build. Managers can also test familiar or novel plans in a safe environment and examine projected market, product, company, or customer performance. section 7.2.5 section 7.3.5

How Data for All covers it

How deep it goes
Explains objectives, variable changes, repeated runs, and safe validation, but does not provide implementation or mathematical detail.
Examples
Uses store-network planning, pricing, promotions, products, customer counts, time boundaries, markets, and digital twins as examples.
What it assumes
Assumes data from prediction and prescription efforts, plus clearly defined objectives and controllable planning variables.
What you can do after
Evaluate plans and assumptions, compare projected outcomes, and identify promising actions before committing resources.
How it connects
Connects simulation with simulated data, synthetic data, prediction, prescription, Monte Carlo simulation, system dynamics, and digital twins.

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This explainer was generated by Manning's knowledge graph from the text of Data for All. The section links point to the parts of the book it draws on.