3. Prescriptive Analytics
Select feasible actions under uncertainty, cost, capacity and causal effects
3. Prescriptive Analytics
Prescriptive analytics combines forecasts or effects with objectives and constraints. Its output is an action, policy or allocation—not merely a higher score.
Learning outcomes
After this module, you can:
- compare actions with expected value, regret and scenario sensitivity;
- formulate a linear optimisation model with auditable units;
- convert demand uncertainty into inventory and allocation decisions;
- use queueing and simulation without treating assumptions as observations;
- connect pricing to contribution, capacity and causal demand response;
- distinguish risk targeting from treatment-effect targeting.
Chapter route
| Chapter | Decision | Main output |
|---|---|---|
| Decision under Uncertainty | act now with uncertain states | payoff and sensitivity table |
| Linear Optimisation | allocate scarce resources | objective, constraints and solution audit |
| Inventory and Allocation | balance shortage and excess | order quantity or allocation policy |
| Queues and Simulation | choose capacity under congestion | waiting/service distribution |
| Pricing and Revenue Management | set price or protect capacity | contribution-aware policy |
| Causal Targeting | intervene where action changes outcomes | net-uplift policy |
Prepare, work, follow up
- Prepare: write a decision variable, one objective and two constraints from a real allocation problem.
- Workshop: solve the HarborMart model by hand, then vary one uncertain input until the chosen action changes.
- Follow up: write an implementation rule, override and rollback condition—not just an optimum.
Students may use a spreadsheet or inspect the supplied enumeration; solver software is not required.