Prescriptive Analytics: From Prediction to Action

The Analytics Evolution

• Descriptive: What happened?
• Diagnostic: Why did it happen?
• Predictive: What will happen?
• Prescriptive: What should we do?

Prescriptive analytics provides recommendations for optimal decisions, considering predictions, constraints, and objectives.

Prescriptive Techniques

Optimization

Find the best solution given constraints.

Linear Programming: Optimize linear objectives with linear constraints. Example: Minimize cost while meeting demand

Integer Programming: Decisions must be whole numbers. Example: How many trucks to deploy

Mixed-Integer Programming: Combination of continuous and integer variables. Example: Route optimization

Simulation

Model complex systems to test scenarios.

Monte Carlo Simulation: Use random sampling to understand outcome distributions. Example: Risk analysis, project planning

Agent-Based Simulation: Model individual actors and interactions. Example: Market dynamics, traffic flow

Discrete Event Simulation: Model sequences of events over time. Example: Manufacturing, service operations

Decision Analysis

Structure complex decisions.

Decision Trees: Map out options and consequences.

Multi-Criteria Decision Analysis: Balance multiple competing objectives.

Real Options: Value flexibility in decision-making.

Use Cases

Supply Chain

• Inventory optimization
• Route planning
• Demand allocation
• Supplier selection

Pricing

• Dynamic pricing
• Promotion optimization
• Revenue management

Resource Allocation

• Workforce scheduling
• Capacity planning
• Budget allocation

Marketing

• Campaign optimization
• Customer targeting
• Channel mix

Implementation Approach

1. Frame the decision: What are you optimizing?
2. Define objectives: What are you trying to achieve?
3. Identify constraints: What limits your options?
4. Model the problem: Mathematical or simulation
5. Solve and analyze: Find optimal solutions
6. Implement and monitor: Execute and learn

Challenges

Model Complexity

Real-world problems have many variables and constraints.

Data Requirements

Need accurate predictions and parameters.

Human Adoption

Decision-makers must trust and use recommendations.

Dynamic Environments

Optimal today may not be optimal tomorrow.

Best Practices

1. Start simple: Add complexity as needed
2. Validate with domain experts: Models should make sense
3. Sensitivity analysis: Understand what matters most
4. Human-in-the-loop: Support decisions, don't replace judgment
5. Monitor outcomes: Are recommendations working?

Prescriptive analytics is powerful but requires careful implementation. The goal is better decisions, not perfect models.

Next Steps

For optimization tools, see Google OR-Tools documentation and IBM Decision Optimization.

Ready to implement prescriptive analytics?

• Explore our Data Analytics services for optimization expertise
• Contact us to discuss your decision support needs