Overview
Prometric, a global leader in assessment services, delivers certification and licensure exams for leading organizations across industries. To ensure fairness, scalability, and efficiency in exam creation, Prometric implemented IBM® ILOG® CPLEX® Optimization Studio to power its Linear On-the-Fly Testing (LOFT) system.
The challenge
Prometric needed to generate unique exams for each candidate while ensuring:
- Statistical equivalence across all tests
- Balanced coverage of subject domains and subdomains
- Controlled exposure of questions from large item banks
- Real-time performance for test delivery
Traditional approaches—including in-house algorithms and open-source solvers—struggled with increasing complexity. As constraints grew into the thousands, performance became inconsistent and unreliable, limiting scalability and impacting user experience.
The solution
Prometric adopted IBM ILOG CPLEX Optimization Studio to power its LOFT engine.
Using CPLEX, Prometric:
- Models each exam as an optimization problem with thousands of constraints
- Uses binary decision variables to select questions from large item banks
- Ensures statistical fairness by enforcing strict psychometric constraints
- Optimizes item exposure to maximize question bank utilization
The solution integrates seamlessly with Prometric’s proprietary systems for item banking, test assembly, and delivery, enabling both real-time and pre-scheduled exam generation.
The Results
Improved performance and scalability
- Handles up to 10x more constraints compared to previous solutions
- Generates exams reliably where earlier systems failed
Â
Faster exam generation
- Reduced test creation time to seconds (10–20 seconds in real-time scenarios)
- Improved candidate experience with minimal wait times
Â
Greater accuracy and fairness
- Enabled tighter statistical tolerances for exam difficulty
- Delivered more consistent and defensible assessments
Â
Enhanced operational efficiency
- Supports real-time and batch scheduling workflows
- Scales to thousands of candidates with unique test instances
Optimization & AI Projects
| Project | Use Case | Time to Value | ROI / Impact |
|---|---|---|---|
|
Drug Pricing Optimization |
Optimize pricing post negotiations with insurers/pharmacies |
4–8 weeks |
2–5% margin uplift; millions in incremental revenue |
|
Trade Promotion Optimization |
Optimize discount schedules for FMCG promotions |
6–10 weeks |
10–20% reduction in promo waste; 3–7% revenue uplift |
|
Shipping Optimization |
Optimize logistics routes and allocations |
4–6 weeks |
8–15% reduction in freight cost; improved OTIF |
|
Flight Planning & Crew Rostering |
Optimize aircraft routing and crew scheduling |
8–12 weeks |
5–10% cost reduction; improved utilization |
|
Ad Placement Optimization |
Optimize TV ad scheduling for revenue maximization |
3–6 weeks |
5–12% increase in ad yield |
|
Project Management Optimization |
Minimize project completion time (makespan) |
2–4 weeks |
10–25% reduction in timelines |
|
AI Proposal Parsing Agent |
Automate proposal parsing and quote generation |
2–4 weeks |
30–60% reduction in manual effort |
|
AI Evaluation Framework |
Improve performance and reliability of AI agents |
3–6 weeks |
Higher accuracy; reduced failure rates |
|
Video Parsing AI Agent |
Extract insights and answer queries from video content |
3–6 weeks |
40–70% reduction in manual review time |
Supply Chain & Retail Optimization
| Project | Use Case | Time to Value | ROI / Impact |
|---|---|---|---|
|
Slotting / SKU Placement |
Optimize SKU placement in warehouse to reduce travel |
3–6 weeks |
15–30% reduction in travel distance; 10–20% productivity gain |
|
Order Batching / Wave Optimization |
Group orders efficiently for picking |
4–8 weeks |
20–40% higher batch density; 10–25% faster processing |
|
Picker Routing Optimization |
Optimize pick paths within warehouse |
2–4 weeks |
10–25% reduction in picking time |
|
DC-to-Store Replenishment |
Optimize delivery routes and schedules |
4–6 weeks |
10–20% transport cost reduction; fewer stockouts |
|
Network Design / Warehouse Siting |
Optimize warehouse locations and demand allocation |
6–12 weeks |
15–30% logistics cost reduction; improved service levels |
Executive Summary Table
| Category | Typical Time to Value | Typical ROI |
|---|---|---|
|
Pricing & Revenue Optimization |
4–10 weeks |
3–12% revenue uplift |
|
Supply Chain Optimization |
3–8 weeks |
10–30% cost savings |
|
AI Automation |
2–6 weeks |
30–70% effort reduction |
|
Strategic Network Design |
6–12 weeks |
15–30% total cost reduction |
Business impact
By adopting IBM CPLEX, Prometric:
- Improved reliability of its assessment platform
- Enhanced fairness and defensibility of certification exams
- Increased flexibility to support complex client requirements
- Positioned itself to support next-generation testing models, including adaptive and AI-assisted assessments
Looking ahead
Prometric is exploring the integration of AI with optimization to:
- Automate item generation and categorization
- Enable adaptive testing models that adjust in real time
- Reduce manual effort in exam development
The combination of optimization and AI is expected to further enhance efficiency while maintaining the high standards required in regulated assessment environments.
About Prometric
Prometric provides end-to-end assessment solutions, including test development, delivery, and psychometric analysis, serving clients across healthcare, finance, IT, and professional certification sectors.
