Our Ops Research (OR) service helps organizations make better, data-driven operational decisions using quantitative modeling, optimization, and simulation. We apply methods from optimization, stochastic modeling, simulation, and analytics to reduce costs, improve throughput, and quantify trade-offs for complex operational systems.
Statement of Work:
- Initial discovery and problem scoping with stakeholders
- Data collection, validation, and feature engineering for modeling
- Exploratory data analysis and baseline performance assessment
- Mathematical modeling and formulation (LP, MILP, stochastic models)
- Simulation and what-if analysis (discrete-event, Monte Carlo)
- Optimization solution development and prototyping
- Policy design and decision rules (e.g., scheduling, routing, inventory)
- Model validation, sensitivity analysis, and robustness checks
- Implementation guidance and integration plan (APIs, dashboards, workflows)
- Knowledge transfer, training sessions, and technical handover
Deliverables:
- Written problem definition and objectives with KPIs
- Cleaned and documented dataset used for modeling
- Analytical models (code + mathematical formulation) and notebooks
- Simulation artifacts and scenario experiment results
- Optimized decision rules and recommended policies
- Interactive dashboards or reports summarizing results and trade-offs
- Implementation blueprint (deployment steps, API examples)
- Model validation report and sensitivity analysis
- Training materials and post-engagement support plan