Atlas Analytics helps organizations make better decisions under uncertainty using operations research, probabilistic models, and statistical tools. Fixed scope. Quantitative rigor. Actionable output.
Request an EngagementWe have a specific point of view on what we do well. This clarity is how we deliver results in three weeks instead of three months.
Atlas Analytics is a focused practice. We do one thing: build quantitative decision support tools that help organizations act with confidence under uncertainty. We don't do strategy generalism, IT implementation, or broad advisory work. If you need a model, a framework, or a simulation — we're the right call. If you need a hundred-slide deck, we're not.
Open-ended engagements that deliver slide decks, not probabilistic models. Generalist teams who don't specialize in causal inference.
Correlation-based risk scores and real-time alerts. They tell you something is wrong. They can't tell you which intervention reduces risk the most.
Most procurement teams still manage risk with heat maps and qualitative assessments. No quantification of disruption propagation paths.
Between expensive generalists and shallow dashboards, there's no focused offering that delivers causal risk intelligence in a fixed timeframe.
A three-week, fixed-scope Bayesian supply chain risk assessment delivered to procurement and risk teams at large organizations. Causal modeling that maps disruption propagation paths, quantifies scenario probabilities, and ranks interventions.
Bayesian network mapping your supply chain's causal risk structure with quantified node probabilities.
Posterior probability distributions across disruption scenarios, with propagation path analysis showing how risk cascades through your commodity network.
Interventions ranked by risk reduction impact. Clear recommendations with quantified confidence intervals.
Beyond SRIT, we offer specialized analytics engagements tailored to specific decision problems.
Capacity optimization, renewable integration modeling, and demand flexibility analysis for utilities and regulators.
Focused two-week engagements applying operations research to specific operational bottlenecks. Clear problem, clear output.
Decision science workshops for leadership teams. Bayesian thinking, scenario analysis, and quantitative risk frameworks.
Grid expansion planning, renewable integration risk, and supplier resilience for critical infrastructure. Grounded in published energy systems research.
Supplier disruption modeling for large procurement organizations. Move from heat maps to quantified causal risk models that inform sourcing decisions.
Decision support for state and local government agencies. Policy simulation, resource optimization, and evidence-based planning under uncertainty.
Dr. Mark Rodgers is a Senior Lecturer at Columbia Business School and former faculty at Rutgers University, where he led research in operations research, energy systems, and supply chain analytics. His work applies Bayesian causal modeling, generation expansion planning, and network optimization to real decisions — for procurement organizations managing supplier risk, utilities navigating capacity uncertainty, and regulators designing energy policy. Published in Applied Energy, Energy Policy, IEEE, and Computers & Industrial Engineering.
Atlas Analytics exists to make that difference measurable.
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