Most analytics firms list industries. What actually determines whether a problem is solvable is its structure: competing constraints, limited resources, decisions under uncertainty. Those structures repeat across sectors, and they are exactly what Ben's published research addresses. Here is where that work applies most directly.
Health systems run on fixed resources and rising demand. The hard questions are allocation questions: how to get more capacity, throughput, and coverage from staff, beds, and equipment that cannot simply be scaled up.
We build optimization models that quantify tradeoffs between competing uses of the same fixed resources, then test them against real operational data before anything changes on the ground.
Manufacturing complexity compounds: scheduling, sequencing, quality, and workforce planning all interact, and small inefficiencies multiply across volume. The gains come from modeling the whole system, not tuning one piece.
Scheduling under many competing constraints is a well-defined class of optimization problem. We formulate it explicitly, solve it, and hand back a model your team can rerun as conditions change.
When the job is positioning limited, high-value resources across geography to minimize response time or cost, intuition breaks down fast. This is one of the most studied problems in operations research, and one of the most directly transferable.
Location and dispatch problems have rigorous mathematical formulations. We adapt them to your constraints, whether that is service-level targets, cost ceilings, or coverage requirements, and validate against your data.
The industries above are where the methods apply most obviously. They are not the limit. If your organization runs on constrained resources and high-stakes decisions, there is likely a model worth building.
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