We frame complex operational problems and build models that leadership can use to compare courses of action.
We apply optimization methods to scheduling, resource allocation, and process design so organizations can do more with constrained people, aircraft, funding, or time.
Example work includes optimizing Navy squadron scheduling from aircraft, personnel, and training requirements, producing a repeatable plan that saved hundreds of hours each year.
We translate technical model results into clear recommendations for program managers and executive decision-makers.
We develop and maintain independent government cost estimates and life-cycle cost models for large defense and IT programs.
We have led cost estimating for a U.S. Army human resources and payroll modernization program valued at more than $3.5 billion over 20 years, including Program Office Estimate updates in ACEIT.
We turn technical and functional requirements into work-breakdown structures, estimating tasks, and inputs to the Planning, Programming, Budgeting, and Execution (PPBE) process.
We coordinate cost positions and briefings so estimates stay defensible when leadership, cost agencies, and budget cycles change.
We analyze manpower, personnel, and training supply chains to show where organizations will be over- or under-resourced.
We have led teams supporting Navy manpower assessments, including models covering more than 250,000 positions and forecasts one to five years forward.
We build tools that compare current personnel data with future budget impacts and highlight under-programming and over-programming risk before it becomes a shortfall.
Results have supported specific policy changes to avoid future manning gaps and reduce recurring analysis time by a large margin.
We use statistical methods, machine learning, and predictive models when they improve the decision, not as an end in themselves.
We have applied machine learning and Markov modeling to student-funding requirements, helping avoid misallocation on the order of $225 million per year.
We automate monthly descriptive and predictive manpower reporting so updates that once took weeks can be produced in hours.
We validate models against independent methods so leadership can see whether a new technique is trustworthy enough to use.
We design simulations and analytical models to test options that are too costly, too slow, or too risky to try first in the real world.
Our modeling work includes large-scale simulation of unmanned-system concepts, including more than 1.5 million runs using high-performance computing and designed experiments.
We use simulation output to identify when a concept is viable and under which conditions it is most effective.
We also build risk and error-detection tools that compare databases and budget changes, flagging inconsistencies and imbalance before they reach a decision brief.
We manage the full analytics lifecycle: business understanding, data mining, cleaning, validation, analysis, visualization, and deployment.
We migrate and repair aging databases, including forensic cleanup before systems are moved to current platforms.
We design secure reporting environments in tools such as Power BI, Tableau, and Qlik, including role-based access so leaders can see the enterprise view while components see only their own data.
We deliver dashboards and briefings that make the story in the data usable for executives, not just stored in another spreadsheet.
