The difference between an organization that knows what it spends and one that knows what it should spend is analytical infrastructure. Building that infrastructure — the models, the frameworks, the dashboards, and the processes that turn raw data into decisions — is some of the most leveraged work in any enterprise. Done right, it compounds: every savings initiative that follows is faster, better-targeted, and harder to dispute.
Micah has built this infrastructure from the ground up at utilities managing billions in annual contracted spend, at consulting firms advising major capital programs, and at startups raising institutional capital. The tools span Excel financial models to Python automation to SQL-based spend analytics — always built for the decision-maker who will use them, not just the analyst who builds them.
Organizations that manage large procurement portfolios often know what they spend at an aggregate level — by vendor, by cost center, by period. What they don't know is what they should spend, which categories are overpriced, which suppliers are underperforming, and where the next wave of savings opportunities is hiding. Micah has built spend analytics infrastructure from raw transaction data: cleaning and normalizing large, messy spend files; categorizing spend against standardized taxonomies; benchmarking category-level pricing against market data; and prioritizing savings opportunities by potential value and ease of capture. At PG&E, this work covered $1.6B in annual contracted gas sourcing spend and formed the analytical foundation for all subsequent category strategy work.
A should-cost model answers a specific question: given the underlying economics of what you are buying — materials, labor, overhead, margin — what should this cost? The answer reframes every negotiation from a positional argument to a data-driven conversation. Micah has built should-cost models for categories ranging from vegetation management services to concentrated solar thermal equipment to EPC contracting for transmission infrastructure. The models incorporate market pricing data, cost driver analysis, and sensitivity analysis that allows procurement teams to understand how the cost will move as market conditions change — and to hold suppliers accountable when it doesn't.
The financial models Micah builds are not templates. They are purpose-built tools designed to answer specific questions: What is the range of savings achievable on this contract? What is the NPV of this sourcing decision under different discount rate assumptions? What is the cost trajectory of this category over a 10-year capital plan? At Raw Solar, the models supported Series A fundraising — sensitivity analyses and scenario models that had to hold up under investor scrutiny. At Power Advocate, they formed the basis for a $14.5B capital demand planning exercise. At PG&E, they tracked and validated $128M in procurement savings against a goal of $95M.
A savings number means nothing if it can't be validated. The frameworks Micah builds define not just the metrics but the methodology: how savings are measured, what baseline is used, how market adjustments are factored in, and how results are reported to senior leadership. At PG&E, this work produced a comprehensive KPI framework for $1.6B in annual gas sourcing spend, a savings tracking system that produced validated results recognized by utility leadership, and the analytical infrastructure that supported reporting to the California Public Utilities Commission. The system achieved 11.6% savings against an 8.6% goal — a gap that would not have been measurable without a rigorous, auditable framework.
Python · R · SQL · Tableau · Excel / VBA · Spark · AWS · SAP · Ariba · PowerAdvocate · Business Objects
Built the analytical infrastructure supporting $1.6B in annual contracted gas sourcing spend at PG&E — including KPI frameworks, cost-to-serve models, and savings tracking systems. Validated $128M in procurement savings against a goal of $95M over the engagement.
Evaluated and prioritized savings opportunities across 33 procurement categories from a $4.3B addressable spend base. Built the scoring model and analytical framework that supported a multi-year strategic sourcing roadmap.
Developed the financial models and scenario analyses underlying a $14.5B, 10-year capital investment plan for a major North American utility, incorporating demand forecasts, labor availability modeling, and supplier capacity constraints.
Built the full suite of financial models for Raw Solar's Series A fundraising — including sensitivity analyses, PPA vs. equipment sales scenarios, and capital return projections — designed to hold up under institutional investor scrutiny.