Case Studies
The Problem: Plant variances netted and averaged across product lines, masking $2.4M margin leakage. Finance team flagged the variance but could not explain the drivers.
What I Did: Disaggregated cost variances by commodity, SKU, and production line. Reconciled actuals from SAP CO against production system data. Rebuilt standard cost logic and identified gaps in how material master pricing flowed through to variance reporting. Identified three root causes: scrap rates on specific SKUs, purchase price variance on one commodity, and absorption variance from volume mix.
The Artifacts: Variance bridge with drill down capability. PPV tracker by commodity. Scrap analysis by SKU and line. Standard cost governance framework with monthly review cadence.
Outcome: Finance team now explains variances in 2 days instead of 2 weeks. Plant controller has the tools to challenge operations on scrap and mix decisions.
The Problem: Keyword level economics unclear. Pricing set by intuition and competitor benchmarking, not data. Sales team pushing for lower prices, finance pushing back with no model to support either position.
What I Did: Rebuilt unit economics from transaction data. Calculated contribution margin by keyword segment. Tested pricing scenarios with elasticity assumptions. Identified high margin segments being underpriced and low margin segments being overpriced.
The Artifacts: Margin waterfall by keyword segment. Elasticity model with scenario analysis. Pricing recommendation deck with implementation roadmap.
Outcome: Moved from flat $70/lead pricing to a three-tier model ($90/$120/$160) based on keyword intent and geography. Projected 47% revenue increase with no volume growth.
The Problem: Airline balance sheets were optimized for steady-state demand, not a sudden revenue collapse. CFOs needed a clear view of cash runway, financing options, and decision triggers under severe uncertainty.
What I Did: Built a scenario-based liquidity framework from public financials and airline cost structures. Modeled cash burn under three disruption scenarios, then wrote the playbook: maximize cash, slash costs, monetize assets, secure government aid, prepare for restructuring.
The Artifacts: Cash runway scenario model. Carrier risk comparison. Trigger-based decision framework. Five-imperative liquidity playbook (cash maximization, cost actions, asset monetization, government support, restructuring readiness).
Outcome: The retrospective validated the framework — the severe scenario played out almost exactly as modeled.
What I Do
Margin & Variance Analysis
I disaggregate cost variances to find what's driving the numbers. Most finance teams flag the variance but can't explain it. I reconcile actuals against production data, rebuild standard cost logic, and identify the specific SKUs, commodities, or volume mix issues causing the leakage. Then I build the variance bridge and governance framework so the plant controller has data to challenge operations.
Pricing & Unit Economics
I rebuild unit economics from transaction data when pricing is based on intuition. I calculate contribution margin by segment, test elasticity scenarios, and find where you're leaving money on the table. The output is a rigorous pricing model that gets finance and sales on the same page about profitability.
FP&A Infrastructure & Close Optimization
I fix the "47 Excel tabs from three systems that don't talk" problem. I use SQL, Alteryx, and Power Query to automate the data wrangling so your team can analyze instead of copy-paste. The goal is a single source of truth for actuals, automated KPI refresh, and a month-end close that takes days instead of weeks.
SEAN DILLARD