Enhancing profitability with fine-grained model

Client Background

NKT Photonics manufactures advanced lasers across multiple product lines, including defense. We began in the finance function and later engaged IT and engineering. Existing Alteryx and Power BI workflows could not support the required depth and granularity for profitability analysis.

Aspiration

Create an end-to-end, highly granular profitability model so leadership can see revenue and fully loaded costs at product and product-line level, identify inefficiencies, and steer pricing, portfolio, and operations.

How We Did It

Profitability model in Snowflake and dbt: Set up a governed data backbone capable of product-level detail.
CFO co-development: Worked side by side to codify revenue recognition, cost drivers, and allocation logic.
Detailed cost modeling: Incorporated BOMs, labor, overhead, service, rebates, and freight to reach true unit economics.
Validation and scenarios: Reconciled to financials, added what-if views for pricing, mix, and cost changes.

Rollout framework:

Strategy and alignment workshops with business and tech
Technical discovery with domain teams
Foundation rollout for infra, security, CI/CD, and branching
Enablement and onboarding for users and developers
Continuous improvement for migration, maintenance, and support

Migration at scale: An agentic migration assistant to port MS SQL objects into dbt, first 1:1, then automated refactoring, delivering an estimated 80% speed-up versus prior manual approaches.

Key Outcomes

Product-level visibility: Clear unit economics by product and line.
Actionable optimization: Exposed inefficiencies, improvement opportunities, and candidates for phaseout.
Transaction-ready transparency: A robust view of profitability that supported ownership transition and executive decision-making.
Future-ready: A strong base for analytics, ML, and automation across multiple domains.