About the practice
Summit Analytics was founded on a specific observation: analytics investments fail when the people building them don’t understand the business. Generic BI consultants learn the tool; they don’t learn the supply chain. Summit Analytics exists to do the opposite — go deep on the domain, publish the references and frameworks the work actually needs, and take on engagements selectively.
The practice works where enterprise supply chains meet the lakehouse — demand planning, inventory, procurement, fulfillment, and production — building Databricks data foundations, optimization models, and the day-to-day reporting that planners run on.
What we publish
The resource library is the practice’s primary artifact — free and ungated, no sign-in. Reference databases for the ERP systems supply chains actually run on, with the SAP reference as the flagship; a supply chain KPI dictionary; and cross-ERP modeling guides — plus reference builds that show those definitions running over frozen, simulated data.
- ERP references
- 8 systems · 676 tables
- Supply chain KPI dictionary
- 13 KPIs
- Cross-ERP guides
- 6 guides
- Reference builds
- 14 dashboards
Everything is organized two ways: by business problem — Inventory & Working Capital, Demand Planning & Forecasting, Customer Service & Fulfillment, Procurement & Supply, Manufacturing & Production, Data Foundation & Governance — and by source system, through the ERP references.
How it’s verified
Every reference carries two marks. An update date — the day it last changed — and a Verified month, the month its claims were last re-checked against vendor documentation. The two move independently on purpose: a wording fix is not a re-verification.
Corrections come in through the Feedback button on every reference page, and are used to correct the library. It is free to use in your own work — the terms of use say how.
The principal
Summit Analytics is led by Bryan Schoener, a supply chain analytics practitioner based in the Pacific Northwest. The consulting sits downstream of the library: selective engagements that build the data foundation, stand up the AI on top of it, and train the analysts who run it.
Connect on LinkedIn (opens in new tab) — the company page is on the contact page.
Working on a supply chain analytics challenge?
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A short intro call first; a scoped proposal only if it fits.
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