How the practice works

The practice builds AI-ready data foundations, stands up supply chain AI on top of them, and trains the analysts who run it. Databricks first; Snowflake supported.

Most supply chain AI stalls for the same reason: the model is fine, the data underneath it isn’t. Genie rooms and Cortex agents answer only as well as the schemas they read. So every engagement follows the same arc — build the foundation, deliver the AI, enable the team. Consulting is selective, and the library is the working knowledge the engagements are built on.

  1. Step 1 — The prerequisite

    AI-Ready Data Foundation

    A governed lakehouse your AI can trust

    AI gives wrong answers on top of ungoverned data — confidently. We build the layered foundation that makes it answer supply chain questions correctly: medallion architecture from raw ERP sources through conformed entities into Kimball star schemas — the shape BI tools and AI read correctly — governed in Unity Catalog. Databricks first; Snowflake where that's your platform.

    Capabilities

    • Medallion architecture build-out — bronze, silver, and gold layers from SAP and other enterprise sources
    • Kimball star schema design: facts, conformed dimensions, surrogate keys
    • Unity Catalog governance — access control, lineage, and the documented semantics AI depends on
    • Data pipelines in SQL and Python, with the metric and semantic-layer definitions that keep AI answers consistent with your KPIs

    The layer contracts, published

  2. Step 2 — The payoff

    Supply Chain AI Delivery

    Working AI over inventory and demand-planning data — shown before you commit

    We stand up the AI itself: Databricks Genie rooms where planners ask inventory and demand questions in plain language, AI/BI dashboards for the KPIs supply chain leaders track, and Snowflake Cortex where that's your warehouse. Demo environments mean you see it working before you commit to a rollout.

    Capabilities

    • Databricks Genie rooms scoped and curated for inventory, demand-planning, and supply-planning domains
    • Databricks AI/BI dashboards for supply chain KPIs — MEIO, safety stock, and S&OP reporting frameworks
    • Snowflake Cortex and Cortex Analyst delivery, and Tableau and Power BI where AI extends existing reporting
    • Demo environments and proof-of-concept builds for enterprise evaluation
    See the reference builds

    One of the reference builds

    Demand Forecast Accuracy

    Forecast vs. actual units · 28 months

  3. Step 3 — The multiplier

    Analyst & Team Enablement

    Analysts who are effective alongside the AI

    AI doesn't replace your analysts — it changes what the good ones do. We train teams to curate the data AI reads, verify the answers it gives, and ship the work it accelerates. The material comes from the same place as the free resource library, adapted to your data model.

    Capabilities

    • Private workshops: Databricks SQL, Genie curation, Tableau, and Power BI
    • Custom reference material and playbooks built around your data model, turning AI-assisted analysis into team standards
    • SAP and supply chain data-model onboarding for new analysts

    The published references

    SAPJDEDynamics 365 F&OInfor M3and 4 more

What selective means

A fit check, not a sales process. The practice works across demand planning, inventory, procurement, fulfillment, and production, with enterprise supply chain organizations that have invested in data infrastructure and aren’t getting the value out of it.

Engagements usually start small — a demo environment over inventory or demand-planning data, so the AI is working before a rollout is scoped.

What to expect

  1. A brief intro conversation about your data landscape
  2. Honest assessment of whether Summit Analytics is the right fit
  3. If so, a focused proposal scoped to your specific needs

Working on the foundation, the AI, or the team?

Consulting availability is selective — every inquiry is read and answered.

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