Databricks Lakehouse & AI Demand Forecasting for Supply Chain Intelligence

Overview

We built a complete Databricks Lakehouse supply chain intelligence platform with a scalable Delta Lake architecture, bringing together Delta Lake, Genie AI/BI, and Agent Bricks in about 14 weeks. The shift was massive. They went from constantly putting out daily fires to actually looking ahead with AI-driven planning powered by Databricks demand forecasting and autonomous supply chain intelligence.

Our Solution

Unified Data Lakehouse on Delta Lake

: We set up a Medallion Architecture (Bronze to Silver to Gold) directly on the Databricks Lakehouse using Delta Lake. It pulls live data from SAP, their WMS, POS systems, and supplier feeds using Auto Loader. By the time data hits the Gold layer, it's clean and ready for business users.

Lakeflow Pipelines for Real-Time Data Orchestration

: Nightly batch jobs are gone. We swapped them out for Lakeflow Declarative Pipelines. These automatically handle changes in schema and check data quality on the fly, so the ERP syncs continuously and stays fresh within a 15-minute window through a modern real-time data platform.

AI-Powered Demand Forecasting with Genie Code

: Instead of manual guesswork, we used Genie Code to train time-series models for over 4,800 active SKUs. The system looks at seasonality and outside market signals, updating predictions every 24 hours straight into the planners' dashboards inside the supply chain intelligence platform.

Agent Bricks for Autonomous Supply Chain Intelligence

: We set up three AI agents running in the background. They monitor demand risks, watch for supplier delays, and let the team run quick what-if scenarios for upcoming promotions.

Genie AI/BI for Self-Service Analytics

: Now, planners just type their supply chain questions in plain English. No SQL needed. They get accurate, chart-based answers back in under 30 seconds through a modern retail supply chain analytics experience.

Unity Catalog for Governance and Compliance

: We locked everything down with Unity Catalog. Every model, dashboard, and table has strict row-level security and full lineage tracking, completely sorting out their GDPR compliance.

Key Features

Key Platform Features

The core capabilities driving autonomous supply chain intelligence and enterprise AI agents

Unified Lakehouse Architecture

One centralized Delta Lake architecture for supply chain intelligence that replaces four messy, disconnected systems. Everyone finally works from the exact same numbers.

AI Demand Forecasting at SKU Level

FMCG demand forecasting and machine learning models handling 4,800+ SKUs. It automatically picks the right model, refreshes daily, and tracks experiments without human intervention.

Conversational Analytics with Genie

Planners can ask everyday questions about stock or suppliers through Databricks Genie AI and get an immediate visual answer back in seconds using conversational retail supply chain analytics.

Autonomous Supplier Risk Monitoring

A smart agent that watches supplier metrics, figures out how many days of stock are left during a disruption, and suggests reorders for stronger AI-powered supply chain resilience.

Scenario Planning Agent

A real-time engine to test what happens if a promotion spikes demand or a supplier fails, giving planners solid probabilities.

Real-Time Pipeline with Auto Loader

Swapped out clunky overnight batches for continuous data flow from SAP and logistics feeds, keeping numbers fresh within 15 minutes.

End-to-End Data Governance

Unity Catalog handles all the security. It tracks who sees what, masks sensitive columns, and ensures strict GDPR alignment.

Scalable Medallion Architecture

A tiered Databricks Lakehouse data design built to naturally grow and adapt as the company adds more SKUs or expands into new regions.

Technology Stack

Built with Modern Tech

We leverage cutting-edge technologies to build scalable, secure, and high-performance applications that grow with your business.

Databricks

Delta Lake

Databricks Auto Loader

Lakeflow Declarative Pipelines

Genie Code

Databricks Agent Bricks

Unity Catalog

MLflow 3.0

Apache Spark

dbt

SAP Integration

Python

Business Impact

Measurable Results

Real outcomes that transformed our client's operations and delivered significant ROI.

22%

Drop in Inventory Costs

Slashed holding costs in the first six months by fixing forecast accuracy and catching overstock issues early with AI-powered Databricks demand forecasting.

81%

New Forecast Accuracy

Jumped from a 61% baseline to 81%. The new ML tools completely crushed the old system, especially during seasonal shifts.

< 30 Seconds

Query Response Time

Planners used to wait two whole days for an analyst to pull numbers. Now they get answers themselves in seconds through conversational analytics.

31%

Fewer Out-of-Stock Issue

Missed sales dropped significantly across 3,200 retail partners because the system pushed smarter reorder prompts through the AI-powered supply chain platform.

4 to 1

Tool Consolidation

Ditched SAP exports, old WMS reports, a broken forecasting app, and endless Excel sheets for one Databricks environment.

4–6 Days

Early Supplier Warning

The enterprise AI agents catch supplier performance dips days before they actually impact warehouse operations, giving the team time to pivot.

4,800+

SKUs on Autopilot

Pushed highly accurate AI demand forecasting and time-series models live for thousands of products, updating automatically every single day.

14 Weeks

Total Delivery Time

Ripped out the old disjointed infrastructure and launched the governed, AI-driven Databricks Lakehouse in just over three months.

Our Process

Project Delivery Approach

A proven methodology that ensures quality delivery, on time and on budget.

Discovery & Data Audit (Weeks 1–2)

Looked at everything—SAP, WMS, and all the manual Excel sheets. We figured out the data gaps and set clear goals with the supply chain director.

Lakehouse Architecture Design (Weeks 3–4)

Mapped out the Medallion setup on the Databricks Lakehouse using Delta Lake. We also locked in the governance rules and access policies with the IT team.

Pipeline Development & ERP Integration (Weeks 5–7)

Built the Auto Loader feeds and Lakeflow pipelines. We actually got the Gold layer datasets delivered ahead of the deadline.

ML Forecasting & Agent Development (Weeks 8–11)

Trained the AI demand forecasting models using Genie Code and got all three AI agents built, tested, and plugged into the main dashboards.

Genie Spaces & User Enablement (Weeks 12–13)

Set up the Databricks Genie AI workspaces and conversational analytics experiences so they understood the client's specific business lingo. After training, planners were up to speed in under two hours.

Go-Live & Hypercare (Week 14)

Rolled it out regionally first, then went national. We stuck around for two weeks of hypercare and handed over all the playbooks for the new AI-powered supply chain intelligence platform.