Retail & E-commerce
UK
14 WeeksDatabricks Lakehouse & AI Demand Forecasting for Supply Chain Intelligence
We built a Databricks Lakehouse with AI demand forecasting and Genie AI/BI for a UK FMCG brand, cutting costs 22% and fixing supply chain blind spots.
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.
The Challenge
Client Overview
The client is a major UK consumer goods brand pulling in £400M a year. They handle 14 different product categories across three continents. Thanks to organic growth and a couple of acquisitions, they got big fast. But that rapid growth created a mess behind the scenes. They were running enterprise-level logistics on an outdated, disjointed data setup that belonged in the past.
Key Pain Points
- There was no single source of truth. SAP, the WMS, some old forecasting tools, and random spreadsheets all showed different numbers. The analytics team burned 2 to 3 days every single week just trying to make the math match up.
- Forecast accuracy was stuck at a grim 61%. This caused massive overstocking for slow-moving items and painful stockouts for the fast sellers, tying up working capital.
- They had absolutely zero visibility into supplier risks. If a key supplier went dark, the business had no way to figure out how long their inventory would hold up.
- Planners couldn't get their own answers. Even simple questions meant logging a ticket with the data team and waiting around two days.
- The old forecasting software just used basic moving averages and static rules. It didn't factor in market signals, seasonality, or anything involving real machine learning or FMCG demand forecasting.
- Massive compliance and governance holes. With US expansion coming up, lacking an audit trail, proper access controls, or data lineage was a massive red flag.
Our Solution
Unified Data Lakehouse on Delta Lake
Lakeflow Pipelines for Real-Time Data Orchestration
AI-Powered Demand Forecasting with Genie Code
Agent Bricks for Autonomous Supply Chain Intelligence
Genie AI/BI for Self-Service Analytics
Unity Catalog for Governance and Compliance
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.
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
PythonMeasurable Results
Real outcomes that transformed our client's operations and delivered significant ROI.
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.
New Forecast Accuracy
Jumped from a 61% baseline to 81%. The new ML tools completely crushed the old system, especially during seasonal shifts.
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.
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.
Tool Consolidation
Ditched SAP exports, old WMS reports, a broken forecasting app, and endless Excel sheets for one Databricks environment.
Early Supplier Warning
The enterprise AI agents catch supplier performance dips days before they actually impact warehouse operations, giving the team time to pivot.
SKUs on Autopilot
Pushed highly accurate AI demand forecasting and time-series models live for thousands of products, updating automatically every single day.
Total Delivery Time
Ripped out the old disjointed infrastructure and launched the governed, AI-driven Databricks Lakehouse in just over three months.
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.
