Entrada

The Challenge

Executive Summary

A leading food distribution company, faced significant challenges with understocking and overstocking, leading to potential revenue losses of $15 million annually.

Company Background

With a complex supply chain involving multiple suppliers, warehouses, and fluctuating consumer demand, the company struggled with:

Understocking, causing lost sales due to unfulfilled orders.
Overstocking, leading to spoilage of perishable goods and increased storage costs.
Fragmented data systems, which hindered real-time decision-making and forecasting accuracy.
Regulatory compliance challenges due to inconsistent data governance across regions.

These issues contributed to an estimated $15 million in annual revenue losses from inefficiencies and missed opportunities.

Challenges

Inventory Imbalances: Inaccurate demand forecasting led to frequent understocking and overstocking, impacting customer satisfaction and profitability.
Data Silos: Legacy systems, including on-premises data warehouses and disparate ETL tools, created fragmented data environments, slowing down analytics and decision-making.
Risk Identification: Lack of real-time analytics made it difficult to detect supply chain disruptions, such as supplier delays or demand spikes, in a timely manner.
Governance and Compliance: Inconsistent data access controls and auditing processes posed risks of non-compliance with food safety and regional regulations.

Solution

Entrada with Databricks helped to modernize its data infrastructure, leveraging the Databricks Data Intelligence Platform, including AI, Unity Catalog, and a structured migration strategy. The implementation was executed in three phases:

Phase 1: Migration to Databricks

Entrada migrated legacy data warehouse and ETL pipelines to the Databricks Lakehouse Platform. Key steps included:

Automated Code Conversion: Using Databricks’ automated tools, legacy SQL and ETL scripts were converted to Databricks SQL, saving over 80% of development time.
Delta Lake Integration: Historical sales, supplier, and inventory data were migrated to Delta Lake, enabling scalable and efficient data processing for real-time analytics.
Data Validation: Parallel testing and reconciliation tools ensured data integrity during migration, maintaining consistency with legacy systems.

Phase 2: Implementing Unity Catalog for Governance

Unity Catalog was deployed to centralize data governance and ensure compliance across customer’s operations. Key features included:

Centralized Metadata Management: Unity Catalog provided a single source of truth for data assets, including tables, AI models, and dashboards, streamlining access control and auditing.
Role-Based Access Controls: Data access was restricted based on user roles, ensuring compliance with food safety regulations and protecting sensitive supplier data.
Data Lineage and Auditing: Unity Catalog’s lineage tracking enabled customer to monitor data flows, ensuring transparency and regulatory compliance.

Phase 3: AI-Driven Demand Forecasting

Databricks’ AI and machine learning capabilities were leveraged to build a robust demand forecasting model to address understocking and overstocking. Key components included:

Machine Learning Models: Using Databricks MLflow, Entrafa developed predictive models that analyzed historical sales, seasonal trends, weather data, and external factors like economic conditions.
Real-Time Analytics: Databricks SQL and Delta Live Tables enabled real-time processing of IoT data from warehouses and supplier feeds, detecting anomalies such as supply chain delays or demand spikes.
AI Functions: Allowed analysts to query ML models directly from SQL, embedding AI insights into daily workflows for rapid decision-making.
Mosaic AI: Utilized Mosaic AI for scalable model serving, ensuring forecasts were updated in real time to reflect changing market conditions.

Results

The Databricks implementation delivered measurable outcomes for:

Data Democratization: Unity Catalog and Databricks SQL empowered non-technical users, such as business analysts, to access and analyze data, reducing reliance on data science teams.

Revenue Savings: Optimized inventory management reduced understocking and overstocking, saving $15 million annually by minimizing lost sales and spoilage costs.

Risk Mitigation: Real-time anomaly detection identified supply chain risks, such as supplier delays or unexpected demand fluctuations, enabling proactive mitigation.

Cost Efficiency: Migration to Databricks reduced infrastructure costs by 50% compared to legacy systems, with serverless computing and Delta Lake improving scalability.

Improved Governance: Unity Catalog ensured compliance with food safety regulations, with automated auditing and role-based access controls reducing compliance risks.

Enhanced Collaboration: Delta Sharing facilitated secure data sharing with suppliers, improving supply chain agility and reducing lead times by 20%.

About Entrada
Entrada is a Databricks-focused consulting and implementation partner backed by Databricks Ventures. Entrada harnesses the power of Databricks to help customers accelerate their AI + data initiatives. Our expertise in AI/ML, Databricks, and analytics is centered around industry-centric solutions. Our mission is to simplify complex data + AI challenges and support end-to-end transformations, delivering future-ready solutions fast.