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A leading grocery retailer aimed to introduce intelligent automation and AI into its produce supply chain to ensure customers receive the freshest products in stores. The organization partnered with DC Tech to modernize its data platform and implement advanced analytics solutions. By leveraging real-time insights and machine learning capabilities on modern cloud technologies, DC Tech delivered a scalable solution that improved inspection accuracy, enhanced operational efficiency, and helped reduce operational costs across the supply chain.
Retail
Integrated data workflows, robust data governance, AI-driven capabilities and next-generation data infrastructure transformation.
Databricks, Machine Learning, AI, Cloud
A large grocery retailer wanted to improve the efficiency of its cold-chain operations to ensure that temperature-sensitive products such as dairy, meat, and fresh produce remained fresh throughout transportation and storage. However, its legacy data infrastructure was not capable of processing real-time data from sensors and logistics systems. As a result, the organization faced difficulties in detecting issues quickly and minimizing food waste.
The retailer lacked the ability to continuously monitor temperature conditions across warehouses, delivery trucks, and distribution centers. Without real-time alerts, temperature deviations often went unnoticed until product quality had already been affected.
Data from IoT sensors, transportation systems, and inventory platforms was stored in separate systems. This fragmentation prevented teams from gaining a unified view of cold-chain performance and slowed down decision-making.
Operational teams relied on historical reports rather than predictive insights. Without advanced analytics and AI capabilities, the company struggled to anticipate disruptions or optimize inventory movement for perishable goods.
Temperature fluctuations and delayed responses contributed to higher levels of food spoilage. This not only increased operational costs but also impacted sustainability goals and reduced confidence in supply-chain efficiency.
To overcome these challenges, DC Tech implemented a modern cloud-based data architecture built on Databricks. The solution included real-time streaming pipelines, curated datasets based on a Medallion architecture, and AI-powered quality scoring models that enabled inspectors to make faster and more accurate decisions.
The team developed scalable pipelines to gather data from internal systems as well as external sources such as temperature sensors and logistics platforms.
Data was cleaned, standardized, and organized into accessible formats to support reporting and actionable insights for business teams.
DC Tech integrated data from multiple systems to create a unified Fresh Domain Data layer, enabling end-to-end visibility across the cold-chain network.
The team continuously monitored and resolved data quality issues to ensure reliable analytics and accurate reporting.
Using Azure Data Lake Storage (ADLS) and Databricks, DC Tech implemented a scalable Medallion architecture that supports efficient and high-quality data processing.
With Databricks Unity Catalog, DC Tech established centralized governance and a single source of truth for consistent and secure data acces
Identify performance gaps and uncover opportunities for operational improvement through intelligent data analysis.
Convert complex data into clear insights that help teams take the right actions and improve supply chain performance.
Enable teams to make faster, data-driven decisions with real-time visibility into daily operations.
For example, analytics can reveal that temperature-sensitive products such as leafy greens and bananas are frequently exposed to higher temperatures during transportation from distribution centers to retail locations. With these insights, leadership can quickly identify operational issues, initiate corrective actions, and resolve root causes—helping ensure customers receive fresher produce in stores.
Developed and deployed a modern cloud-based architecture to support real-time analytics and improve monitoring across the entire supply chain.
Implemented centralized governance using Databricks Unity Catalog to manage data access, maintain metadata standards, and ensure consistent validation, testing, and monitoring for reliable data.
Adopted a Medallion-based architecture in Databricks with Bronze, Silver, and Gold layers to enable structured data processing, stronger traceability, and improved data lineage.
Built scalable pipelines to ingest and integrate data from Supplier Hub, Coupa, and other internal and external platforms.
Enabled machine learning workflows using Databricks MLflow for feature management, model development, tracking, deployment, and monitoring to support intelligent decision-making.
The grocery retailer successfully adopted AI within its operational processes and quickly experienced measurable improvements, including better product freshness and higher efficiency across supply chain activities.
Reduction in food waste
Faster operational response
Improved inventory visibility
Better cold-chain monitoring
From strategy to implementation, we provide the knowledge and leadership our clients rely on to accelerate their business. Our proven team takes a unified approach to driving large-scale change and unlocking new opportunities for growth and success.