A national grocer needed to bring intelligent automation and AI to their produce supply chain and ensure their stores were stocked with the freshest food for their customers. The client was looking for a trusted Databricks partner with experience applying AI in retail. By modernizing their data platform and applying machine learning to real-time insights, Kforce Consulting Solutions delivered a scalable solution that is driving measurable cost savings, improved inspection results and increased operational efficiency.
INDUSTRY
Retail
KEY SOLUTIONS
Data ingestion pipelines, data quality management, AI enablement, data platform modernization
KEY TECHNOLOGIES
Databricks, PySpark, PySQL, Azure and GCP
At a glance
Meet the team
Brad Boyd
Ted Horne
William Lockley
Discover the power of partnership
Challenge
The national grocery chain needed to improve the freshness of perishable food in their stores. The existing systems were not equipped to handle real-time data processing and analytics, leading to inefficiencies in supply-chain operations and lost revenue.
Slow response times
The client didn't have a streaming capability and lacked real-time data ingestion and processing. This limited their ability to respond quickly when perishable food was at risk in transit.
Inconsistent data quality
The data architecture was outdated. Without medallion architecture or a layered data model, it was challenging to trace data lineage and ensure quality.
Poor supply-chain insights
The client lacked a modern data platform. Data was siloed and inaccessible, which made it difficult for the client to understand what was happening across their supply chain in real time.
Low trust in data
Without proper validation, testing or metadata management, stakeholders had limited confidence in the data for decision-making.
Solution
To address these challenges, Kforce implemented a cloud-native data architecture using Databricks. This included streaming pipelines, curated datasets using a Medallion Architecture, and AI-driven quality scoring to help inspectors make real-time decisions.
This comprehensive solution optimized cold-chain operations and gave the grocer the insights needed to reduce the risk of food going bad in transit.
Ingestion pipelines
The team built robust pipelines to collect data from both internal systems and external applications (e.g., temperature sensors, logistics platforms).
Data curation and presentation
Data was curated, standardized and presented in accessible formats for internal stakeholders to drive actionable insights.
Data integration
Kforce joined data from multiple systems to create a unified Fresh Domain Data layer, enabling holistic visibility across the cold chain.
Data quality management
The team proactively investigated and resolved data quality issues to ensure accuracy and reliability in analytics and reporting. This is an ongoing, continual activity to ensure data quality remains excellent.
Cloud-native architecture
Leveraging Azure Data Lake Storage (ADLS) and Databricks, the team implemented a medallion architecture that ensures scalable, high-quality data processing.
Unified data governance
Using Databricks Unity Catalog, Kforce established a single source of truth for sourcing data, enabling consistent access and governance across teams.
Platform enablement
The team adopted the Databricks Framework and integrated API-driven data sources to enrich the platform with both real-time and historic insights.
The project operated off real-time and batch data from the refrigerator supply chain, including temperature sensors, purchase orders, warehousing and transportation. This data informed the AI-driven cold-chain intelligence initiative.
A PROGRESSIVE DATA JOURNEY
ANALYTICS ›
Uncover improvement opportunities
OPERATIONAL INSIGHTS ›
Extract meaningful next steps
REAL-TIME ADJUSTMENTS
Empower active participation in day-to-day decisions
For example, analytics may reveal that fresh produce like leafy greens and bananas are frequently exposed to high temperatures during transit from distribution centers to stores. With this insight, management can identify critical issues, direct operations to investigate and resolve the root cause, greatly reducing the chance of shoppers coming across wilted lettuce and mushy bananas.
Deliverables
Scalable insights across the supply chain
Designed and implemented an end-to-end, cloud-native architectures for real-time analytics.
Trusted, governed data
Implemented Databricks Unity Catalog for centralized data governance, access control and metadata management across domains. Implemented validation, testing and monitoring frameworks to ensure data accuracy and ongoing trustworthiness.
High-quality data processing
Transitioned to a Databricks medallion architecture and built Bronze, Silver and Gold data layers to support structured data processing and further data lineage and traceability.
Improved supply chain intelligence
Developed robust pipelines for ingesting data from Supplier Hub, Coupa (Spend Management) and other internal/external systems.
AI use cases & design
Enabled machine learning lifecycle with Databricks Managed MLflow, including feature store, model training, tracking, registry, serving and monitoring.
Project outcomes
The national grocer successfully integrated AI into their business process and saw near-immediate benefits, including enhanced freshness and increased operational efficiency.
The work resulted in several qualitative improvements:
- Improved cross-functional collaboration thanks to a centralized platform for inventory data
- Accelerated decision making due to self-service enablement that doesn't rely on engineering teams
- More secure and efficient data access through the Unity Catalog
- Reduced latency and overhead storage without duplicating data
Because of the success of the project, the client extended the partnership with Kforce to include a second phase of work. The initiative is ongoing, and outcomes are still being achieved. Estimated quantifiable outcomes, based on results to date, include:
40% reduction in data engineering effort through self-service access and reusable curated datasets, reducing dependency on engineering teams for ad hoc data requests.
50% improvement in data access latency enabled by Unity Catalog's zero-copy data sharing and optimized query performance across workspaces.
28% cost savings on storage and compute by eliminating redundant data copies and streamlining processing through the medallion architecture.
2.5x increase in data adoption across teams due to improved data discoverability, trust and accessibility via centralized governance and documentation.
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