Client Overview
The client is a Fortune 50 omnichannel home improvement retailer operating thousands of physical locations across North America, with a rapidly growing e-commerce and professional (B2B) customer segment. The company supports a complex supply chain that includes direct fulfillment centers, distribution centers, and last-mile delivery networks serving both consumers and high-volume professional customers.
A significant portion of the client’s catalog includes configurable products sold under generic SKUs, particularly in categories tailored for professional buyers. These products vary by configuration but historically lacked standardized, structured dimension data.
Business Challenge
To qualify products for direct fulfillment, the organization requires accurate product dimensions and package size data to support:
- Transportation planning
- Warehouse slotting
- Delivery eligibility rules
- Cost and margin calculations
However, many configurable products:
- Were sold under generic
- SKUs
- Did not have reliable dimensional attributes
- Could not be routed through direct fulfillment centers as a result
This limitation prevented a large subset of products from being eligible for faster, more efficient fulfillment options—particularly impacting professional customers who rely on predictable delivery timelines for job-site planning.
Objective
The goal was to determine whether existing but underutilized product data could be leveraged to:
- Estimate missing product dimensions
- Support scalable automation rather than manual data enrichment
- Expand the catalog eligible for direct fulfillment
- Improve outcomes for professional (B2B) customers
Solution Approach
Data Exploration & Feasibility Analysis
Working in close partnership with the client’s Data Science and Analytics team, we conducted a deep analysis of:
- Historical order data
- Supplier-provided attributes
- Product category patterns
- Related SKU dimension distributions
The focus was on identifying whether statistically meaningful patterns existed that could be used to infer approximate dimensions for configurable products.
AI & Machine Learning Strategy
Rather than attempting to produce perfect measurements, the initiative focused on generating high-confidence estimated dimension ranges suitable for fulfillment decisioning.
Key elements included:
- Feature engineering using product attributes and historical analogs
- Pattern recognition across similar product families
- Model validation against known dimension datasets
- Confidence scoring to support operational risk thresholds
The resulting model was designed to:
- Return estimated dimensions within acceptable tolerance levels
- Flag outliers or low-confidence predictions
- Integrate into downstream fulfillment eligibility logic
Results & Business Impact
Expanded Fulfillment Eligibility
The initiative enabled the retailer to:
- Unlock a previously ineligible set of products
- Route more configurable items through direct fulfillment centers
- Reduce reliance on slower or more expensive fulfillment paths
Improved Professional Customer Experience
Professional account users benefited from:
- Greater product availability with faster delivery options
- More predictable lead times
- Improved job planning and supply coordination
Operational Efficiency
- Reduced need for manual product data remediation
- Scalable approach that could be extended to additional categories
- Better utilization of existing fulfillment infrastructure
Strategic Value
This project demonstrated how AI-driven estimation, when paired with strong domain knowledge and operational constraints, can:
- Solve real-world data gaps without perfect data
- Enable business growth without massive replatforming
- Improve both customer experience and supply chain efficiency
It also established a repeatable blueprint for using machine learning to augment imperfect enterprise data at scale.
Key Takeaways
Even mature enterprises can unlock new value from data they already havespend more time on what truly matters: serving people and advancing their mission.
AI can be effectively used to infer missing operational data, not just predict outcomes
Estimated data, when confidence-scored and validated, can drive real business decisions
Cross-functional collaboration between business, data science, and operations is critical to success