WILL QUINN WAREHOUSING AND SUPPLY CHAIN STRATEGIST
ALBERTO OCA PARTNER MCKINSEY & COMPANY
pipe fi ttings, is that spelled out as “one half inch” or 1/2” in the system? Depending on the measurement, Quinn said the same item could be entered in three or four different ways, each of which could be interpreted as a distinct item that needs its own spot in the warehouse. FEDA and its members are ahead of this issue. One of
the key objectives of the FEDA Data Portal has been to provide uniform and standardized product information that distributors and manufacturers can rely on to support their sales and supply chain efforts. While those standards have been in place from the start, the FEDA Future of Distribution Council Product Data Standards & Integration Subcommittee is working through refi ning the portal’s data fi elds to ensure they continue to provide complete, uniform data. Alberto Oca, a partner with McKinsey & Company
1. Starting with Clean Data Companies can invest heavily in advanced algorithms,
but if the underlying data is inconsistent, duplicated, or incomplete, the output is going to be unreliable — and costly.
“There’s so much data a distributor can use, but it’s
got to be cleaned up,” said Will Quinn, a warehousing and supply chain strategist who has managed distribution networks for global companies such as Grainger and Coca-Cola. Using consistent formatting for data attributes such as units of measurement is critical to ensuring that an AI solution will be able to properly use that information. If an underbar system uses half-inch
who specializes in warehousing, logistics, and distribution, noted that although AI is becoming more adept at working with structured and unstructured data, companies must still pay attention to how the information it feeds off is managed post-integration. This helps business leaders proactively catch AI errors or anomalies. “Areas to focus on from a data perspective are ensuring inputs like lead-time variability, service-level targets, and demand segmentation are regularly maintained,” Oca said.
2. Establishing Operational Alignment Having quality data is a great start, but organizations also
need a plan for what to do with all that information. Oca stressed the importance of setting a value-driven goal up front then exploring how AI can help make it happen. “Defi ne the north star fi rst and the impact you want to achieve instead of focusing on a technology roadmap fi rst,” he said. “It’s important to set the ambition, and
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