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Modernizing the Nordstrom Supply Chain: Efficient Order Fulfillment using JanusGraph, Cassandra and Spark Jeff Callahan

September 12, 2019

DESCRIPTION: Today’s Nordstrom customers expect order fulfillment options shipping to the most convenient locations in the timeframes that best fit their schedules. This session offers an inside look at how Nordstrom’s Supply Chain Technology team combined an extensive network of brick and mortar facilities with a custom software stack built atop JanusGraph to present fulfillment options tailored to each individual order. ABSTRACT: Over the last decade, the volume of online retail sales as a proportion of overall retail sales has nearly tripled, and there are no signs of this trend slowing down. In a $500 billion online retail market driven by ever more savvy online shoppers, expectations for order fulfillment have grown more demanding. Consumers expect a choice of fulfillment options that offer convenience, flexibility and value. Nordstrom’s Supply Chain Technology team is charged with meeting those customer expectations through efficient distribution of millions of unique items from a cross-country network of brick and mortar facilities. For the engineers on our team, it made perfect sense to use JanusGraph to model a dynamic inventory picture managed by a diverse set of geographically distributed facilities. Despite our enthusiasm, not everyone was eager to adopt an unfamiliar open source graph system. In this presentation, Jeff Callahan will discuss why his team at Nordstrom embraced JanusGraph as the backbone of a new data pipeline built to meet the evolving expectations of customers today (and tomorrow, too). After taking a detailed look at the system architecture, we’ll also discuss some of the internal organizational challenges we faced in adopting JanusGraph, and how we managed to navigate those challenges to ultimately deliver an even stronger solution. NOTE: While this presentation focuses on JanusGraph, there is considerable overlap with Big Data, Machine Learning and Cassandra

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