Leveraging Data Science for "Decision Intelligence" in the DC

E-commerce order fulfillment demands have introduced unprecedented complexities in distribution centers (DCs) and fulfillment environments. As delivery windows shrink and product varieties (SKUs) multiply, fulfillment speeds and order volumes are accelerating beyond the capabilities of even the most sophisticated DCs.

While many online retailers are transitioning to advanced automation and robotics, traditional strategies of planning fulfillment waves days (or even hours) in advance are no longer viable. Order priorities can vary widely and change in an instant, and DC operators need the flexibility and agility to adapt continuously to fluctuations in demand and resource availability.

Increased process automation is becoming a necessity, but the integration of multiple automated systems and workflows can result in even more complexity. System interdependencies often are overlooked or underestimated, and a decision made in one system can create a cascading effect of unintended consequences in others, resulting in process inefficiencies at best — or productivity bottlenecks due to frequent manual interventions and reactive troubleshooting in worst case scenarios. The progressive uptake in automation also has led to the proliferation of data from nearly every corner of the DC.

Learn how data science techniques — such as machine-learning algorithms, advanced data-driven optimization techniques and artificial intelligence — can leverage this abundance of data for insights into the interplay between automation systems. Combined with sophisticated warehouse execution system (WES) software, these tools can enable dynamic, real-time “decision intelligence” to achieve optimal execution strategies and business results in complex fulfillment environments.

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