Fortune 500 Retailer Finds $1m+ with Improvements to Supply Chain Management Using Unsupervised

supply-chain-cs
This case study takes a look at how a Fourtune 500 retailer turned to Unsupervised to optimize their supply chain. Unsupervised was able to provide rapid insights that resulted in over $1M in projected cost savings and an entirely new way to evaluate shipping upgrades.
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Meeting Customer Expectations Leads to Costly Shipping Requirements

Any retailer knows you don’t just compete on quality and price, you also have to meet customer expectations. A Fortune 500 retailer prioritized getting products into customers’ hands on time, every time.

But the enterprise relied on a commercial system with internal rules and analytical models to determine where to source a product in hand and deliver to the customer by a promised date. Often shipments would require expensive upgrades to air transportation to ensure they arrived on time.

Even with the internal team spending 50% of their time over a year analyzing and data building models, few insights led to real optimizations and savings.

Unsupervised Unearths Insights That Lead to Actions

Using several months of shipping and customer data, Unsupervised was able to provide rapid insights that resulted in over $1M in projected cost savings and an entirely new way to evaluate shipping upgrades.

‍Specifically, Unsupervised surfaced insights that led to quick, bottomline-boosting actions:

  • Warehouse Staffing – One large distribution center was seeing an abnormal number of shipping upgrades for products that required multiple handlers. With new insights from Unsupervised, the retailer realized the warehouse was understaffed on a specific week day, resulting in more delayed orders. The retailer acted quickly to address the staffing shortage and saved significant shipment costs.
  • Automation Workflows – Unsupervised helped the retailer identify a faulty shipping automation workflow, where a recurring pattern of product being shipped back and forth between distribution centers to meet fluctuating demand. The retailer captured cost savings by optimizing shipment forecasting and in-hand inventory.
  • Supply Chain Optimization – Several patterns were found associated with promotions that did not provide enough linkage to supply chain forecasting, resulting in dramatic increases in air shipments to meet customer demand. The pattern also identified specific spikes in demand and shipping costs associated with third-party resellers. These insights allowed for conversations between teams to optimize planning, reduce cost, and improve overall customer satisfaction

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