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

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.

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 used Unsupervised to find ways to improve their shipping logistics and on-time delivery.

This company relied on a commercial system with a rules engine and analytical models to determine which location or distribution center to source a product from so that it could be delivered to the customer by a promised date. Issues meant that shipments would require expensive upgrades to air transportation to ensure they arrived on time.

Even with an internal team of data scientists spending over a year analyzing and data building models, there were still significant insights that Unsupervised found, leading 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 would save over $1M in annualized costs and gave the team an entirely new way to identify categories of common model error.

Here are some examples of 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