Global 50 CPG Company Enhances In-Transit Visibility & Analytics
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
- Consumer Goods
- Logistics & Transportation
- Warehouse & Inventory Management
- Public Transportation Management
- Real-Time Location System (RTLS)
- Data Science Services
- System Integration
A Global 50 Consumer Packaged Goods (CPG) company was facing significant challenges with its North American truck shipments, averaging 25,000 per month. The company lacked a system for real-time monitoring of inventory in-transit, leaving operation team members in the dark about real-time disruptions and unable to prevent or mitigate late or non-delivery for their customers. The company's new strategic cross-docking network was underperforming due to inaccurate inbound Estimated Time of Arrivals (ETAs), which reduced the number of goods that could be cross-docked. The company's Transportation Management System (TMS) and Enterprise Resource Planning (ERP) systems were not receiving real-time information and rarely received shipment plan updates. Consequently, the company had to employ hundreds of people to manually update ETAs by making check calls to carriers, drivers, and customers. This manual process was not only time-consuming and error-prone but also latent, with disruption reports often received well after they occurred.
Consumer Packaged Goods (CPG)
About The Customer
The customer is a Global 50 Consumer Packaged Goods (CPG) company that averages 25,000 North American truck shipments per month. The company was struggling with a lack of real-time monitoring of inventory in-transit, which led to operational inefficiencies and customer dissatisfaction due to late or non-delivery of goods. The company's new strategic cross-docking network was underperforming due to inaccurate inbound Estimated Time of Arrivals (ETAs), and their TMS and ERP systems were not receiving real-time information, leading to a manual and error-prone process of updating ETAs.
The CPG company partnered with Savi to implement Savi Visibility™, a live streaming in-transit tracking and ETA solution. The company asked their truck carriers to send Electronic Data Interchange (EDI) and telematics feeds to Savi, while Savi set up an automated feed from and to the company's TMS to log planned shipments. After ingesting the data from the carriers and the TMS, Savi's machine learning platform used Artificial Intelligence (AI) to build algorithms that could predict both inbound and outbound ETAs with much higher accuracy. The Savi Visibility user interface provided map, list, and reporting views of the real-time status of all shipments. Predictive alerts, such as “Trending Late” and “Trending Early,” were determined using customer-specific thresholds of time, distance, and amount predicted late. Predictive ETAs and alerts were sent to users and the ERP system, enabling synchronization between TMS, Warehouse Management System (WMS), and yard management operations.
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