Savi Technology (Lockheed Martin) > Case Studies > Global 50 CPG Company Enhances In-Transit Visibility & Analytics

Global 50 CPG Company Enhances In-Transit Visibility & Analytics

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 Global 50 CPG Company Enhances In-Transit Visibility & Analytics - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Consumer Goods
  • Transportation
Applicable Functions
  • Logistics & Transportation
  • Warehouse & Inventory Management
Use Cases
  • Public Transportation Management
  • Real-Time Location System (RTLS)
  • Data Science Services
  • System Integration
The Customer

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 Challenge
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.
The Solution
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.
Operational Impact
  • The implementation of Savi Visibility™ dramatically reduced the operational burden, making far fewer check calls necessary. Planners were able to focus on mitigating or avoiding disruptions of shipments that would otherwise have arrived late. The improved inbound ETA accuracy enabled more inbound loads to be synchronized with outbound shipments, increasing the percentage of loads to be cross-docked and reaching the original efficiency target of regional cross-docking centers. The feed from Savi’s big data platform allowed the TMS to be continuously updated with accurate ETAs, keeping the planning, operations, and customer teams up to date in real-time. The company also gained actionable insights about in-transit inventory levels, helping them to reduce safety stock or avoid stock-outs. The early outreach and collaboration with their customers when an unavoidable disruption occurs, as well as more accurate shipment ETAs overall, has brought improved customer satisfaction.
Quantitative Benefit
  • 22% improvement in cross-docking efficiency and orchestration
  • 17x increase in ETA accuracy
  • 350+ hours/week productivity gained per transportation lane

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