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Improve Just-in-Time Production with Real-Time Tracking of Goods
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
- Sensors - GPS
Applicable Industries
- Consumer Goods
Applicable Functions
- Logistics & Transportation
- Warehouse & Inventory Management
Use Cases
- Predictive Maintenance
- Supply Chain Visibility
- Track & Trace of Assets
Services
- Data Science Services
- System Integration
The Challenge
A leading global CPG company faced challenges in their European sector due to on-time delivery complications for shipments that crossed multiple land and sea borders. The company needed to plan heavily around the estimated time of arrival (ETA) of raw goods at their production facilities, which were limited on space. Receiving goods just-in-time for production was essential to minimize stock levels and avoid delays in production schedules. Therefore, they required a solution that would provide greater visibility and accurate ETAs to ensure timely receipt of materials.
About The Customer
The customer is a leading global Consumer Packaged Goods (CPG) company operating in the European sector. They face significant logistical challenges due to the complexity of their supply chain, which involves crossing multiple land and sea borders. The company operates production facilities that are limited on space, making it crucial to receive raw materials just-in-time to minimize stock levels and avoid production delays. The company sought a solution to improve visibility and accuracy in the estimated time of arrival (ETA) of shipments to enhance their just-in-time production processes.
The Solution
Savi Technology provided a comprehensive solution to address the company's needs by leveraging advanced analytics and sensor technology. Savi placed GPS pallet sensors on goods to actively track their location throughout the shipment process. This real-time tracking allowed the company to have greater visibility of their in-transit goods. Savi's solution dynamically calculated and adjusted the ETA for shipments based on the data received from the sensors, ensuring more accurate delivery times. Additionally, Savi set up alerts to notify the production facilities if goods were going to arrive later than expected. These alerts were based on dwell time at configured checkpoints and overall destination arrival times. Savi also identified trends that allowed the company to proactively resolve delivery problems, enhancing their ability to maintain production schedules and minimize stock levels.
Operational Impact
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