Radically Improve Operations with Estimated Time of Arrival
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公司规模
1,000+
地区
- America
国家
- United States
产品
- Savi Performance Analytics
技术栈
- Telematics
- Sensor Data
- Machine Learning
- Big Data Technologies
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 功能应用 - 运输管理系统 (TMS)
适用行业
- 消费品
适用功能
- 物流运输
- 仓库和库存管理
用例
- 车队管理
- 供应链可见性(SCV)
服务
- 软件设计与工程服务
- 系统集成
关于客户
The customer is a large Consumer Packaged Goods (CPG) company that manufactures a high percentage of their products. They operate on a large scale, handling 85,000 shipments weekly. The company had previously invested in traditional solutions, such as warehouse and transportation management systems, but these did not meet their needs for precise and accurate time of arrivals. They had spent millions of dollars working with several consulting and technology providers without achieving the desired results. The company was left relying on drivers to provide estimated time of arrivals (ETA) and employees to manually update statuses, leading to numerous operational inefficiencies.
挑战
A large Consumer Packaged Goods (CPG) company needed end-to-end supply chain visibility with an emphasis on timeliness. Their current visibility was limited to the day of delivery and lacked precise and accurate time of arrivals, causing numerous operational inefficiencies. The company had previously invested in traditional solutions, such as warehouse and transportation management systems, and spent millions of dollars working with several consulting and technology providers who didn’t deliver the results or solutions they needed. Ultimately, they were left relying on drivers to provide estimated time of arrivals (ETA) and employees to manually update statuses.
解决方案
Savi Technology provided the CPG company with Savi Performance Analytics, a comprehensive SaaS, purpose-built solution. The solution was live within six weeks by leveraging current telematics and sensor data, requiring no customization. Savi’s proprietary ETA algorithms combined real-time and historical data to allow the CPG company to better predict transit times. The company rolled out the solutions to highly-traveled transit lanes, as well as to security and risk teams, to track and secure 85,000 shipments weekly. Additionally, as more data was acquired, Savi’s algorithms became smarter and more accurate due to advanced machine learning. This led to improved planning for shipments, cross-docking, and on-time arrivals.
运营影响
数量效益
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