冷链运输及冷藏车队管理系统

Modern airports are one of the busiest environments on Earth and rely on process automation equipment to ensure service operators achieve their KPIs. Increasingly airport SCADA systems are being used to control all aspects of the operation and associated facilities. This is because unplanned system downtime can cost dearly, both in terms of reduced revenues and the associated loss of customer satisfaction due to inevitable travel inconvenience and disruption.

Speed to market is precious for DRVR, a rapidly growing start-up company. With a business model dependent on reliable mobile data, managers were spending their lives trying to negotiate data roaming deals with mobile network operators in different countries. And, even then, service quality was a constant concern.

To reduce maintenance costs and delay-causing failures for Deutsche Bahn. They need manual measurements by a position measurement system based on custom-made MEMS sensor clusters, which allow autonomous and continuous monitoring with wireless data transmission and long battery. They were looking for data pre-processing solution in the sensor and machine learning algorithms in the cloud so as to detect critical wear.

Organizations frequently implement a maintenance strategy for their fleets of vehicles using a combination of time and usage based maintenance schedules. While effective as a whole, time and usage based schedules do not take into account driving patterns, environmental factors, and sensors currently deployed within the vehicle measuring crank voltage, ignition voltage, and acceleration, all of which have a significant influence on the overall health of the vehicle.In a typical fleet, a large percentage of road calls are related to electrical failure, with battery failure being a common cause. Battery failures result in unmet service agreement levels and costly re-adjustment of scheduled to provide replacement vehicles. To reduce the impact of unplanned maintenance, the transportation logistics company was interested in a trial of C3 Vehicle Fleet Analytics.

The company has a long track record of innovative technology solutions. For example, 3M helps its customers optimize parking operations by automating fee collection and other processes. To improve support for this rapidly expanding segment, 3M needed to automate its own data collection and reporting. The company had recently purchased the assets of parking, tolling, and automatic license plate reader businesses, and required better insight into these acquisitions. Chad Reed, Global Business Manager for 3M Parking Systems, says, “With thousands of installations across the world, we couldn’t keep track of our software and hardware deployments, which made it difficult to understand our market penetration.” 3M wanted a tracking application that sales staff could use to get real-time information about the type and location of 3M products in parking lots and garages. So that it could be used on-site with potential customers, the solution would have to provide access to data anytime, anywhere, and from an array of mobile devices. Jason Fox, Mobile Application Architect at 3M, upped the ante by volunteering to deliver the new app in one weekend. For Fox and his team, these requirements meant turning to the cloud instead of an on-premises datacenter. “My first thought was to go directly to the cloud because we needed to provide access not only to our salespeople, but to resellers who didn’t have access to our internal network,” says Fox. “The cloud just seemed like a logical choice.”

The existing system used by the customer could only track a few thousand assets and was able to generate only a few standard set of reports. As the number of assets tracked grew exponentially, the system started to break at the seams. The Tracking devices were from different manufacturers following different protocols. There was no proper integration among the devices to send instant alerts. There are thousands of tracking devices spread across multiple geographies, that are moving. The configuration and troubleshooting of these devices incurred heavy costs, which was a logistics challenge. The existing system did not provide sophisticated Analytics, Business Intelligence and Insights from the data