Anomaly Detection: How to Improve Core Product Accuracy and Efficiency with IoT
Customer Company Size
Mid-size Company
Region
- Europe
Country
- France
Product
- Coyote IoT devices
- Coyote mobile applications
- Dataiku Data Science Studio
Tech Stack
- Vertica
- Python
- PostgreSQL
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Transportation
Applicable Functions
- Logistics & Transportation
Use Cases
- Real-Time Location System (RTLS)
- Predictive Maintenance
Services
- Data Science Services
About The Customer
Coyote is the European leader in real-time road information. The company was founded in 2005 and has 250 employees. It boasts 4.8 million users across Europe and generated a turnover of over €100 million in 2014. Coyote's products include IoT-based devices and mobile applications that enable users to warn other drivers of traffic hazards and conditions that they detect while driving. These products rely heavily on the accuracy of incoming data, particularly the driving speed limits within their embedded maps.
The Challenge
Coyote, the European leader of real-time road information, faced a significant challenge in maintaining the accuracy of speed limit data within their embedded maps. This data is crucial for the functioning of their IoT devices and mobile applications, which warn drivers of traffic hazards and conditions. The company needed an automated, algorithm-based solution that could correct speed limit data and leverage the high volume of incoming data from their IoT devices to generate actionable insights and predictions. This also required instilling a data-driven approach within the company, where decisions are based on real-world data rather than standard analytics reports.
The Solution
Coyote used Dataiku Data Science Studio to develop an algorithm that leverages vast amounts of IoT-derived data. The algorithm segments roads into sections and analyzes patterns in each section, enabling Coyote to build a predictive model that estimates the speed limit of the road section. This machine learning process facilitated the detection of speed limit anomalies and enabled Coyote to estimate the global quality and reliability of the displayed speed limit. The collaborative functionalities of Dataiku DSS were crucial in this process, enabling employees with differing skill-sets to work together and promoting a widespread understanding of data mining, visualization, and smart data issues within the company.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Airport SCADA Systems Improve Service Levels
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.
Case Study
IoT-based Fleet Intelligence Innovation
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.
Case Study
Digitize Railway with Deutsche Bahn
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.
Case Study
Cold Chain Transportation and Refrigerated Fleet Management System
1) Create a digital connected transportation solution to retrofit cold chain trailers with real-time tracking and controls. 2) Prevent multi-million dollar losses due to theft or spoilage. 3) Deliver a digital chain-of-custody solution for door to door load monitoring and security. 4) Provide a trusted multi-fleet solution in a single application with granular data and access controls.
Case Study
Vehicle Fleet Analytics
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.
Case Study
3M Gains Real-Time Insight with Cloud Solution
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.”
