Download PDF
Delivering Company-Wide Data-Driven Workflows
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Automotive
- Transportation
Applicable Functions
- Logistics & Transportation
Use Cases
- Fleet Management
- Predictive Maintenance
Services
- Data Science Services
- System Integration
The Challenge
Car Next Door, an Australia-based car-sharing service, was relying on a homegrown data analytics solution. However, as the company grew, the internal development team was spending a disproportionate amount of time servicing data requests rather than focusing on product development. The company was facing issues with inconsistent metric definitions and data accuracy. Users typically worked with static data in spreadsheets, leading to human errors that were often undetectable, causing distorted results. The company was looking for a solution that would make data easily accessible to the company at large and provide real value across multiple aspects of the business.
About The Customer
Car Next Door is an Australia-based car-sharing service that is making an impact with its unique car-sharing service to help foster a cleaner, greener environment. Through the Car Next Door platform, neighbors help neighbors reduce waste and greenhouse gas emissions by safely, easily, and cost-effectively sharing their idle cars by the hour, by the day, or for longer periods of time. Car Next Door estimates that for every car that’s shared, there are up to 10 fewer cars on the road, as car borrowers are less likely to purchase and use their own vehicles. The Sydney company was launched in 2012, with 20 cars and 60 borrowers. By 2019, the total number of shared cars increased to more than 3,000. With its environmentally conscious ethos, Car Next Door offsets the carbon produced from every mile that’s driven. To date, offsets have planted 40,500 trees in biodiverse Australian forests.
The Solution
Car Next Door selected Looker for its power and flexibility. With Looker, they can now access and analyze trusted data in real-time. Implementing Looker resulted in a 97% increase in efficiency for the process of adding new data sets. These updates to the data model are now made in minutes vs. days. Looker has enabled richer and faster analytics for Car Next Door and created a data-driven culture that improved efficiency across the business. Every department has created custom data experiences based on their unique needs, allowing the entire company to make better decisions for a more sustainable future. Looker’s geospatial functionality identifies cars in locations associated with weather events. Segment then creates a cohort of users associated with those cars. And Braze SMS sends alerts to drivers about potential hazards to help them get to safety and prevent loss or damage to vehicles or personal property.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
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.
