HP Big Data Helps Ford to Better Manage Fleets and Personalize Employee Drives
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HP-Ford partnership is leveraging Big Data for the next level of Telematics insights based intelligence.
By Martin Risau
As sensors and telematics systems become more common, information that can be collected about vehicle usage and driver behavior is becoming increasingly available and accessible. Everyone in the automotive value chain is interested in gathering information and understanding driver behavior but there is still a lot of uncertainty about how to use the data responsibly.
Ford Motor Company engineers recently completed a real-world driving experiment with HP, discovering which commuting commonalities could provide future breakthroughs for better managing fleets, personalized services and recommendations for individual drivers.
The Fleet Insights experiment, one of 25 Ford Smart Mobility experiments, is designed to better understand how driving behavior is changing. The experiment tracked 100 HP fleet vehicles and drivers via a device plugged into the car and leveraged HP’s Big Data Discovery Experience and the HP Haven Big Data platform to gather and analyze data to determine possibilities for lowering operating costs and optimizing underutilized vehicles for fleets as well as personal driving.
Using the HP Vertica analytics engine, part of the HP Haven platform, Ford data scientists and IT leaders were able to explore patterns and multiple dimensions of fleet driver activity. The data was sent to the cloud where location details, vehicle status, and trip specifics were used to create mobility profiles and analyze fleet optimization. The goal? To improve the efficiency and provide new services for both the company fleet manager and the drivers.
Efficiency optimization is critical for fleet operators. The project found that managers could swap vehicle types for task-based needs or use parked company vehicles, instead of renting a car at airports, when the driver is out of town. Additional observations during the experiment included:
Drivers may have even more to gain: quicker coffee runs, cheaper gas, and faster routes. Resulting in a more efficient day and less of a headache. The study created mobility profiles using four questions:
The trips fell into four groups:
HP and Ford share a common vision around bringing together data, mobility, and analytics to explore new ways to deliver better customer experiences, new revenue streams, and lower fuel and maintenance costs in the automotive industry. The results of this experiment can help unleash improvements for business operations for fleet management and personal driving experiences. As the project continues throughout 2015, the conclusions are likely to shed more light on how fleet solutions can benefit companies and individuals.
The Big Data Discovery Experience is not only assisting in improving HP’s fleet efficiency for the future, but also will help give employees a quick kick of caffeine to improve their bumper-to-bumper blues.
Martin Risau is Senior Vice President and General Manager of the Analytics & Data Management Practice (A&DM) organization within HP’s Enterprise Services business. This organization delivers solutions that address the most challenging problems companies face in managing information and data assets. A&DM provides services to handle both structured and unstructured data, leveraging “big data” to drive business outcomes for clients.
Related:
As sensors and telematics systems become more common, information that can be collected about vehicle usage and driver behavior is becoming increasingly available and accessible. Everyone in the automotive value chain is interested in gathering information and understanding driver behavior but there is still a lot of uncertainty about how to use the data responsibly.
Ford Motor Company engineers recently completed a real-world driving experiment with HP, discovering which commuting commonalities could provide future breakthroughs for better managing fleets, personalized services and recommendations for individual drivers.
The Fleet Insights experiment, one of 25 Ford Smart Mobility experiments, is designed to better understand how driving behavior is changing. The experiment tracked 100 HP fleet vehicles and drivers via a device plugged into the car and leveraged HP’s Big Data Discovery Experience and the HP Haven Big Data platform to gather and analyze data to determine possibilities for lowering operating costs and optimizing underutilized vehicles for fleets as well as personal driving.
Using the HP Vertica analytics engine, part of the HP Haven platform, Ford data scientists and IT leaders were able to explore patterns and multiple dimensions of fleet driver activity. The data was sent to the cloud where location details, vehicle status, and trip specifics were used to create mobility profiles and analyze fleet optimization. The goal? To improve the efficiency and provide new services for both the company fleet manager and the drivers.
Efficiency optimization is critical for fleet operators. The project found that managers could swap vehicle types for task-based needs or use parked company vehicles, instead of renting a car at airports, when the driver is out of town. Additional observations during the experiment included:
- Regardless of location, most drivers visited the same national coffee house and refueled with the same brand of gasoline
- Traveling employees often left their vehicles unused at the airport for days. These vehicles could be utilized more effectively by nearby drivers
- 70 percent of trips took place during weekdays and typical trip distances were 13 miles or less
Drivers may have even more to gain: quicker coffee runs, cheaper gas, and faster routes. Resulting in a more efficient day and less of a headache. The study created mobility profiles using four questions:
- Who was driving?
- Their destination?
- When did they take the trip?
- What route did they ultimately use?
The trips fell into four groups:
- City block driving (34 percent): Involved frequent direction changes, driving near the speed limit, idling at stoplights with short distances.
- Freeway driving (21 percent): Involved few driving direction changes with large deviations from the speed limit depending on traffic, and long trip durations and driving distances with less stop and go than city block commute.
- Non rush-hour driving (29 percent): Short trip duration and short distance with less stops and idling
- Rush-hour driving (16 percent): Short trip duration and short distance with frequent stops and idling during peak drive hours
HP and Ford share a common vision around bringing together data, mobility, and analytics to explore new ways to deliver better customer experiences, new revenue streams, and lower fuel and maintenance costs in the automotive industry. The results of this experiment can help unleash improvements for business operations for fleet management and personal driving experiences. As the project continues throughout 2015, the conclusions are likely to shed more light on how fleet solutions can benefit companies and individuals.
The Big Data Discovery Experience is not only assisting in improving HP’s fleet efficiency for the future, but also will help give employees a quick kick of caffeine to improve their bumper-to-bumper blues.
Martin Risau is Senior Vice President and General Manager of the Analytics & Data Management Practice (A&DM) organization within HP’s Enterprise Services business. This organization delivers solutions that address the most challenging problems companies face in managing information and data assets. A&DM provides services to handle both structured and unstructured data, leveraging “big data” to drive business outcomes for clients.
Related:
