Stakeholders in commercial trucking are investing in Big Data analytics to deliver efficiencies to customers as well as to transform their business processes; the impact of these investments is expected to be felt across the industry by 2019. In 2014, 5 of the 12 global HD OEMs were already independently and through collaborations with IT companies engaged in developing Big Data analytics. By 2019, all 12 top global HD OEMs are likely to leverage Big Data analytics to increase their margins through sales, service, maintenance, financing, and warranty cost reduction. Vehicle driving and maintenance data monetization is increasingly becoming a significant business activity for value chain participants, most of whom lack experience or expertise in harnessing the potential of Big Data. Predictive and prescriptive analyses through Big Data hold the potential to deliver times the return on investment (ROI) in harnessing Big Data. Global Tier-1 suppliers are evaluating or implementing Big Data platforms across their enterprises, with more focus on warranty and parts management. Large Tier-1 suppliers such as Cummins, Wabco, ZF, and Dana are expected to become increasingly involved in the prognostics value chain with associated data analytics capabilities across their product lines. A sustainable Big Data strategy relies on collaborating and coordinating among IT suppliers and internal IT functions within OEMs/Tier-1 companies. HD OEMs are expected to increasingly rely on IT companies for Big Data analytics, securing vehicular systems, and developing customizable design and service offerings. By 2020, emerging Class 8 truck sales and rental businesses are likely to gain benefit from data as fleet managers begin to make purchase and rental/leasing decisions based on "functional obsolescence” (how long can we run a truck?) and "economic obsolescence” (when is the optimal point to replace to a new truck?). OEMs are developing strategies to leverage Big Data to harness more robust customer and dealership management systems, to better understand end-user buying patterns and to accelerate their innovation-to-value cycle. In North America and Europe, several OEMs are already ushering in Big Data through telematics and prognostics.
Executive Summary—OEMs Leveraging Telematics Data
Leveraging telematics data enables seamless collaboration among stakeholders, improves customer service levels, and helps in analyzing risks at the vehicle and service levels.
Needs of OEMs •To improve warranty management, as it has a direct impact on the OEM’s profitability •To enhance product quality and reliability management •To manage customer relationships through the vehicle lifecycle
Opportunities •Leveraging the connected strategy of OEMs by providing a factory-fitted telematics solution at the time of purchase or as the standard •Increasing revenue through telematics services •Enhancing CRM (sales strategy) through telematics •Entering new business avenues outside the automotive sector (energy, driver health, advertising, financial services)
Examples OEMs are driving prognostics by accessing internal, historical data to understand the utilization patterns of components and to improve component quality. Daimler FleetBoard uses its business-analytics-based telematics solution to enable OEMs to offer customers an innovative insurance policy for their fleets. Scania measures the transport flow of mines, sending data to the workshop responsible for meeting contractual targets on a certain quantity of material and a certain level of uptime in percentage terms.
Tier-1 Suppliers Foray into Telematics
Tier-1 suppliers are becoming increasingly involved in the diagnostics/prognostics value chain by offering electronic interfaces in their product lines, enabling Tier-1 companies to monitor product reliability in real time.
Challenges for Tier-1 Suppliers The present state of warranty management is often ineffective despite reasonable improvements (warranty-related collaboration) made between suppliers and OEMs. Suppliers are hamstrung due to the lack and/or delay of vital data from OEMs.
Benefits Following are the reasons for Tier-1 suppliers to integrate telematics into their product lines: •Telematics improve component durability, reliability, and effective warranty management •They expand aftermarket business •They create a recurring business model by providing value added services •They make it possible to share/sell data to insurance companies or government bodies.
Examples Cummins offers “Connected Diagnostics” aiming to integrate its engine expertise with existing telematics applications. Diagnostic App monitors the operations (load classes, speed profiles, route profiles) of ZF-EcoLife Transmission and enables better predictive maintenance. Wabco’s TX-Trailer Guard collects data on status, tire pressure, trailer braking, stability, and other efficiency and safety systems, backed by real-time alerts and automated reporting. Michelin’s telematics solution ‘EFFIFUEL’ focuses on tire management, apart from its fuel management program.
Table Of Contents
Executive Impact Analysis of Big Data in the Trucking Industry : Rising Downtime Reduction and Empty Miles Concerns Expediting Big Data Adoption in the Trucking Industry Executive Summary 3 Research Scope, Objectives, Background, and Methodology 15 Impact of Big Data on Fleet Operations (TCO) 20 Implications of Big Data for Commercial Vehicle OEMs 31 Big Data and Analytics Revenue and Opportunities 38 Conclusions 41 Appendix 44