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Executive Impact Analysis of Big Data in the Trucking Industry

Executive Impact Analysis of Big Data in the Trucking Industry

Key Findings

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.

Executive Analysis of Big Data in Commercial Vehicles: Key Findings, North America and Europe, 2014

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.

About this report

This study provides an executive analysis of Big Data in the North American and European trucking industries. Using both primary and secondary research data, analyses and discussions cover key trends, total cost of ownership, and market opportunities by value chain participant. Big Data service revenue forecasts are provided for the period 2014–2022. Key conclusions and a future outlook are also provided.


  • Executive Summary
    • Key Findings
    • Executive Summary-OEMs Leveraging Telematics Data
    • Executive Summary-Tier-1 Suppliers Foray into Telematics
    • Executive Summary-Evolving Partnership Models
    • Executive Summary-OEM/Tier-1 Analytics Application by Function
    • Executive Summary-Possible Average Annual Savings for OEMs and Fleets
    • Executive Summary-Case Study of UPS
    • Executive Summary-Big Data Application Areas and Partners for OEMs
    • Executive Summary-Big Data Application Areas and Partners for Tier-1 Companies
    • Executive Summary-Big Data Application Areas and Partners for Telematics Providers
    • Key Conclusions and Future Outlook
  • Research Scope, Objectives, Background, and Methodology
    • Research Scope
      • Table Executive Analysis of Big Data in Commercial Vehicles: Telematics Installed Base, North America and Europe, 2014-2022
    • Research Aim and Objectives
    • Key Questions this Study Will Answer
    • Research Methodology
  • Impact of Big Data on Fleet Operations (TCO)
    • Total Cost Of Ownership
    • TCO Restructuring through Big Data and Analytics 2014-2022
    • Predictive Analytics Service Offerings and Average Potential Savings for Fleets
    • Application of Big Data in Fuel Cost Optimization
    • Application of Big Data in Cost Optimization for Driver Wage Benefits and Safety
    • Application of Big Data in Maintenance Cost Optimization
    • Application of Big Data in Insurance Cost Optimization
    • Big Data for Fleets: Maines Paper and Food Services
    • Big Data Analytics for Connecting Shippers to Carriers Using Mobile Platform
    • Summary-Impact on Fleet Operations
  • Implications of Big Data for Commercial Vehicle OEMs
    • Big Data Opportunities for OEMs
    • Leveraging Big Data in OEM Research and Product Development
    • Leveraging Big Data in OEM Manufacturing Processes
    • Leveraging Big Data in OEM Marketing
    • Leveraging Big Data in OEM Warranty and Aftersales
    • Summary-Implications of Big Data for Commercial Vehicle OEMs
  • Big Data and Analytics Revenue and Opportunities
    • Big Data Service Revenue 2014-2022
    • Revenue Distribution by Stakeholders
  • Conclusions
    • Key Conclusions and Future Outlook
  • Appendix
    • Abbreviations and Acronyms Used
    • Market Engineering Methodology

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