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市場調查報告書

連網型·車隊/資訊服務分析 (2015∼2016年)

The Connected Fleet & Data Services Report 2015-16

出版商 TU Automotive 商品編碼 295003
出版日期 內容資訊 英文 70 Pages; 14 Figures
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連網型·車隊/資訊服務分析 (2015∼2016年) The Connected Fleet & Data Services Report 2015-16
出版日期: 2015年11月30日 內容資訊: 英文 70 Pages; 14 Figures
簡介

車隊產生及收集的資料量正在爆炸性增加。如何評估這個資料的價值,如何實際活用,成了車隊業界相關人士面臨的一大課題。

本報告提供連網型 (網絡連接型) 車隊管理市場現狀與未來的市場機會相關分析,提供您今後的資料活用的市場機會,及目前活用案例·案例研究,車隊資料活用的生態系統 (資料分析企業·內容企業與汽車廠商的合作方法等),其他行業 (電子商務,行動通訊,旅遊業等) 應吸取的教訓等相關之調查與考察。

摘要整理

第1章 車隊連網型服務·資料商務的基本資料

  • 車隊用資料的價值鏈與流程
  • 有效率的資料管理·活用的重要性
  • 車隊資料活用的趨勢
    • 商務意向型解決方案
    • 司機的行動和車輛運用資料的關聯性
    • 資料的相關頻率的上升
    • 硬體設備非依存型系統
    • 共享經濟
    • 資料的雲端外包
    • 資料交換
  • 為何現在馬上實行車隊企業策略相當重要?
  • 地區性差異

第2章 夥伴關係/商業化的模式

  • 夥伴關係 (產業聯盟) 模式
  • 商業化·實用化的模式
    • 商業化模式的選擇1:收費服務
    • 商業化模式的選擇2:提高效率用保存·共享
    • 商業化模式的選擇3:每筆交易·活動的收費
    • 商業化模式的選擇4:車輛購買時的免費提供
    • 商業化模式的選擇5:編入車輛費用

第3章 障礙與課題

  • 資料的隱私
  • 資料的可靠性

第4章 案例研究的分類

  • 農業·建設業
  • 資料交換
  • 司機行為分析
  • 車隊管理企業
  • 貨物運輸效率
  • 保險
  • 汽車廠商
  • 車載資通系統·服務的供應商
  • 車載資通系統·軟體的供應商
  • 案例研究
    • Accuscore:風險型保險
      • 經營模式
      • 夥伴關係
      • 障礙與課題
      • 吸取的教訓
      • 分析與開設
    • ATG Risk Solutions
    • Cargomatic:PaaS的貨物運輸效率化
    • Caterpillar:為優化業績的機器運用效率化
    • Donlen (Hertz):透過整合車隊管理和車載資通系統提高業績
    • Fleetmatics:小∼中規模車隊的效率化
    • John Deere:透過設備的完全一體化,改善整體產業效率
    • MiX Telematics:全球規模整體性連網型·車隊服務
    • Navistar:全面性改善卡車製造商的「擁有體驗」
    • Omnitracs:車載資通系統市場初期的領導者
    • Peloton 所說的Technology:「從道路到自動駕駛」的明確願景
    • PTV Group:移動效率的改善
    • Zurich Global:車載資通系統的風險評估

第5章 富有魅力的市場機會

  • 共享經濟
  • 自動駕駛車
  • 商務解決方案·服務的轉變
  • 資料交換

第6章 來自其他產業的教訓

  • 共享經濟
  • 社群媒體的雲端外包

第7章 分析結果和開設

  • 分析方法
  • 簡稱
  • 相關分析

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目錄

Industry Overview

The volume of data being generated and collected from fleet vehicles is growing exponentially. What is the value of this data and how to capitalize on it is the next biggest challenge the fleet industry has to face.

This report looks at the fleet landscape and the current trends and future opportunities being presented. The report analyzes the opportunities to monetize data through an overview of use cases and case studies. The report also maps the monetization eco-system in fleet, presenting partnership options of how data analytics companies and content companies can work with OEMs. In conclusion, the report looks at what lessons the fleet industry can learn from other industries, such as retail, e-commerce, mobile and travel.

Key Areas Covered

  • Business models for utilizing fleet data effectively to generate revenue
  • Partnership options and examples for effective collaboration and pooling of fleet data
  • Extensive case studies and analysis

Your Key Questions Answered

  • How is the fleet market changing?
  • How are fleet companies using data to their advantage?
  • Why is it essential to have an effective data strategy?
  • How are successful companies working with data and what lessons can be learnt?
  • What are the experts predicting for connected fleets?
  • How will increased data and connectivity affect fleet?
  • What are the commercial model options for fleet players working with data?
  • What are the best data use cases in fleet?
  • What can fleet players learn from looking at established strategies?
  • What are the main challenges in working with big data and how best to navigate them?
  • What are the examples in the following areas?
    • Agriculture
    • Construction
    • Driver Behaviour
    • Fleet Management
    • Freight Efficiency
    • Insurance
    • Mobile Workforce Management
    • Original Equipment Manufacturing
    • Telematics Service Provider
    • Telematics Software Vendor

Table of Contents

  • Welcome
  • About TU-Automotive
  • Acknowledgements
  • List of Figures
  • Key terms

Executive Summary

  • Compelling fleet data opportunities
  • The sharing economy
  • Autonomous vehicles
  • Shift to business solutions & services
  • Data exchanges
  • Partnerships
  • Barriers & issues
  • Privacy & data security
  • Data liability
  • Lessons learned <>Introduction

1. Fleet connected services and data business basics

  • 1.1. Fleet data value chain and flow
  • 1.2. Importance of efficient data management and data monetization
  • 1.3. Trends in fleet data monetization
    • 1.3.1. Business driven solutions
    • 1.3.2. Correlation of driver behavior and vehicle operations data
    • 1.3.3. Frequency of data collection increasing
    • 1.3.4. Hardware agnostic
    • 1.3.5. The sharing economy
    • 1.3.6. Crowd sourcing of data
    • 1.3.7. Data exchange
  • 1.4. Why is it crucial for fleet players to put strategies in place now?
  • 1.5. Regional differences

2. Partnership & commercial models

  • 2.1. Partnership models
  • 2.2. Commercial & monetization models
    • 2.2.1. Commercial model option 1: Pay for service
    • 2.2.2. Commercial model option 2: Share in savings due to increased efficiency
    • 2.2.3. Commercial model option 3: Per transaction or event
    • 2.2.4. Commercial model option 4: Free with vehicle purchase
    • 2.2.5. Commercial model option 5: Bundled in cost of vehicle

3. Barriers & issues

  • 3.1. Data privacy
  • 3.2. Data liability

4. Case study categories

  • 4.1. Agriculture & construction
  • 4.2. Data exchange
  • 4.3. Driver behavior analysis
  • 4.4. Fleet management companies
  • 4.5. Freight efficiency
  • 4.6. Insurance
  • 4.7. Original equipment manufacturers
  • 4.8. Telematics service providers
  • 4.9. Telematics software vendors
    • 4.10. Case studies
      • 4.10.1. Accuscore: Risk based insurance
      • 4.10.1.1. Business model
      • 4.10.1.2. Partnerships
      • 4.10.1.3. Barriers and issues
      • 4.10.1.4. Lessons learned
      • 4.10.1.5. Analysis & commentary
    • 4.10.2. ATG Risk Solutions
      • 4.10.2.1. Business model
      • 4.10.2.2. Partnerships
      • 4.10.2.3. Barriers and issues
      • 4.10.2.4. Lessons learned
      • 4.10.2.5. Analysis & commentary
    • 4.10.3. Cargomatic: Platform as a service making freight more efficient
      • 4.10.3.1. Business model
      • 4.10.3.2. Partnerships
      • 4.10.3.3. Barriers and issues
      • 4.10.3.4. Lessons learned
      • 4.10.3.5. Analysis & commentary
    • 4.10.4. Caterpillar: Optimizing equipment to optimize business results
      • 4.10.4.1. Business model
      • 4.10.4.2. Partnerships
      • 4.10.4.3. Barriers and issues
      • 4.10.4.4. Lessons learned
      • 4.10.4.5. Analysis & commentary
    • 4.10.5. Donlen (Hertz): Merging fleet management with telematics for better business results
      • 4.10.5.1. Business model
      • 4.10.5.2. Partnerships
      • 4.10.5.3. Barriers and issues
      • 4.10.5.4. Lessons learned
      • 4.10.5.5. Analysis & commentary
    • 4.10.6. Fleetmatics: Making small to medium fleets more efficient
      • 4.10.6.1. Business model
      • 4.10.6.2. Partnerships
      • 4.10.6.3. Barriers and issues
      • 4.10.6.4. Regional differences
      • 4.10.6.5. Lessons learned
      • 4.10.6.6. Analysis & commentary
    • 4.10.7. John Deere: Fully integrating equipment into the overall business operations
      • 4.10.7.1. Business model
      • 4.10.7.2. Agriculture
      • 4.10.7.3. Power systems
      • 4.10.7.4. Construction
      • 4.10.7.5. Lessons learned & partnering
      • 4.10.7.6. Analysis & commentary
    • 4.10.8. MiX Telematics: Global, comprehensive connected fleet services
      • 4.10.8.1. Business model
      • 4.10.8.2. Partnerships
      • 4.10.8.3. Barriers and issues
      • 4.10.8.4. Regional differences
      • 4.10.8.5. Lessons learned
      • 4.10.8.6. Analysis & commentary
    • 4.10.9. Navistar: Truck OEM improving the total ownership experience
      • 4.10.9.1. Business model
      • 4.10.9.2. Partnerships
      • 4.10.9.3. Barriers and issues
      • 4.10.9.4. Lessons learned
      • 4.10.9.5. Analysis & commentary
    • 4.10.10. Omnitracs: Early leadership in telematics
      • 4.10.10.1. Business model
      • 4.10.10.2. Partnerships
      • 4.10.10.3. Barriers and issues
      • 4.10.10.4. Lessons learned
      • 4.10.10.5. Analysis & commentary
    • 4.10.11. Peloton Technology: Clear value on the road to autonomous driving
      • 4.10.11.1. Business model
      • 4.10.11.2. Partnerships
      • 4.10.11.3. Barriers and issues
      • 4.10.11.4. Lessons learned
      • 4.10.11.5. Analysis & commentary
    • 4.10.12. PTV Group: Increasing trip efficiency...
      • 4.10.12.1. Business model
      • 4.10.12.2. Partnerships
      • 4.10.12.3. Barriers, issues, and regional differences
      • 4.10.12.4. Lessons learned
      • 4.10.12.5. Analysis & commentary
    • 4.10.13. Zurich Global: Insight to risk via telematics
      • 4.10.13.1. Business model
      • 4.10.13.2. Partnerships
      • 4.10.13.3. Barriers, issues & regional differences
      • 4.10.13.4. Lessons learned
      • 4.10.13.5. Analysis & commentary

5. Compelling opportunities

  • 5.1. Sharing economy
  • 5.2. Autonomous vehicles
  • 5.3. Shift to business solutions & services
  • 5.4. Data exchanges

6. Lessons learned from other industries

  • 6.1. Sharing economy
  • 6.2. Social media crowd sourcing

7. Final analysis & commentary

  • Methodology
  • Abbreviations
  • References

List of Figures

  • Figure 1: Fleet Data Value Chain and Flow
  • Figure 2: Accuscore Business Model Example
  • Figure 3: ATG Risk Solutions Business Model Example
  • Figure 4: Cargomatic Business Model Example
  • Figure 5: Caterpillar Business Model Example
  • Figure 6: Donlen Business Model Example
  • Figure 7: Fleetmatics Business Model Example
  • Figure 8: John Deere Business Model Example
  • Figure 9: Mix Telematics Business Model Example
  • Figure 10: Navistar Business Model Example
  • Figure 11: Omnitracs Business Model Example
  • Figure 12: Peloton Technology Business Model Example
  • Figure 13: PTV Group Business Model Example
  • Figure 14: Zurich Global Business Model Example
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