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市场调查报告书
商品编码
1643865

商用车 (CV) 预测性维护产业：北美、欧洲、印度，2024-2029 年

Commercial Vehicle (CV) Predictive Maintenance Industry, North America, Europe, and India, 2024-2029
出版日期: | 出版商: Frost & Sullivan | 英文 76 Pages | 商品交期: 最快1-2个工作天内

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简介目录

预测：提高营运效率和降低维护成本将推动成长

在技​​术的快速进步和对效率、安全性和永续性日益增长的需求的推动下，商用车行业正在经历重大变革时期。这一转变中的一个突出的创新是预测技术，即根据即时资料预测车辆的健康和性能的能力。该研究深入探讨了商用车预测、生态系统、关键参与企业和市场占有率。它还确定了主要趋势和案例研究，强调了预测在彻底改变维护方法、提高业务效率和节省成本方面的潜力。该研究重点关注北美、欧洲和印度重量超过 3.5 吨的商用车。已开发市场和新兴市场的纳入提供了对这些地区机会和挑战的全面认识。

商用车预测利用资料分析、人工智慧 (AI) 和机器学习 (ML) 演算法来预测车辆零件故障和维护需求。研究首先定义预测预测，列出一些常用的 ML 方法，概述五年时间范围内商用车应用的预测预测范围，并强调这种预测方法与传统的反应性和预防性维护实践之间的鲜明对比。

商用车预测的成长机会在于大幅降低维护和营运成本的潜力，因为传统的维护策略往往会导致效率低下和过度停机。随着商用车变得越来越复杂，车辆资料的可用性正在达到顶峰。这些资料透过两种主要途径从车辆中提取：诊断工具和远端资讯处理，它们作为预测 ML 演算法的数据来源。在涉及这些资料来源之后，该研究对预测性维护生态系统中利用这些资料管道提供预测性维护服务的各个参与者进行了分类。它还讨论了这些参与公司之间的相互关係及其运作，确定了新的Start-Ups、新兴领导企业和主导公司，并透过将主要企业相互映射来获得有意义的见解，揭示实际情况。

当预测系统与其他新兴技术如远端资讯处理和自动驾驶时，将会放大其潜在优势。考虑到这些创新，该研究描绘了必将对行业产生影响的关键趋势，并讨论了 2024 年的三大关键趋势：数位双胞胎、无线更新和机器学习的进步，每个趋势都附有详细的案例研究。

儘管预测技术前景广阔，但在商用车中广泛应用仍面临许多挑战。预测的一个主要成长阻碍因素是保险桿到保险桿解决方案的高误报率，这阻碍了车队所有者和OEM的广泛采用。误报将预测范围限制在特定应用的利基市场。在人工智慧和机器学习领域，分析和资料科学公司可以开发精确的演算法来减少这些误报，从而促进更广泛的用户采用他们的解决方案。

总而言之，该研究估计了截至 2023 年北美、欧洲和印度商用车市场的市场规模、装置量和预测渗透率。此外，该报告还提供了截至 2029 年的五年预测，包括全部区域的收益和市场估计。

预测对商用车产业来说是一个变革机会，能够带来显着的利益。随着技术的发展，预测系统的采用可能会变得更加广泛。 Prognostics 正在透过 Prognostics 纯业务提供者、远端资讯处理服务供应商和OEM之间的策略伙伴关係和併购重塑维护生态系统，推动车队管理的下一波创新浪潮。

目录

改变商用车 (CV) 预测性维护

  • 成长为何变得越来越艰难？
  • The Strategic Imperative 8（TM）
  • 三大战略对商用车维修业的影响

目的、目标、范围

  • 研究目标
  • 调查方法
  • 研究范围

成长环境

  • 竞争环境
  • 主要发现
  • 成长动力
  • 成长抑制因素
  • 预后的定义
  • 实现预测模型的方法
  • CV 预测范围

心血管维护预测解决方案

  • 心血管维护预测资料通道
  • 心血管维护中的预后预测
  • 心血管预后预测与解决方案交付管道
  • 使用远端资讯处理资料进行 CV 维护预测
  • 预测在心血管维护的应用

商用车预测生态系统

  • 透过预测服务为车队所有者增加价值
  • 价值链预测
  • 全球： OEM预测 - 可用解决方案
  • 世界：一家专门​​从事预测和诊断工具的公司
  • 主要生态系参与者比较（2023年）

趋势和案例研究

  • 预测的主要趋势
  • 趋势：数位双胞胎和感测器融合
  • 案例研究：Intangles -数位双胞胎公司
  • 趋势：OTA 更新
  • 案例研究：沃尔沃卡车正常运行时间中心 - OTA 更新
  • 趋势：深度学习和人工智慧在 CV 维护的应用
  • 案例研究：CerebrumX

区域分析

  • 区域分析：北美
  • 区域分析：欧洲
  • 区域分析：印度
  • 预测解决方案供应商分享
  • 收益分析

成长要素

  • 成长指标
  • 收益预测考虑因素
  • 各地区收益预测
  • 销售预测：按地区
  • 收益预测情境分析
  • 装置量预测
  • 价格趋势及预测分析
  • 关键成长动力：TSP 和预测合作伙伴伙伴关係
  • 主要成长抑制因素：高误报率导致采用速度缓慢

最佳实践评估

  • 最佳实务评量：Pitstop
  • 最佳实践评估：Intangles
  • 最佳实践评估：CerebrumX

成长机会宇宙

  • 成长机会 1：预测服务 (PaaS)
  • 成长机会二：库存自动化
  • 成长机会三：机器学习与人工智慧开发

后续步骤Next steps

  • 成长机会的好处和影响
  • 后续步骤Next steps
  • 附件列表
  • 免责声明
简介目录
Product Code: PFNC-42

Prognostics is Driving Growth by Increasing Operational Efficiency and Reducing Maintenance Costs

The commercial vehicle industry is undergoing a major transformation, fueled by rapid technological advancements and rising demand for efficiency, safety, and sustainability. A standout innovation in this shift is prognostics, which is the ability to predict vehicle health and performance based on real-time data. This study takes a deep dive into prognostics in commercial vehicles, the ecosystem, key participants, and their market share. It also identifies key trends and case studies and highlights the potential of prognostics to revolutionize maintenance practices, enhance operational efficiency, and drive cost savings. The focus of this study is on commercial vehicles that weigh more than 3.5 tons in North America, Europe, and India. By including both developed and developing markets, the study provides a comprehensive view of the opportunities and challenges in these regions.

Prognostics in commercial vehicles leverages data analytics, artificial intelligence (AI), and machine learning (ML) algorithms to forecast vehicle component failures and maintenance needs before they occur. The study kicks off by defining prognostics, listing some common ML approaches used, outlining the scope of prognostics regarding commercial vehicle applications with a 5-year timeline, and highlighting the sharp contrast of this predictive approach with traditional reactive and preventive maintenance practices.

The growth opportunity in prognostics for commercial vehicles lies in its potential to significantly reduce maintenance and operational costs, as traditional maintenance strategies often lead to inefficiencies and excessive downtime. As commercial vehicles become more sophisticated, vehicle data availability is at its peak. This data is extracted from the vehicle through 2 primary routes-diagnostics tools and telematics, which become the sources to feed prognostics' ML algorithms. After touching upon these data sources, the study moves on to classify different categoric participants of the predictive maintenance ecosystem that leverage these data channels to offer prognostics services. The study also discusses the inter-relationships between these participants and their functions, identifies new start-ups, emerging leaders, and dominant companies, and throws light on the on-ground scenario by drawing meaningful insights by mapping key companies against each other.

The integration of prognostics systems with other emerging technologies, such as telematics and autonomous driving, amplifies its potential benefits. Considering these innovations, this study maps key trends with their impact on the industry against certainty and discusses the top 3 trends of 2024 (digital twins, OTA updates, and advances in ML, each of which is elaborated along with a case study).

Despite its promise, the widespread adoption of prognostics in commercial vehicles faces several challenges. A key growth restraint in prognostics-high false positives in bumper-to-bumper solutions, which has kept fleet owners and OEMs from widespread adoption-is discussed. False positives have restricted prognostics to a niche and made it an application-specific market. Here lies another notable opportunity in the AI and ML domains for analytics and data science companies to develop accurate algorithms that can reduce these false positives, increasing the solution's adoption across a wider user base.

In conclusion, the study estimates market size, installed base, and penetration of prognostics as of 2023, across the North American, European, and Indian commercial vehicle markets. In addition, it offers a 5-year forecast until 2029 for revenues and estimated market bases across the regions of study.

Prognostics represents a transformative opportunity for the commercial vehicle industry, offering significant advantages. As technology evolves, the adoption of prognostics systems will become increasingly prevalent. Prognostics is reshaping the maintenance ecosystem through strategic partnerships and mergers and acquisitions among dedicated prognostics companies, telematic service providers, and OEMs, driving the next wave of innovation in fleet management.

Table of Contents

Transformation in CV Predictive Maintenance

  • Why Is It Increasingly Difficult to Grow?
  • The Strategic Imperative 8™
  • The Impact of the Top 3 Strategic Imperatives on the CV Maintenance Industry

Aims, Objectives, and Scope

  • Aims and Objectives
  • Research Methodology
  • Scope

Growth Environment

  • Competitive Environment
  • Key Findings
  • Growth Drivers
  • Growth Restraints
  • Definition of Prognostics
  • Approaches to Implementing Prognostics Models
  • Scope of Prognostics in CVs

Prognostics Solutions in CV Maintenance

  • Data Channels for Prognostics in CV Maintenance
  • Prognostics in CV Maintenance
  • Channels to Offer Prognostics Solutions in CVs
  • Prognostics in CV Maintenance through Telematics Data
  • Applications of Prognostics in CV Maintenance

Prognostics Ecosystem in Commercial Vehicles

  • Value Addition for Fleet Owners through Prognostics Services
  • Prognostics in the Value Chain
  • Global*: OEMs in Prognostics-Available Solutions
  • Global: Dedicated Prognostics and Diagnostics Tool Companies
  • Comparison of Key Ecosystem Participants, 2023

Trends and Case Studies

  • Key Trends in Prognostics
  • Trend: Digital Twin and Sensor Fusion
  • Case Study: Intangles-A Digital Twin Company
  • Trend: OTA Updates
  • Case Study: Volvo Trucks Uptime Center-OTA Updates
  • Trend: Deep Learning and AI in CV Maintenance
  • Case Study: CerebrumX

Regional Analysis

  • Regional Analysis: North America
  • Regional Analysis: Europe
  • Regional Analysis: India
  • Prognostics Solution Providers' Share
  • Revenue Share Analysis

Growth Generator

  • Growth Metrics
  • Revenue Forecast Considerations
  • Revenue Forecast by Region
  • Units Forecast by Region
  • Revenue Forecast Scenario Analysis
  • Installed Base Forecast
  • Pricing Trends and Forecast Analysis
  • Key Growth Driver: Partnerships between TSPs and Prognostics Companies
  • Key Growth Restraint: Slower Adoption Because of High False Positives

Best Practices Recognition

  • Best Practices Recognition: Pitstop
  • Best Practices Recognition: Intangles
  • Best Practices Recognition: CerebrumX

Growth Opportunity Universe

  • Growth Opportunity 1: Prognostics-as-a-service (PaaS)
  • Growth Opportunity 2: Inventory Automation
  • Growth Opportunity 3: Developments in Machine Learning and AI

Next Steps

  • Benefits and Impacts of Growth Opportunities
  • Next Steps
  • List of Exhibits
  • Legal Disclaimer