商用車 (CV) 預測性維護產業：北美、歐洲、印度，2024-2029 年

Commercial Vehicle (CV) Predictive Maintenance Industry, North America, Europe, and India, 2024-2029

出版日期: 2025年01月07日 | 出版商:

Frost & Sullivan | 英文 76 Pages | 商品交期: 最快1-2個工作天內

價格

簡介目錄

預測：提高營運效率和降低維護成本將推動成長

在技術的快速進步和對效率、安全性和永續性日益成長的需求的推動下，商用車行業正在經歷重大變革時期。這一轉變中的一個突出的創新是預測技術，即根據即時資料預測車輛的健康和性能的能力。該研究深入探討了商用車預測、生態系統、關鍵參與企業和市場佔有率。它還確定了主要趨勢和案例研究，強調了預測在徹底改變維護方法、提高業務效率和節省成本方面的潛力。該研究重點關注北美、歐洲和印度重量超過 3.5 噸的商用車。已開發市場和新興市場的納入提供了對這些地區機會和挑戰的全面認知。

商用車預測利用資料分析、人工智慧 (AI) 和機器學習 (ML) 演算法來預測車輛零件故障和維護需求。研究首先定義預測預測，列出一些常用的 ML 方法，概述五年時間範圍內商用車應用的預測預測範圍，並強調這種預測方法與傳統的反應性和預防性維護實踐之間的鮮明對比。

商用車預測的成長機會在於大幅降低維護和營運成本的潛力，因為傳統的維護策略往往會導致效率低下和過度停機。隨著商用車變得越來越複雜，車輛資料的可用性正在達到頂峰。這些資料透過兩種主要途徑從車輛中提取：診斷工具和遠端資訊處理，它們作為預測 ML 演算法的數據來源。在涉及這些資料來源之後，該研究對預測性維護生態系統中利用這些資料管道提供預測性維護服務的各個參與者進行了分類。它還討論了這些參與公司之間的相互關係及其運作，確定了新的Start-Ups、新興領導企業和主導公司，並透過將主要企業相互映射來獲得有意義的見解，揭示實際情況。

當預測系統與其他新興技術如遠端資訊處理和自動駕駛時，將會放大其潛在優勢。考慮到這些創新，該研究描繪了必將對行業產生影響的關鍵趨勢，並討論了 2024 年的三大關鍵趨勢：數位雙胞胎、無線更新和機器學習的進步，每個趨勢都附有詳細的案例研究。

儘管預測技術前景廣闊，但在商用車中廣泛應用仍面臨許多挑戰。預測的一個主要成長阻礙因素是保險桿到保險桿解決方案的高誤報率，這阻礙了車隊所有者和OEM的廣泛採用。誤報將預測範圍限制在特定應用的利基市場。在人工智慧和機器學習領域，分析和資料科學公司可以開發精確的演算法來減少這些誤報，從而促進更廣泛的用戶採用他們的解決方案。

總而言之，該研究估計了截至 2023 年北美、歐洲和印度商用車市場的市場規模、裝置量和預測滲透率。此外，該報告還提供了截至 2029 年的五年預測，包括全部區域的收益和市場估計。

預測對商用車產業來說是一個變革機遇，能夠帶來顯著的利益。隨著技術的發展，預測系統的採用可能會變得更加廣泛。 Prognostics 正在透過 Prognostics 純業務提供者、遠端資訊處理服務供應商和OEM之間的策略夥伴關係和併購重塑維護生態系統，推動車隊管理的下一波創新浪潮。

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.