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全球预测性维护与资产绩效市场：2023-2028
市场调查报告书
商品编码
1373226

全球预测性维护与资产绩效市场：2023-2028

Predictive Maintenance & Asset Performance Market Report 2023-2028
出版日期: | 出版商: IoT Analytics GmbH | 英文 295 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

本报告审视了全球预测性维护和资产绩效市场，提供了预测性维护 (PdM)、基于状态的维护 (CbM) 和资产绩效管理 (APM) 的定义，概述了技术和流程实施，并介绍它总结了驱动因素、市场规模趋势和预测、各个细分市场和地区的详细分析、竞争状况、显着趋势和问题、案例研究等。

范例视图





预测性维护 (PdM)、状态维护 (CbM) 和资产绩效管理 (APM) 市场分析与预测：

  • 依技术堆迭（连接、硬体、服务、软体）
  • 按託管类型（私有云/本地/公有云）
  • 按细分市场（第一产业、医疗保健、运输、建筑和房地产、其他）
  • 依行业（离散製造、混合製造、製程製造）
  • 按地区（撒哈拉以南非洲、中东和北非、南亚、拉丁美洲和加勒比地区、北美、东亚和太平洋地区、欧洲和中亚）
  • 依国家（新加坡、澳洲、韩国、日本、中国、比利时、波兰、荷兰、瑞士、西班牙、义大利、法国、英国、德国、加拿大、美国等）

*不包括 APM 细分。

上市公司

  • ABB
  • AVEVA
  • AWS
  • Arundo
  • AspenTech
  • Augury
  • Baker Hughes
  • Cognite
  • Falkonry
  • GE
  • I-care
  • IBM
  • MachineMetrics
  • MathWorks
  • Microsoft
  • Novity
  • Rockwell Automation
  • SKF
  • Siemens
  • Telit Cinterion

目录

第 1 章执行摘要

第 2 章简介

  • 考虑预测性维护的三种方法
  • PdM、CbM、APM 的定义
  • 资产绩效管理的关键组成部分
  • PdM 与其他方法的比较
  • PdM 的典型资产类型/应用领域
  • PdM 的主要优势

第 3 章技术概述

  • PdM 实施流程
  • 了解更多：购买和建构 PdM 解决方案
  • 详细资料：感测技术
  • 详细资讯：PdM 数据分析
  • 详细资料：PdM 软体
  • 详细资讯：APM 软体的工作原理

第四章市场规模与前景

  • 全球智慧维护市场概况
  • 世界 PdM 和 CbM 市场
    • 全球 PdM 和 CbM 市场：按资产和感测器类型划分
    • 全球 PdM 与 CbM 市场：依技术堆迭划分
    • 全球 PdM 与 CbM 市场：依託管类型划分
    • 全球 PdM 与 CbM 市场：按细分市场
    • 全球 PdM 和 CbM 市场：按地区
  • 全球 APM 市场

第五章竞争态势

  • 公司状况
  • 10 家最大的 PdM 供应商
  • 10 家最大的 CbM 供应商
  • 详细资料：PdM 公司简介
  • 近期影响 PdM 竞争格局的重大新闻
  • PdM 启动
  • 併购活动中的机器视觉专利
  • 专利分析

第 6 章个案研究

第 7 章最终使用者见解

  • 数位化调查
  • 维护/可靠性调查

第 8 章趋势与问题

第9章研究方法/市场定义

第 10 章关于 IoT 分析

作者

简介目录

A 295-page report detailing the market for next-generation maintenance, including detailed definitions, adoption drivers, market projections, competitive landscape, end-user insights, notable trends, and case studies.

The “Predictive Maintenance Market Report 2023-2028” constitutes the 4th update of IoT Analytics' ongoing coverage of predictive maintenance and is part of IoT Analytics' ongoing coverage of industrial and software/analytics topics. The content presented in this report is based on a compilation of primary research, including surveys and interviews with 35+ industry experts from predictive maintenance vendors and end users conducted between March and October 2023.

The report encompasses a holistic overview of the current state of the predictive maintenance market and adjacent markets such as condition-based maintenance and asset performance management, including market projections, factors driving adoption, competitive landscape, technology and process implementation overview, notable trends and challenges, and insightful case studies.

The primary objective of this document is to provide our readers with a comprehensive understanding of the current predictive maintenance market landscape, offering in-depth analysis, market sizing, and valuable insights to facilitate informed decision-making and strategic planning.

SAMPLE VIEW

What is predictive maintenance (PdM)?

  • A set of techniques to accurately monitor the current condition of machines or any type of industrial equipment
  • ... using either on-premises or cloud analytics solutions
  • ... with the goal of predicting upcoming machine failure by using statistical methods and supervised/unsupervised ML.

Among other benefits, this approach promises cost savings over routine or time-based preventive maintenance because tasks are performed only when warranted.

What is asset performance management (APM)?

  • A strategic equipment management approach that helps optimize the performance and maintenance efficiency of individual assets and of entire plants or fleets.

APM aims to improve the efficiency, availability, reliability, maintainability, and overall life cycle value of assets. This concept includes elements of CbM and PdM but goes beyond them.

What is condition-based maintenance (CbM)?

  • A maintenance approach that monitors the actual condition of an asset to determine what maintenance needs to be done.

It does not involve further analytics, such as predicting the remaining useful life (RUL) or the overall health of the machine.

SAMPLE VIEW

The “ Predictive Maintenance Market Report 2023-2028” analyzes the predictive maintenance (PdM), condition-based maintenance (CbM), and asset performance management (APM)* market from 2021 to 2028. It provides detailed data and forecasts for the market size:

  • by tech stack (connectivity, hardware, services, software)
  • by hosting type (Private cloud/on-premises, public cloud)
  • by segment (primary sector, health care, transportation, contruction & real estate, other, hybrid manufacturing, process manufacturing, discrete manufacturing)
  • by industry (discrete manufacturing, hybrid manufacturing, process manufacturing)
  • by region (Sub-Saharan Africa, Middle East & North Africa, South Asia, Latin America & Caribbean, North America, East Asia & Pacific, Europe & Central Asia)
  • by country (East Asia & Pacific: Singapore, Australia, South Korea, Japan, China, Other; Europe and Central Asia: Belgium, Poland, Netherlands, Switzerland, Spain, Italy, France, United Kingdom, Germany; North America: Canada, United States)

*no breakdowns included for APM.

SAMPLE VIEW





Questions answered:

  • What is predictive maintenance, condition-based maintenance, and asset performance management?
  • What role does predictive maintenance play in the overall maintenance space?
  • What are the key features, functionalities, and components of predictive maintenance solutions? What are the key components of asset performance management solutions?
  • What is the current market size and projected growth of the predictive maintenance market?
  • How does the predictive maintenance market split by tech stack, segment, hosting type, asset type, sensor type and region?
  • What does the competitive landscape for predictive maintenance look like, who are the key players, and what is their market share?
  • What are the emerging predictive maintenance trends and challenges?
  • What are some successful case studies demonstrating the benefits of predictive maintenance in various applications?

Companies mentioned:

A selection of companies mentioned in the report.

  • ABB
  • AVEVA
  • AWS
  • Arundo
  • AspenTech
  • Augury
  • Baker Hughes
  • Cognite
  • Falkonry
  • GE
  • I-care
  • IBM
  • MachineMetrics
  • MathWorks
  • Microsoft
  • Novity
  • Rockwell Automation
  • SKF
  • Siemens
  • Telit Cinterion

Table of Contents

1. Executive Summary

2. Introduction

  • 2.1. Three ways to look at predictive maintenance
  • 2.2. Definition of PdM, CbM, and APM
  • 2.3. Asset performance management key components
  • 2.4. Comparison of PdM with other approaches
  • 2.5. PdM typical types of assets/application areas
  • 2.6. PdM key benefits

3. Technology Overview

  • 3.1. PdM implementation process
  • 3.2. Deep dive: buying vs. building the PdM solution
  • 3.3. Deep dive: sensing techniques
  • 3.4. Deep dive: PdM data analysis
  • 3.5. Deep dive: PdM software
  • 3.6. Deep dive: APM software in action

4. Market size & outlook

  • 4.1. Overview of the global smart maintenance market
  • 4.2. Global PdM and CbM Market
    • 4.2.1. Global PdM and CbM Market in 2022, by Asset and Sensor Type
    • 4.2.2. Global PdM and CbM Market, by Tech Stack
    • 4.2.3. Global PdM and CbM Market, by Hosting Type
    • 4.2.4. Global PdM and CbM Market, by Segment
    • 4.2.5. Global PdM and CbM Market, by Region
      • 4.2.5.1. Market regional deep dive: East & Pacific Asia, Europe & Central Asia, and North Amercia
  • 4.3. Global APM Market

5. Competitive landscape

  • 5.1. Company landscape
  • 5.2. The 10 largest PdM vendors
  • 5.3. The 10 largest CbM vendors
  • 5.4. Deep dive: top five PdM company profiles
  • 5.5. Notable recent news with effect on the PdM competitive landscape
  • 5.6. PdM start-ups
  • 5.7. Mergers and acquisitions (M&A) activity machine vision patents
  • 5.8. Patent analysis

6. Case Studies

  • 6.1. Case studies overview
  • 6.2. Case studies

7. End User Insights

  • 7.1. Digitization Survey
  • 7.2. Maintenance and Reliability Survey

8. Trends & Challenges

  • 8.1. Trends
  • 8.2. Challenges

9. Methodology and market definitions

10. About IoT Analytics

Authors