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市场调查报告书

预测性维护市场:2021-2026

Predictive Maintenance Market Report 2021-2026

出版商 IoT Analytics GmbH 商品编码 1000359
出版日期 内容资讯 英文 183 Pages
商品交期: 最快1-2个工作天内
价格
预测性维护市场:2021-2026 Predictive Maintenance Market Report 2021-2026
出版日期: 2021年04月15日内容资讯: 英文 183 Pages
简介

此报告检查了预测性维护(PdM)市场,并提供以下内容:

  • 2021年至2026年的市场规模和前景,包括细分
  • 按技术(连通性,硬件,平台和中间件,数据基础结构和服务器,应用程序,系统集成和服务,分析和AI软件)
  • 按托管类型(私有云/本地,公共云)
  • 按细分
  • 按地区/国家
  • 包括有关280家公司的弯曲者状况的详细信息,PdM初创公司的建立和并购活动
  • 关于 "预测性维护最终用户觀点" 的两个发现(包括ROI分析和技术细节)
  • 10个最近的案例研究
  • 7个当前趋势 2个挑战,有关先前提到的趋势和挑战的最新信息

这是IoT预测性维护领域中的第三次更新。除了最新的市场评估之外,两项最终用户调查还提供了业务和技术见解。

调查内容:

  • 什么是预测性维护?
  • 当前市场规模和预测性维护的增长率
  • 预测性维护中使用的技术类型
  • 预测性维护供应商及其策略的示例
  • 意外停机经济学
  • 预测性维护,计划投资,实施最佳实践的当前渗透
  • 当前预测维护模型的KPI
  • 最常用的预测性维护分析方法
  • 客户在进行预测性维护方面的收益
  • 当前市场趋势的特征
  • 阻碍市场的问题

目录

第1章执行摘要

第2章简介

  • 定义
  • PdM搜索兴趣
  • 与其他保护方法的比较
  • 应用领域

第3章技术概述

  • 技术栈
  • 详细分析:传感技术
  • 详细分析:分析

第4章市场规模和前景

  • 总体PdM市场
  • 市场:按技术
  • 市场:按行业
  • 市场:按地区
  • 关于PdM市场的专家意见

第5章竞争情况

  • 公司情况
  • 预测性维护启动
  • 并购活动

第6章业务模型和案例研究

  • 商业模式
  • 案例研究

第7章最终用户注意事项

  • 商业调查
  • 技术调查

第8章趋势和挑战

  • 趋势
  • 挑战
  • 专利分析

第9章研究方法和市场定义

第10章关于出版商

目录

183-page market report examining the Predictive Maintenance market. Including:

  • Market size & outlook 2021-2026, with breakdowns:
  • By technology (Connectivity, Hardware, Platform and Middleware, Data Infrastructure and Servers, Application, System Integration and Services, Analytics and AI Software)
  • By hosting type (Private Cloud / On-premises, Public Cloud)
  • By segment
  • By region/country
  • Vendor landscape of ~280 firms, with detailed information on PdM startups founding, and M&A activities
  • Results from two surveys on the "Predictive Maintenance end-user perspective" (incl. a view on ROI and technical details)
  • Discussion of 10 recent case studies
  • Discussion of 7 current trends and 2 challenges, plus updates on previously identified trends and challenges

The “Predictive Maintenance Market Report 2021-2026 ” constitutes the third update of IoT Analytics' ongoing coverage of the Predictive Maintenance space. Along with the latest market assessment, the report provides business and technical insights collected through two end-user surveys. The reports' offering is further enhanced with an extensive summary of recent market developments, trends, and challenges, along with an updated market forecast for the market in 2021-2026 by technology, segment, and region.

Find out:

  • What is Predictive Maintenance?
  • How big is the Predictive Maintenance market today and how fast is it growing?
  • Which types of technologies are used for Predictive Maintenance?
  • What are examples of Predictive Maintenance vendors and their strategies to achieve zero downtime?
  • What are the economics of unplanned downtime?
  • What is the current Predictive Maintenance penetration rate, planned investments and implementation best-practices?
  • What are the KPIs of today's Predictive Maintenance models?
  • What are the most used Predictive Maintenance analytics methods?
  • Which benefits customers see from their Predictive Maintenance investments?
  • What trends are characterizing the market currently?
  • What challenges are holding the market back?

At a glance:

Definition of Predictive Maintenance:

Predictive maintenance describes 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 automated (near) real-time analytics and supervised or unsupervised ML. (Note: Many PdM implementations use "near real-time" analytics, i.e., with several minutes of delay.)
  • Among other benefits, this approach promises cost savings over routine or time-based preventive maintenance because tasks are performed only when warranted.

Predictive Maintenance is defined and further differentiated from other maintenance approaches.

The report presents a complete picture of the technology stack of IoT PdM solutions, along with deep dives on 4 PdM sensing techniques and 7 key analytics considerations (e.g., types of data sources).

The market is defined as annual PdM technology-spend by companies implementing PdM Solutions, and an analyst opinion on market development provided.

The market is further broken down into technology stack elements, segments, and regions.

The report provides a company landscape with ~280 firms, grouped into four main categories based on their main PdM offering: Hardware, Connectivity, Storage and Platform, and Analytics. In the same chapter, PdM startups founding information, and M&A activities are also presented.

10 cases studies are presented with detailed information on technology, challenges addressed, PdM approach taken, and solution implications.

The report provides results from two surveys on the Predictive Maintenance end-user perspective, with business- (e.g., economics of unplanned downtime), and technical-related (e.g., precision of models) information on PdM implementations.

7 trends and 2 challenges are described in detail, backed up with interview quotes and examples.

Table of Contents

1. Executive Summary

  • 1.1 Executive Summary
  • 1.2 Changes Since the 2019 PdM Report

2. Introduction

  • 2.1 Definition
  • 2.2 PdM Search Interest
  • 2.3 Comparison With Other Maintenance Approaches
  • 2.4 Application Areas

3. Technology Overview

  • 3.1 Technology Stack
  • 3.2 Deep Dive: Sensing Techniques
  • 3.3 Deep Dive: Analytics

4. Market Size & Outlook

  • 4.1 Total PdM Market
  • 4.2 Market by Technology
  • 4.3 Market by Vertical
  • 4.4 Market by Region
  • 4.5 Expert Opinions About the PdM Market

5. Competitive Landscape

  • 5.1 Company Landscape
  • 5.2 Predictive Maintenance Startups
  • 5.3 M&A Activities

6. Business Models & Case Studies

  • 6.1 Business Models
  • 6.2 Case Studies

7. End User Insights

  • 7.1 Business Survey
  • 7.2 Technical Survey

8. Trends & Challenges

  • 8.1 Trends
  • 8.2 Challenges
  • 8.3 Patent Analysis

9. Methodology & Market Definitions

10. About