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)
- 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.
- 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
- 2.1 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