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.
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.
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.
- 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?
A selection of companies mentioned in the report.
- Baker Hughes
- Rockwell Automation
- Telit Cinterion
Table of Contents
1. Executive Summary
- 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
- 18.104.22.168. 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