Predictive Maintenance initiatives saved organizations $17B in 2018, as the number of vendors surges
ANNOUNCEMENT: Hamburg, Germany – 20th June 2019 //
IoT Analytics, a leading provider of market insights & competitive intelligence for the Internet of Things (IoT), M2M, and Industry 4.0, today published a comprehensive 136-page market report on the current state of Predictive Maintenance (PdM), titled “Predictive Maintenance Report 2019-2024”.
Some of the key findings include:
- Significant cost avoidance. Customers report great ROI from their Predictive Maintenance initiatives (Typically 10-50% reduction in maintenance costs). It is estimated that $17B has been saved by organizations worldwide in 2018 alone, thanks to new, sophisticated Predictive Maintenance programs.
- Vendor explosion. The number of Predictive Maintenance vendors has doubled in 2 years (182 known vendors today).
- Maturing market. Although most projects are still in pilot and Proof of Concept stage, many projects are starting to scale – some companies are now performing Predictive Maintenance on 100k+ assets.
- Increasing role of analytics. Due to advances in AI and the increase in data points, sophisticated analytics are becoming more and more important and make up a larger share of the overall PdM budget.
- Continued market growth. The market for Predictive Maintenance solutions in 2018 has reached $3.3B and is forecasted to grow at 39% to $23.5B by 2024. It is estimated that in 2024, adopters of Predictive Maintenance will save $188B in costs on top of other benefits such as improved regulatory compliance and enhanced safety.
- Shifting challenges. Key implementation challenges have shifted from being data model-related to data quality and people-related.
Sample Report Insight:
End-user skill-shortage is the main driver for adopting automated PdM software
- End-user skill-shortage is one of the main drivers for adopting automated PdM software. Many industrial companies already possess tons of data, but often struggle to extract value from them because they lack trained employees (such as data scientists that can extract insights). These companies turn to automated AI/software solutions instead. Many start-ups (e.g., Senseye, Presenso, Uptake, SparkCognition) demonstrate rapid growth with a “data scientist in a box”-type offering. They leverage supervised and/or unsupervised Machine Learning to extract insights from data and bring Predictive Maintenance to clients even without deep data science expertise on their side.
Commenting on the report findings, IoT Analytics Managing Director Knud Lasse Lueth, said:
Report Content Overview:
The research, undertaken in the last 12 months, shows that Predictive Maintenance continues to be a hot topic and is one of the fastest growing use cases for the Internet of Things (IoT), helped by the parallel advances in many fields; namely sensing technologies, connectivity, IoT architectures, data science & artificial intelligence. In developing the 136-page report, the analyst team at IoT Analytics studied over 180 companies that offer Predictive Maintenance technology elements and reviewed 110+ implemented Predictive Maintenance projects. 9 of the leading companies and 9 case studies are presented in depth, alongside an analysis of current business models and M&A activities. The Predictive Maintenance report also calls out 8 market drivers & characteristics, 11 major industry trends as well 10 challenges/barriers to adoption, both for technology providers as well as technology users.
The Predictive Maintenance market report forecasts a compound annual growth rate (CAGR) of 39% over the time frame of 2019-2024, with annual spending reaching US$23.5 Billion by 2024.
The global share of PdM implementations is given for Internal PdM versus PdM as a Service. The market is broken down into 7 distinct categories of technology (including Hardware, Connectivity, Platform, Storage, Analytics, Applications, and System Integration & Services), 3 deployment types (private cloud, public cloud, and on-premise), and 12 industry segments (including Automotive & Transportation, Buildings, Chemicals, Consumer Products, Discrete Industries, Energy, Healthcare, Metals & Mining, Oil & Gas, Pulp & Paper, Water & Wastewater, and Other). A regional breakdown with country level deep dives is provided for APAC, Europe, MEA, North America, and South America. Note: This market data comes in an additional excel file.
Furthermore, the report includes a competitive landscape section (and accompanying database) classifying over 180 PdM vendors as well as a PdM projects database (with 110+ projects).
The Market Report is available to download HERE.
A sample of the market report can be downloaded HERE.
About IoT Analytics
IoT Analytics is the leading provider of market insights & competitive intelligence for the Internet of Things (IoT), M2M, and Industry 4.0. The boutique research firm helps more than 40,000 Internet of Things decision-makers understand IoT markets every month. IoT Analytics tracks important data around the IoT ecosystem such as market developments, M&A activity, start-up funding, company projects, use cases and latest news. Product offerings include in-depth market reports as well as bespoke research and consulting services. As a research pioneer, IoT Analytics combines traditional methods of market research such as interviews and surveys with state-of-the art web-mining tools to generate high-calibre insights.
IoT Analytics is headquartered in Hamburg, Germany. For more information, visit www.iot-analytics.com
Predictive Maintenance Market 2019-2024 – Market Report Structure – 136 pages in total
What has changed since the last report
1. Introduction to Predictive Maintenance
1.1 Definition & Disambiguation
1.2 Role in IoT & I4.0
2.1 Application areas
2.2 Technology stack
2.3 Deep-dive: Sensing techniques
2.4 Deep-dive: Analytics
3. Market Size & Outlook
3.1 Total Market & Overall Drivers
3.2 Market by Technology
3.3 Market by Industry
3.4 Market by Region
4. Competitive Landscape
4.2 Company Profiles
4.3 M&A Activity Log
5. Business Models & Case Studies
5.1 Benefits of PdM implementations
5.2 Software vendor perspective
5.3 OEM perspective
5.4 Operator/Shopfloor perspective
5.5 Case studies
6. Trends & Challenges
6.1 Major trends
6.2 Challenges & Barriers
7. Methodology & Definitions
About IoT Analytics
Companies Mentioned (selection from report)
Accenture, Bosch, Brüel&Kjaer, C3, Cassantec, Caterpillar, Cisco, Dell, General Electric, Helium, Huawei, Hitachi, IBM, Keysight Technologies, Konux, Microsoft, National Instruments, OSIsoft, PTC, Rockwell Automation, Samsara, SAP, SAS, Schneider Electric, Senseye, Siemens, Sight Machine, SKF, Software AG, Spectris plc, Splunk, Tachyus, thyssenkrupp, Uptake +160 more.
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