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Predictive Maintenance for Manufacturing Industry Market Segmentation by Product Type (Solution and Service); and by Technology (Machine Learning, Deep Learning, and Big Data) – Global Demand Analysis and Opportunity Outlook 2020-2029

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Inflation And Looming Recession to Haunt Businesses:

In 2022 & 2023, market players expected to sail in rough waters; might incur losses due to huge gap in currency translation followed by contracting revenues, shrinking profit margins & cost pressure on logistics and supply chain. Further, U.S. economy is expected to grow merely by 3% in 2022.

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Purchasing power in the couPurchasing power in the country is expected to fell nearly by 2.5%. On the other hand, European countries to see the worst coming in the form of energy crisis especially in upcoming winters!! Right after COVID-19, inflation has started gripping the economies across the globe. Higher than anticipated inflation, especially in western world had raised concerns for national banks and financial institutions to control the economic loss and safeguard the interest of the businesses. Increased interest rates, strong USD inflated oil prices, looming prices for gas and energy resources due to Ukraine-Russia conflict, China economic slowdown (~4% in 2022) disrupting the production and global supply chain and other factors would impact each industry negatively.                                                         Request Insights

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Predictive Maintenance for Manufacturing Industry Market Highlights 2020-2029

Predictive maintenance for manufacturing industry market is estimated to grow with a high CAGR during the forecast period, i.e., 2020-2029. Extensive research associated with predictive maintenance for manufacturing industry in western countries, along with growing need to reduce maintenance cost and downtime are expected to fuel the progress of this market. The growth of the market can also be attributed to factors such as increase in investments in predictive maintenance in industries as a result of IoT adoption. Moreover, lack of employees and personnel, coupled with global supply chain disruption as well as high demand for various goods during the COVID-19 pandemic encouraged companies to take extra care of their manufacturing equipment and machinery to increase output. This resulted in a surge in demand for predictive maintenance solutions across the globe. However, many companies have started to use smart sensors, advanced artificial intelligence systems, and other Industry Internet of Things (IIoT) solutions to track health and efficiency of vital machinery used in their manufacturing process to avoid costly production downtimes.


Predictive maintenance techniques are formed to determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach confirms cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted. It is likely to be influenced by a range of political, economic, social, technical, and industry-specific factors.

The market is segmented based on component into software and services, out of which, the software  segment is anticipated to grab the largest share by the end of 2020, improvement of safety in factories is one of the primary concerns of the manufacturing industry. Furthermore, machine breakdowns are also causing severe production loses in the manufacturing industry. Demand for better safety, reduction of costs and machine utilization are driving the global market for manufacturing predictive analytics.

On the basis of Technology, the market is segmented into machine learning, deep learning, big data and analytics. Out of which machine learning is estimated to grab the largest market share during the forecast period 2020-2029. Manufacturers are adopting machine learning based predictive maintenance. It depends on large amount of historical or test data, along with tailored machine-learning algorithms, to test different scenarios and predict the errors in the system. Then it generates the alerts accordingly. When properly designed and implemented, a machine learning algorithm will learn the typical data’s behavior and identify deviation in real-time. A machine monitoring system will comprise input about diverse temperatures, engine speed, and others. The system can then predict the time of the breakdown. Additionally, big data analytics is projected to grab the substantial market share owning to the increasing technological advancement and dealing with the large data securely. As, the data security is one of the major concern for any organisation. Today the adoption big data technology is high because it is cost efficient, provide accurate results, and facilitates to analyse the large data set innovatively .Moreover, the interpretation helps the organisations in booting their sales and retaining customer loyalty.

Predictive Maintenance for Manufacturing Industry Market Regional Synopsis

Geographically, the market is segmented into North America, Latin America, Europe, Asia Pacific and the Middle East & Africa region. North America is expected to hold the largest market size in the global predictive maintenance for manufacturing industry market. North America is estimated to be the leading region in terms of adopting and developing predictive maintenance. The rising investments in emerging technologies such as IoT, AI, ML, the increasing presence of predictive maintenance vendors, and growing government support for regulatory compliance are the major factors expected to contribute to the market growth during the forecast period, while Asia Pacific is expected to grow at the highest CAGR during the forecast period. In APAC, the highest growth rate can be attributed to the massive investments made by private and public sectors for enhancing their maintenance solutions, resulting in an increased demand for predictive maintenance solutions used for automating the maintenance and plant safety process.


The predictive maintenance for manufacturing industry market is further classified on the basis of region as follows:

  • North America (U.S. & Canada) Market size, Y-O-Y growth & Opportunity Analysis
  • Latin America (Brazil, Mexico, Argentina, Rest of Latin America) Market size, Y-O-Y growth & Opportunity Analysis
  • Europe (U.K., Germany, France, Italy, Spain, Hungary, Belgium, Netherlands & Luxembourg, NORDIC, Poland, Turkey, Russia, Rest of Europe) Market size, Y-O-Y growth & Opportunity Analysis
  • Asia-Pacific (China, India, Japan, South Korea, Indonesia, Malaysia, Australia, New Zealand, Rest of Asia-Pacific) Market size, Y-O-Y growth & Opportunity Analysis
  • Middle East and Africa (Israel, GCC (Saudi Arabia, UAE, Bahrain, Kuwait, Qatar, Oman), North Africa, South Africa, Rest of Middle East and Africa) Market size, Y-O-Y growth & Opportunity Analysis

Market Segmentation

Our in-depth analysis of the Predictive Maintenance for Manufacturing Industry market includes the following segments:

By Product Type

  • Software
  • Service

By Technology

  • Machine Learning
  • Deep Learning
  • Big Data and Analytics

Growth Drivers

  • Growing need to reduce maintenance cost and downtime
  • Increase in investments in predictive maintenance in industries as a result of IoT adoption
  • Need to prolong lifetime of ageing industrial machinery
  • Increase in demand for embedded computer systems or IoT enabled systems to avoid unplanned maintenance downtime is driving the global predictive maintenance for manufacturing market.


  • Lack of Skilled Workforce and Data Security & Privacy Issue 

Top Featured Companies Dominating the Market

  • International Business Machines Corporation
    • Company Overview
    • Business Strategy
    • Key Product Offerings
    • Financial Performance
    • Key Performance Indicators
    • Risk Analysis
    • Recent Development
    • Regional Presence
    • SWOT Analysis
  • Robert Bosch GmbH
  • Rockwell Automation, Inc.
  • Siemens AG
  • Schneider Electric 
  • SAS Institute
  • PTC Inc.
  • General Electric Company
  • Software AG



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