Market evolution and high value for users.
Predictive maintenance allows you to calculate the remaining time before a machinery or technology fails: once the parameters necessary for the assessment have been identified, they are processed through mathematical models to understand when it is necessary to intervene to avoid the damage.
In recent years, this market has become an application with high return ROI, also in terms of value for users.
There are many technological developments that have contributed to the rapid expansion of the market: the increase in the connection of IoT resources, major advances in cloud services and improvements in the accessibility of machine learning (ML) / data science frameworks.
IoT Analytcs has published a detailed study (Predictive Maintenance Market Repoort 2021-2026) on what is happening and will happen in the predictive maintenance market until 2026:
In 2016, the year the study started, the global market for predictive maintenance (hardware, software and services) was valued at $ 1.5 billion, much lower than condition-based maintenance (many companies carried out interventions at regular and frequent intervals to reduce the risk of breakdowns).
Today, however, the trend is to move to predictive (in some cases already prescriptive): the market in 2021 was valued at 6.9 billion dollars, also thanks to the progress of sensors, data science, AI technologies and the decrease in costs of IoT infrastructures.
After COVID-19, the market will see a strong recovery, with an estimated annual growth rate of 31%, to reach $ 28.2 billion by 2026.
The advantages that these solutions offer are certain: savings in terms of time and maintenance costs, improved production capacity, up to zero unplanned downtime.
Return on investment
Out of 100 senior IT and industrial sector OT managers surveyed (IoT Analytics Report), more than 83% confirmed the positive ROI, and 45% reported cost depreciation less than a year, demonstrating how important it is to invest in these applications.
In the future, PdM solutions will be increasingly sophisticated and easier to implement, will bring ever greater ROI and will become a real must for industrial organizations of all kinds.
While in the past many solutions were autonomous (separate from other business applications), in the future they will be standard part of business software: today it is increasingly common to integrate different sensing technologies, control systems, maintenance software and other systems. Almost all predictive maintenance solutions are integrated, such as ERP (Enterprise Resource Planning) or CMMS (Computerized Maintenance Management System) solutions.
In 2016 it was considered essential for companies to have a team of data scientists with specific know-how for the use case, so that they could adapt digital twin data, data models and algorithms / libraries to their specifications. predictive maintenance needs. Today the use of automated PdM software and the development of “low-code / no-code” solutions is increasing, which allow non-technological experts to develop, scale and apply information on artificial intelligence without writing code, up to to an installation offer as easy as setting up an iphone (M. Akesson, CIO of GE Gas Power, 2020).
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