// ROBOTICS AND SMART FACTORIES TERM
Prescriptive Maintenance
An advanced maintenance strategy that uses data to predict when equipment will fail and then recommends the exact actions to take to prevent that failure. It goes beyond just predicting.
TECHNICAL DEFINITION
Prescriptive Maintenance is a sophisticated maintenance strategy that utilizes real-time sensor data, historical performance, predictive analytics, and machine learning to not only forecast equipment failures but also to recommend specific, optimal actions and timing for maintenance interventions to prevent downtime and maximize asset lifespan.
BACKGROUND
ANSI/ASHRAE/IES Standard 90.1: Energy Standard for Buildings Except Low-Rise Residential Buildings is an American National Standards Institute (ANSI) standard published by ASHRAE and jointly sponsored by the Illuminating Engineering Society (IES) that provides minimum requirements for energy efficient designs for buildings except for low-rise residential buildings. The original standard, ASHRAE 90, was published in 1975. There have been multiple editions to it since. In 1999 the ASHRAE Board of Directors voted to place the standard on continuous maintenance, based on rapid changes in energy technology and energy prices. This allows it to be updated multiple times in a year. The standard was renamed ASHRAE 90.1 in 2001. It has since been updated in 2004, 2007, 2010, 2013, 2016, and 2019 to reflect newer and more efficient technologies.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Proactive Maintenance
- Optimized Maintenance
- AI-Driven Maintenance
- Smart Maintenance
USAGE NOTE
Prescriptive maintenance helps factories schedule repairs precisely when needed, avoiding unnecessary downtime and costs.
DEVELOPERS
Organizations developing technology related to Prescriptive Maintenance.
Siemens provides a comprehensive portfolio of digital industrial solutions, including MindSphere and Xcelerator, which leverage AI, IoT, and digital twins to enable predictive and prescriptive maintenance strategies for manufacturing and industrial assets.
GE Digital's Asset Performance Management (APM) suite utilizes advanced analytics and AI to not only predict equipment failures but also prescribe optimal maintenance actions to improve reliability and operational efficiency.
PTC's ThingWorx platform and its industrial IoT solutions enable manufacturers to connect assets, collect data, and apply advanced analytics to move beyond predictive maintenance to prescriptive recommendations for asset optimization.
IBM Maximo Application Suite integrates asset management with AI and analytics to provide deep insights into asset health, predicting potential issues and recommending specific maintenance actions to prevent downtime and optimize performance.
SAP's Intelligent Asset Management solutions, including SAP Predictive Maintenance & Service, help organizations transition from reactive to proactive and prescriptive maintenance by analyzing data to recommend optimal interventions.
Rockwell Automation integrates advanced analytics and AI into its FactoryTalk software suite to provide manufacturers with insights that extend to prescriptive maintenance, suggesting specific actions to resolve issues and improve asset performance.
Schneider Electric's EcoStruxure platform offers a range of solutions that leverage IoT, cloud, and analytics to provide predictive and prescriptive insights for industrial assets, optimizing energy efficiency and asset reliability.
C3 AI provides an enterprise AI platform and applications, including C3 AI Reliability and C3 AI Predictive Maintenance, which apply machine learning to vast datasets to predict failures and prescribe optimal maintenance strategies.
SparkCognition specializes in AI-powered analytics and cognitive solutions, offering prescriptive maintenance platforms that leverage machine learning to analyze operational data and recommend optimal actions to improve asset uptime and efficiency.