Representative Automation Clients
Representative End User Clients
Representative Software Clients
Asset Performance Management (APM) is an approach to managing assets that prioritizes business objectives in addition to traditional asset reliability and availability goals. APM has become a primary enabler of digital transformation for asset management among industrial companies. Modern APM combines traditional asset management practices with new digital technologies for transformation advances in reliability, maintenance execution, and business performance.
Business goals: Through digital transformation in asset and risk management, and improvements in asset availability and uptime, users achieve higher revenue and profitability while improving customer satisfaction with on-time delivery and quality.- Asset ecosystem: The ecosystem for assets extends beyond the plant floor and facilities to include a wide range of applications across asset-intensive industries that leverage sophisticated OT, IT and engineering systems and related production, maintenance, and engineering personnel. It also encompasses third party partners and other providers of parts and services.
- Digital tools: Apply modern technologies across a range of tools like smart devices, augmented reality, and mobility to improve business processes and create new methods for asset management.
- Data & analysis: Enable greater depth of collaboration across the asset ecosystem by using digital twin, digital thread, and other modern information assimilation and management approaches.
- Practices and apps: Traditional practices and applications become more effective when enhanced with data, digital tools, and support for business goals.
Asset Performance Management (APM) Business Benefits
APM involves people, processes, and technologies to improve the uptime with higher revenue and longevity of physical assets to conserve cash while reducing operating costs and business risk. APM helps assure assets have the needed capability for optimal operating performance to meet today’s dynamic business and production goals with high customer satisfaction for on-time delivery and product quality. This APM approach becomes a means to systematically improve key metrics like uptime, mean time to repair (MTTR), asset longevity, on-time shipments, quality/yield, and safety. Success with these metrics leads to improvements in executive metrics like revenue, margin, customer satisfaction, work-in-process (WIP) inventory, and return on assets (ROA).
Artificial Intelligence (AI) Enhances Asset Management
Artificial Intelligence (AI) is significantly enhancing Asset Performance Management (APM) practices by introducing more intelligent, proactive, and automated approaches to performance management. AI algorithms can analyze vast amounts of performance data in real-time, detecting subtle anomalies that might escape human notice.
Predictive maintenance (PdM) employs advanced AI modeling and machine learning (ML) technologies, to analyze hundreds of process parameters over time, as well as compare these to historical asset data. This helps manufacturers estimate wear and degradation of assets or its parts and forecast asset failure in advance. This helps improve lead times, providing operators with better information sooner so that they have more time to address the issues to avoid imminent asset failures.
Generative AI Addresses Industrial Data Challenges
Generative AI is advancing predictive maintenance by addressing major industrial data challenges. For a successful PdM program, it is imperative that the ML algorithms are trained on clean data, the right amount of data, and the right type of data. When existing data is limited or missing, GenAI algorithms can learn the underlying patterns of the existing data to imitate this data and generate synthetic data that is similar to the original data, to fill the data gaps and help improve the quality of the data. This synthetic data generated by AI can be used to fill in missing values, and create larger data sets, so that PdM programs have the necessary starting point to create reliable algorithms. While this capability to generate synthetic data by Gen AI is already being leveraged in the financial and healthcare sector, mainly to protect client confidentiality, the industrial sector is just beginning to explore various applications.
ARC Coverage Areas
ARC Coverage Areas