Adopt artificial intelligence to maximize asset performance

Overview

The Infosys Industrial Manufacturing practice leverages an Artificial Intelligence (AI) ecosystem, spanning sensors, analytics, automation, predictive modeling, and machine learning, to maximize asset performance. Near real-time visibility into the condition of each industrial asset enables maintenance teams to minimize downtime. Condition-based maintenance boosts reliability across asset classes – from heavy engineering, farming and mining equipment to automated teller machines and power generators.

Infosys Asset Genome framework provides descriptive as well as prescriptive analytics by extracting relevant data from millions of records spanning maintenance and inspection logs, parts recall / repair / replacement history, warranty and field service records, and machine failure reports. Our framework uncovers the cause(s) of equipment malfunction, be it dysfunctional operations, subpar maintenance, or faulty supplies.

Business insights maximize the lifespan as well as return on assets by avoiding common / repeated failures via reevaluation of design, modification of procurement specifications, targeted training, and preventive maintenance. Further, it eliminates time and resources spent on unscheduled maintenance. Our tools for estimating the lifetime of equipment, components and spare parts help prioritize procurement and plan for alterative assets / suppliers. Further, the insights shape pricing strategies for maintenance service and warranty plans.

The Infosys Asset Efficiency Testbed, developed in collaboration with the Industrial Internet Consortium (IIC), maximizes uptime of industrial assets. Significantly, it rationalizes costs across the asset lifecycle by boosting efficiency of operations, maintenance and service.