DigiTwin

Overview

Organizations today face an increasing demand for shorter time-to-market, a need to simulate performance outcomes and tackle issues real-time of their assets. Compounding this challenge is

1. Organizations' lacking the ability to monitor assets (during various stages of the asset's lifecycle) real time potentially leading to downtime and delays.
2. Data sitting across multiple siloed applications reducing the ability to obtain a complete overview of an asset.

By creating an exact replica of any asset virtually (a digital twin), organizations are able to not just identify any process failures before the product goes into production, but they are also able to effectively gather predictable and prescriptive insights. While 62% of the organizations have identified the need and trying to automate / adopt digital twin technology only 13% of the organization have been successful so far.

Yexle's DigiTwin solution ingests data and replicates processes in a virtual environment so organizations can obtain valuable insights, predict possible outcomes that the real-world product might undergo and pro-actively recommend options to repair, service or replace these products thereby improving the processes and performance of these physical objects

Key Features & Functionality

• IoT to monitor the assets real-time
• AI ML to identify damage, categorize issue types
• Configurable workflow task assignment and case assignment on Appian
• Prescriptive analytics to intelligently identify issues and suggest in Repair vs. Replace decisions (Improve Total Expenditure – TOTEX)
• Predictive Analytics to Identify areas of frequent issues
• Raise case to monitor whether the issue identified needs verification, repair or replace
• Case management to re-assign, instruct for replacement

Benefits & Business Impact

• Faster go-to market
• Reduced resolution time
• Pro-active (not reactive) issue resolution
• AI driven insights enabling cost savings and improved efficiency
• Continuous learning by machine learning helps improve data quality and enables AI to improve it's results and insights over time
• Improved straight through processing (STP) and resolution of issues over time due to continuous learning by AI and ML, thereby reducing repetitive tasks
• 24/7 availability and issue resolution
• Transparency and adherence to regulations