The new DNA for Service Ops Intelligence

We see manufacturers and service Providers of Enterprise complex equipment being reactive to service.  We  use ML and IOT (where applicable) to not just be proactive but to drive efficiency around device, people and parts in Service

Our Applied Machine Learning solution takes device data exhaust and service records to predict failure.  Using device prediction as a baseline, we again use ML to get right person with the right part to the right place at the right time.  As data input increases, we go beyond service efficiency to service revenue and then to device as a service.  Using eKryp, Service costs can be reduced by over 30%, and parts inventory by 10% by accurately predicting failure, and parts demand.   

eKryp, an Intelligent Service AI solution delivered as SaaS provides predictive insights on device, parts and people by continuously learning from your service, field assets, and customer data.

How it Works?

eKryp  uses artificial intelligence and machine learning to consume complex enterprise  data into meaningful actions within Service's context  using Mean Work Between Failure (MWBF) approach:

  1. Focuses on Work (n-dimensional across usage, features, geographical, models) in servicing a device along with a function of Time.
  2. Converts all the time-based series data to Work-based models to pick the right model to the type of question that needs to be answered.
  3. Provides answers back as a function of Time based on the questions.
  4. Answers then feed into eKryp application modules to optimize service operations, preventative maintenance schedule, inventory, technician skill set, etc.,

For the owners of the assets and procurement team, we strive to bring in JIT (Just In Time) service recommendations. Even with a minimum data set (your asset details and maintenance records), we show significant improvements from day one. As your organization and data matures, we add enhanced datasets.

Service Efficiency

Service Parts

Predicts future parts needs based on past parts replacement and demand trends


  • What is the parts replacement trend? Necessary? Compare technicians?
  • Refurbished vs. OEM part?
  • Parts need over life of equipment? Parts life time demand?
  • Types of Parts across equipment?

Device

Deep insights into future failure trend and device failure trends to increase Uptime


  • How do I predict device downtime so we can fix before the device goes down?
  • Which components within the target equipment are causing issues?
  • Do we refresh device or continue to maintain?

Service Personnel

Zoom in on people to reduce Service Operations cost so your margin on service increases


  • What events predict the maintenance schedule better?
  • How do I reduce multiple service calls? Issues causing re-dispatch?
  • Can we predict and fix other issues?
  • How do I optimize field service plan?

Want to learn more?

Download our Service Intelligence Whitepaper