Emerging Issue Detection & Reliability Care (Open Source)
Value driving business case solutions in manufacturing digitization Request ContactValue gain in warranty management and service & maintenance
Warranty costs for manufacturing companies range from 2% to 7% of annual revenue. (1)
According to a study, maintenance costs in Europe are over 450 billion euros, around 300 billion euros of which, can be influenced by targeted improvement. The estimated savings potential is around 70 billion euros per year. (2)
In today’s markets, it has never been easier to build hardware and electronics yet making a profit has never been more difficult. The world is becoming an increasingly competitive place and only companies capable of setting themselves apart from the competition with software and services will survive and flourish.
(1) Warranty Week
(2) ConMoto study “Value-oriented maintenance – the strategic dimension of the wrench”
Open approach to reducing machine failures
Major reasons for machine failures which can be addressed by producers are – shortcomings in design, and parts produced outside design specifications.
As a result, clients experience different performance of machines being manufactured and sold.
Market competition in face of IIoT sooner or later pushes companies to use Big Data technologies, AI (Artificial Intelligence), Deep Learning and Natural Language Processing/Textmining to become alert to emerging issues or bad operational condition of machine parts early, analyse root causes and give recommendations for efficient design, production processes as well as service & maintenance procedures.
Building a business case and verifying it, is where companies can benefit most from the best practice approach provided by EXA. You can get started fast with pre-built solutions based on cost effective Open Source technology, use the minimum necessary feature set and gain trust to solutions being supported by experienced and skilled Business Analysts, Consultants, Big Data Architects and Data Scientists.
Benefits to Solution–Bundle
Emerging Issue Detection (EID)
Emerging Issue Detection (EID) covers the first phase within lifetime of an asset – the infant-mortality phase during warranty period.
Identify emerging issues
and root-cause factors sooner
Reduce warranty costs
Diminish risk to negative publicity and gain greater customer loyalty
Support in RCA (root-cause-analysis)
Prioritisation of early production process improvements
Reliability Care (RC)
Reliability Care (RC) covers the second phase – the normal lifetime, during service & maintenance period before the final phase of wear-out.
Optimize operational costs
and capital asset investments
Save scarce maintenance budgets
through dynamic support & maintenance plans based on historical data, predictive methods and/or live sensor data
Digitization of Paperwork & Natural Language Processing (NLP)
EXA-solution “Digitization of Paperwork & Natural Language Processing (NLP)“ complements the above two solutions.
Digitize paper based processes
and make information available for analytics and decision making
Simple and fast service & maintenance report
as well as warranty claim processing
Textmining of free-text data in reports
Efficiency gains
in service through higher data quality
All solutions can be employed independently, as well as contained individual micro services, or in synergy.
Features
Asset Diagnostic & Decision Support Center
Accurate, continuous, near-real time assessment
on asset’s infant and normal lifetime health and dependencies
Explore, analyze, diagnose - condition and risk
to “population of assets, individual asset”, derive process and warranty & maintenance advisories based on 360° KPI view to assets
Understand structural/hierarchical dependency
Importance, condition of assets parts, operational risk to parts and aggregated risk to compound asset
Integrated architecture serving multiple use cases on Open Source technology
Pre-built solution for integration and analytics
of service & maintenance, quality, warranty, social media, sensor data, supporting IIoT & Industry 4.0 scenarios
AI, Deep Learning, Machine Learning and Text Mining
NLP, Document Classification, Sentiment Analysis, … – Pipelines available to process and map sensor, maintenance data#
Deploy lean, performant runtime environment
Cloud – Azure, AWS, Open Stack; Hadoop, Docker, Kubernetes, Spark, Kafka, Python, … at scale, elastic and near-real-time
#Available as an individual micro service via API
