Emerging Issue Detection & Reliability Care (Open Source)
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
- 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.
EXA has built an integrated architecture providing a solution bundle serving multiple business use cases: “Emerging Issue Detection (EID)” is covering the first phase within lifetime of an asset – the infant-mortality phase during warranty period and “Reliability Care (RC)”, the second phase – the normal lifetime, during service & maintenance period before the final phase of wear-out. These two solutions are complemented by the EXA-solution “Digitization of Paperwork & Natural Language Processing (NLP)“.
All solutions can be employed independently, as well as contained individual micro services, or in synergy.
Benefits to Solution-Bundle
Emerging Issue Detection (EID)
- 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) and prioritisation of early production process improvements
Reliability Care (RC)
- 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)
- 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
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