Recently, GE Aviation and Accenture teamed together to create Taleris (Source: Aviation Week, July 1, 2013, Airline Intel), a company which delivers Intelligent Operations Services for Airlines and Cargo Carriers, to harness mature predictive analytics and prognostic technology with parametric and non-parametric information to provide meaningful outcomes to petabytes of disparate data for the aviation industry. In simple terms, this relationship brings together structured and non-structured data, where analytics are performed using predictive models so the airline operation could have optimized planning and implementation. At first glance, this operational nirvana of providing near-real time decision making ability seems impractical due to the difficulties in cost effectively analyzing incredibly large amounts of data. A near-utopia state would further include the fusion of external data, that is unstructured and sentiment based from social media.
The integration of data is gravely needed in all the manufacturing sectors, including automotive, industrial, electronics, aerospace, healthcare and medical devices. There are a lot of enterprise systems bearing the alphabet soup of acronyms like ERP, CRM, SCM, SRM, PLM, EAM, QMS to name a few. Each one generates megabytes of data on a continuous basis, each doing its own thing, and each not very aware of the relationship other systems hold. What this fails to provide is impactful information gleaned from the fusion of related data without using some sort of relationship models.
When data is entered into the quality management system, even within processes like customer complaints, CAPA, supplier management, etc., people tend to look at them individually. But those days are over. TrackWise EQMS not only provides a platform to manage these individual processes but also provides flexibility and configurability for repeatable and predictable business processes suitable for a specific industry. TrackWise Analytics component integrates the data, or fuses the related data, through the automatic generation of the semantic layer as the report data warehouse to allow the end user to create the metrics, outputs, charts etc. that brings operational information for effective decision-making.
Analytics provides a platform for future predictive models to bring forward leading indicators that would allow companies to minimize the effects of disruptive events. This becomes even more powerful when social media, unstructured data, and customer sentiments are included as part of the predictive models. This level of self-service analytics is essential to today’s manufacturing effectiveness since companies are facing tough competition, disparate supplier networks, greater scrutiny of margins (i.e. cash flow) and complex manufacturing operations.
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