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SKYLYZE Big Data Analytics for Manufacturing Industry

SKYLYZE Automotive Warranty Management

The SKYLYZE warranty management solution is aiming at suppliers in the automotive business. From the perspective of data the warranty period is the most precious period for the supplier because the OEM provides him with substantial information about customer claims, part exchange and occurred failures.
Furthermore OEM customer claims go regularly along with recourse claims to the supplier in this period. It is therefore crucial for the supplier to as well check incoming claims on plausibility and to analyze failures from the field and be thus able to gain information about the product’s behavior during its life circle.
For this SKYLYZE offers an ingenious solution, which covers „intelligent automated claim validation“ as well as „field data analysis“ by means of suitable analytic methods and special dashboards.

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SKYLYZE Field Data Analytics

Especially in the automotive sector the observation and analysis of field data are a binding requirement for all suppliers (e.g. by VDA). As a result the OEMs commit their suppliers to corresponding activities. The SKYLYZE solution for field data analysis can easily be transferred from the automotive sector to all other domains of the manufacturing industry.

The analysis of customer claims from the field is one of the most effective actions in order to monitor and  control product quality. SKYLYZE collects such customer claims data from all relevant data sources and systems by means of its efficient Data Integration Engine and transfers the data to an optimized Data Warehouse, now ready for the interactive analysis by the SKYLYZE Analyzer Application.

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SKYLYZE Predictive Analytics

You will be in a position to predict possible future developments as well as assess adequate action via predictive analytics on the basis of identified significant patterns and dependencies found in your data.
In this context Data Mining methods and tools are an essential part of Predictive Analytics solutions.

Classic data mining methods include for example clustering, modelling of decision trees as well as neural networks. Every Predictive Analytics task requires the interaction of data mining know-how, the individual domain know-how on the customer side as well as an efficient analytics platform as a tool support.
A typical use case in the field of predictive analytics is the predictive maintenance case.

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