Automotive

From engineering to after-sales

The automotive industry is a classic example of industrialization. Automotive products also come in an extremely wide range of variations and require custom manufacturing: with so many complex configuration options, no vehicle is exactly like the other.
Good information management takes this variety into account. Like production, information processes are also industrialized and range from development to product communication, factories and diagnostics to the vehicles themselves (vehicular communications with OTX and ODX). In the opposite direction, feedback processes help companies benefit from comments, criticisms and experiences worldwide, regardless of language or market.

Information flow from source to target

Information is recorded as soon as it is created, then stored and linked semantically according to the single source principle. Engineering changes are thus automatically incorporated into all possible views and publications: changes to development parts lists are automatically reflected in replacement parts lists, diagnostic systems are updated with revised maintenance information, etc.

Personalization

A discriminating customer owns a vehicle that was configured and manufactured to his precise specifications.
That customer expects the same from his information: through personalized information products, the vehicle becomes his own and he identifies with the product. This creates a positive feeling of ownership that increases the customer's loyalty to the brand and turns him into a brand ambassador.

Intelligent linking for intelligent use

Intelligent linking is already contained within the data model and therefore does not need to be manually created. It allows the data to be used for any number of multichannel publications:
as PDFs for printed operating manuals and plant literature, standard documentation for regulatory compliance or on interactive portals with semantic queries.

Arriving at the goal of the diagnostic process faster

With semantic linking, the information model "knows" much more than simply what was entered.
Diagnostic processes are therefore not bound by decision trees but can determine the most efficient path based on the semantic network and take probabilities, labor values and other parameters into account.
Through usage analyses, you get valuable feedback on what information is used and how you can further improve your diagnostic processes.