When implementing engineering changes (EC) in companies many information about ECs and
associated processes is stored and forgotten. However, an extraction of information about correlations
in past ECs can have advantageous. In the decision phase of ECs, it is very crucial to identify the
relevant stakeholders and to know which further parts could be affected by the proposed EC in order
to create a good basis for decision. Especially for ECs in complex products, which can affect the
whole product lifecycle it is an important and difficult task.
This paper presents an approach of how information about past EC processes can be extracted by
knowledge discovery in database (KDD) methods in order to support the EC coordinator. The EC
coordinator gets recommendations based on past interrelations of EC data and for probably relevant
stakeholders and affected parts. Here the data mining technique association rule is applied.
The approach was developed while using a real and large database of approximately 53,000 past ECs
of a car manufacturer. A preliminary test has been conducted and the feasibility of the approach was
proven as well as first positive results.
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When implementing engineering changes (EC) in companies many information about ECs and
associated processes is stored and forgotten. However, an extraction of information about correlations
in past ECs can have advantageous. In the decision phase of ECs, it is very crucial to identify the
relevant stakeholders and to know which further parts could be affected by the proposed EC in order
to create a good basis for decision. Especially for ECs in complex products, which can affect the
whole p...
»