A high data quality of part information (master data, classifications, etc.) is the precondition for achieving your targeted potential benefits at all. Without a minimum level of data quality – apart from the possibilities of part maps and 3D search – it is not possible to standardize, consolidate, avoid parts, master part proliferation and calculate target prices and target manufacturing costs. But that´s not all: A high data quality is required to enable searching and finding and to achieve acceptance of the search systems.
To ensure that the creation or provision of part information in sufficient quantity and quality does not become a mammoth task that consumes resources, PartExplorer provides you with the perfect data cleansing tool and an excellent classification system, which offers all imaginable automatisms for cleansing of data, classifying of parts and assigning them to commodity groups. But that´s not all: of course we will not leave you alone with your data cleansing project. A competent and experienced team is always available to convey best practices and to implement even the most complicated set of rules and automatisms. This allows data to be cleansed by a factor of ten or even faster than with conventional methods.
The possible use cases for data cleansing are almost inexhaustible. Below you will find some recurring examples:
- Analysis of terms and spelling variants for creating catalogs of generic names and harmonizing generic names.
- Automatic creation and harmonization of descriptions and generic texts (e.g. material short texts, purchase order texts, long text).
- Rule-based assignment of parts to classes or commodity groups via specific assignment rules to meta data, such as generic name, size, Ecl@ss class number, standard, etc.
- Machine learning principle for automatic assignment of parts to classes or commodity groups by assigning a part to a class or commodity group. Thenceforward similar parts will always follow fully automated as if by magic.
- Automatic fill-in of class-specific attributes by
a) extraction from text attributes of part master data;
b) tapping the provided standard and supplier parts catalogs;
c) calculation via formulas or transformation from other attributes or from user inputs;
d) automatic import and mapping of Ecl@ss data records or other data records of your suppliers;
e) automatic measurement of the 3D model;
f) inheritance from the most similar part;
g) inheritance along the product structure;
h) and much more
Thus, PartExplorer takes over a large part of the manual classification work. Only the unavoidable remaining manual effort remains. This way classification is efficient! The automatisms support you both in the phase of data preparation – e.g. when setting up a classification – as well as in the course of the running application – hence when classifying new parts within the scope of an integrated creation and classification process. But you only define the rules once. They will be applied in both phases.
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Benefit of cleansing data and classifying automatically:
- Improve data quality
- Gain time
- Avoiding unnecessary part proliferation and variance
- Reducing useless part proliferation and variance
- Mastering part proliferation and variance
- Reducing purchase prices and manufacturing costs
Measures to achieve data cleansing and automatic classification:
- Process planning and implementation concepts