Urnauer, Christian ; Metternich, Joachim (2022)
Product Family Identification Based on Data Analytics.
In: Procedia CIRP, 107
doi: 10.1016/j.procir.2022.04.067
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
The value stream method is the most widely used method for analyzing material and information flows in production and designing a new, leaner target state. The first step of the method is the product family selection based on similarities in the processing steps that products pass through. The increasing availability of data in production enables the value stream method to be assisted through data analytics. This paper presents a data-based approach for product family identification. Based on transaction data, two forms of product family matrices can be derived. These are converted into a more easily interpretable form with the help of sorting algorithms. Based on this, cluster analyses are carried out, which efficiently generate suitable suggestions for grouping product families, especially in the case of large product ranges.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Urnauer, Christian ; Metternich, Joachim |
| Art des Eintrags: | Bibliographie |
| Titel: | Product Family Identification Based on Data Analytics |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP |
| Jahrgang/Volume einer Zeitschrift: | 107 |
| DOI: | 10.1016/j.procir.2022.04.067 |
| Kurzbeschreibung (Abstract): | The value stream method is the most widely used method for analyzing material and information flows in production and designing a new, leaner target state. The first step of the method is the product family selection based on similarities in the processing steps that products pass through. The increasing availability of data in production enables the value stream method to be assisted through data analytics. This paper presents a data-based approach for product family identification. Based on transaction data, two forms of product family matrices can be derived. These are converted into a more easily interpretable form with the help of sorting algorithms. Based on this, cluster analyses are carried out, which efficiently generate suitable suggestions for grouping product families, especially in the case of large product ranges. |
| Freie Schlagworte: | business analytics, cluster analysis, data mining, value stream mapping |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > CiP Center für industrielle Produktivität |
| Hinterlegungsdatum: | 07 Nov 2022 10:14 |
| Letzte Änderung: | 07 Nov 2022 10:48 |
| PPN: | 501257152 |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum