IDENTIFYING PART COMMONALITIES IN A MANUFACTURING COMPANY DATABASE

• Authors: Kwapisz Jakub , Infante Virginia
• Languages of publication: EN

Abstracts

EN

Manufacturing companies that produce and assemble multiple products rely on databases containing thousands or even millions of parts. These databases are expensive to support, maintain and the inherent complexity does not allow end users to utilize fully such databases. Designers and engineers are often not able to find previously created parts, which they could potentially reuse, and they add one more part to the database. Engineered improvements without removal of the previous version of the component also cause the avoidable increase of elements in the database. Reuse of parts or planned development of common parts across products brings many benefits for manufacturers. Search algorithm utilized across part databases and varying projects allows identifying similar parts. The goal is to compare part names and attributes resulting in the assignment of a similarity score. Determining common and differentiating part attributes and characteristics between pairs of components allows nominating parts that can become shared in different products. The case study utilizes an industrial example to evaluate and assess the feasibility of the proposed method for identifying commonality opportunities. It turned out that it is possible to find many parts that can be potentially shared between different products.

Keywords

EN

Commonality; Data mining; Industrial database analysis; Part reuse; Product platform; Product family

• Publisher: Katedra Informatyki Szkoła Główna Gospodarstwa Wiejskiego w Warszawie
• Journal: Information Systems in Management
• Year: 2016
• Volume: 5
• Issue: 3
• Pages: 336-346

Dates

published

2016

Contributors

author

Kwapisz Jakub

• MITPortugal, Instituto Superior Tecnico, Universidade de Lisboa, Lisbon, Portugal

author

Infante Virginia

• LAETA, IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

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Identifiers

ISSN

2084-5537