Open AccessIdentify product families using cluster analysis: case study in Passenger Car Radial (PCR) tire product
Author(s) -
Rere Nugrahita,
Isti Surjandari
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/909/1/012057
Subject(s) - variety (cybernetics) , product (mathematics) , component (thermodynamics) , cluster (spacecraft) , production (economics) , automotive industry , order (exchange) , set (abstract data type) , component analysis , product type , computer science , manufacturing engineering , engineering , mathematics , business , economics , thermodynamics , programming language , aerospace engineering , physics , geometry , macroeconomics , finance , artificial intelligence
Manufacturing companies, such as tire manufactures are facing great challenges to cope with increased product variety which induced by customer demand. This variety lead to higher internal complexity in term of design and production. Thus, variety has to be well-managed in order to guarantee the positive outcome for company. One of the solution is to have a well-structured product family. In this research, products data are partitioned into clusters by applying cluster analysis for mixed-type data based on their general characteristic and component specification. Variants within cluster have similarities in term of characteristics and main product component used in production. By applying k-prototypes algorithm to handle these mixed type data, the data set is clustered and interpreted into eight different clusters using selected variables.