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.
|Journal||IOP Conference Series: Materials Science and Engineering|
|Publication status||Published - 21 Dec 2020|
|Event||2020 International Conference on Advanced Mechanical and Industrial Engineering, ICAMIE 2020 - Cilegon City, Banten, Indonesia|
Duration: 8 Jul 2020 → 8 Jul 2020