  The present paper applied Principal Component Analysis (PCA) for grouping of
machines and parts so that the part families can be processed in the cells
formed by those associated machines. An incidence matrix with binary entries
has been chosen to apply this methodology. After performing the eigenanalysis
of the principal component and observing the component loading plot of the
principal components, the machine groups and part families have been identified
and arranged to form machine-part cells. Later the same methodology has been
extended and applied to nine other machine-part matrices collected from
literature for the validation of the proposed methodology. The goodness of cell
formation was compared using the grouping efficacy and the potential of
eigenanalysis in cell formation has been established over the best available
results using the various established methodologies. The result shows that in
70% of the problem there is increase in grouping efficacy and in 30% problem
the performance measure of cell formation is as good as the best result from
literature.
