An integrated PCA DEA framework for assessment and ranking of manufacturing systems based on equipment performance

This paper seeks to present an integrated principal component analysis (PCA) data envelopment analysis (DEA) framework for assessment and ranking of manufacturing systems based on equipment performance indicators.

The integrated framework discussed in this paper is based on PCA and DEA. The validity of the integrated model is further verified and validated by numerical taxonomy (NT) methods.

The results of the integrated PCA DEA framework show the ranking of sectors and weak and strong points of each sector with regard to equipment and machinery. Moreover, a non‐parametric correlation method, namely, Spearman correlation experiment shows high level of correlation among the findings of PCA, DEA and NT. Furthermore, it identifies which indicators have major impacts on the performance of manufacturing sectors.

To achieve the objectives of this study, a comprehensive study was conducted to locate all economic and technical indicators which influence equipment performance. These indicators are related to equipment productivity, efficiency, effectiveness and profitability. Standard factors such as down time, time to repair, mean time between failure, operating time, value added and production value were considered as shaping factors. The manufacturing sectors are selected according to the format of International Standard for Industrial Classification.

The modeling approach of this paper could be used for ranking and analysis of other sectors in particular or countries in general.