抄録
This paper presents a framework for assessment and ranking of chemical manufacturing systems based on machine productivity indicators. The integrated approach discussed in this paper is based on Principle Component Analysis (PCA). The validity of the model is verified and validated by Numerical Taxonomy (NT) approach. Furthermore, a non-parametric correlation method, namely, Spearman correlation experiment shows high level of correlation between the findings of PCA and NT. To achieve the objectives of this study, a comprehensive study was conducted to locate all economic and technical indicators (indexes) which influence machine performance. These indicators are related to machine productivity, efficiency, effectiveness and profitability. 10 indicators were identified as major indexes impacting machinery conditions in manufacturing systems. 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 industrial sectors are selected according to the format of International Standard for Industrial Classification of all economic activities (ISIC). Furthermore, Iranian industries are classified as 4-digit ISIC chemical sectors. Then, a comparative study is conducted through PCA among the 4-digit chemical sectors by considering the selected 10 indicators. PCA ranked the chemical sectors based on 10 indexes discussed in this paper. This in turn shows the weak and strong points of chemical manufacturing sector with respect to machine productivity. Furthermore, PCA identified which machine indicators have the major impacts on the performance of chemical sectors.
