SELECTING THE EFFECTIVE FACTORS ON THE HIERARCHY OF MANUFACTURING OUTPUTS FROM MODIFIED SCOR METRICS

Manufacturing strategies will let the factories be a market winner in some of the manufacturing outputs of cost, quality, delivery, exibility, and innovation by adjusting their manufacturing levers and production systems. The factors of driving supply chain, relating to these manufacturing outputs, are too many to control, and managing them is essential for world-class manufacturing. That is why this study applied the Supply Chain Operations Reference (SCOR) metrics and Delphi method to categorize the eective factors on manufacturing outputs in procurement, production, and sale levels, known as internal supply chain. As these manufacturing outputs and their hierarchy were inter-connected, this study was conducted to distinguish critical factors, metrics, and levers in uencing most prominently on them. To cope with this inter-relationship, group decision making techniques, Decision Making Trail and Evaluation Laboratory (DEMATEL), and hierarchical weighing approach, with MATLAB programming, were used by the view of 35 Iranian Industrial experts and mangers in the supply chain. The conclusion selects the driving variables which give and receive the most in uences among manufacturing outputs of cost, quality, delivery, exibility, and innovation in procurement, production, and sale units, which are adhered to internal supply chain. The results at the rst level of hierarchy show that Iranian supply chain managers prefers cost and quality rather than exibility and innovation, which prove that they are still in primary level of growth in life cycle. Then, the lowestlevel priority claries the necessity of Iranian managers to receive training of information systems and communication technologies which can connect supplier and customer factories with manufacturing factories, such as on-line purchase and sale, to decrease bullwhip eect. Rather, this selection procedure can reduce the number of aective factors and be used as a pre-assumption for dynamic system modeling of supply chain for choosing their environment, which is usually confusing due to lots of interacting variables.