As we move up in the supply chain (SC) from retailer to supplier, amplification in the fluctuation of order increased. To minimize this amplification, understanding of key decision variables which affects the SC is essential. So, in the present work the authors developed a novel approach to examine the structural dependencies among variables responsible for perfect order fulfillment (POF).
Interpretive structural modeling approach has been used to model the structural relationship among the key SC variables. Further, to study the driver-dependence dynamics among variables MICMAC analysis has been used. In the second phase, the influence of driver variables on the POF is investigated by using fuzzy logic.
From the results, it is observed that the variables’ delivery time, number of echelons, data accuracy and information sharing have high driving power which may help the organizations to meet challenges offered by POF. The results showed that for POF is said to be at optimum level when the number of echelons should be low and data accuracy should be high, and information sharing among all partners should also be very high.
Research on SC is classified into three categories, i.e. operational, design and strategic. In the present study authors discussed strategic variables responsible for POF which is the main limitation of the study. The work can be extended by including operational and design variables.
POF in SC network is affected by various variables. The in-depth understanding of contextual association among the variables helps the managers to improve the efficiency of the SC and reduce the bullwhip effect across the downstream SC network.
The study presents a hybrid approach to analyze the key POF dimensions, i.e. forecasting, number of echelons, information sharing, cycle time and delivery time, critical to POF in downstream SC network by developing various case settings.
