A Study to Enhance Supply Chain Performance in Online Merchandising by Integrating Digitalization to SCOR Model using Artificial Neural Network (ANN)

Authors

Keywords:

Artificial neural networks, confusion matrix, digitalization, SCOR model, supply chain management

Abstract

Supply chain operation reference (SCOR) model delineate the business endeavors associated with all the stages for customer satisfaction and it plays a major role in online merchandising by integrating digitalization. This study aims to integrate digitalization to SCOR model for enhancing the supply chain performance (SCP) in online merchandising (SCPOM). There are numerous studies on this area which indirectly uses few of the digital strategies and tools like IoT, RFID, GPS, etc., to enhance SCP. This study has analyzed the impact of digitalization in enhancing the SCPOM. A questionnaire with 24 items has been collected from 310 respondents in India, using purposive sampling method and collected the responses from tech geeks who are using online retail platforms on a regular basis. The model validation has been done using a machine learning application (MLA) called artificial neural network (ANN) and a confusion matrix. This study has found that digitalization along with SCOR model influences the SCPOM with R2 in training is 86.98% and in testing is 93.68%. Also, in confusion matrix the accuracy, specificity, and sensitivity of the model is 98%, 94.5% and 98.2% respectively.

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ANN topology to predict the impact of digitalization in the SCOR model to enhance SCPOM

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Published

16.12.2022

How to Cite

Ramya, A. A. ., & Thangaiah I.S , S. . (2022). A Study to Enhance Supply Chain Performance in Online Merchandising by Integrating Digitalization to SCOR Model using Artificial Neural Network (ANN). International Journal of Intelligent Systems and Applications in Engineering, 10(4), 23–28. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2192

Issue

Vol. 10 No. 4 (2022)

Section

Research Article

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