Modelling Supply Chain Information Collaboration Empowered with Machine Learning Technique

Abstract

Information Collaboration of the supply chain is the domination and control of product flow information from the producer to the customer. The data information flow is correlated with demand fill-up, a role delivering service, and feedback. The collaboration of supply chain information is a complex contrivance that impeccably manages the efficiency flow and focuses on its vulnerable area. As there is always room for growth in the current century, major companies have shown a growing tendency to improve their supply chain's productivity and sustainability to increase customer consumption in complying with environmental regulations. Therefore, in supply chain collaboration, it is a precarious problem to find the best approaches to achieving business intentions, and most organizations prefer to partner with reputable and viable firms. In this respect, machine learning methodology such as Support Vector Machine is used to jeopardize the supply chain information collaboration. More specific efficiency is obtained from the more productive device model. Simulation results show that by adopting the proposed model and applying the Support Vector Algorithm, 98.99 percent accuracy is obtained by training, 98.91 percent by testing, and 98.92 percent from validation. It is clinched that this model will revolutionize the supply chain information collaboration patterns and will provide a significant competitive edge for business sustainability.