Affiliations: [a]
College of Tourism and E-Commerce, Baise University, Baise, Guangxi, China
| [b]
School of Public Health and Management, Youjiang Medical University for Nationalities, Baise, Guangxi, China
Correspondence:
[*]
Corresponding author. Ninggui Duan, School of Public Health and Management, Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China. E-mail: [email protected].
Abstract: Integrating the E-commerce system with an enterprise resource planning tool can help the firm improve performance, maintain customers, and increase sales. In Enterprise Resource Planning, integration features can be provided either as developed features or as separate assignments and contributions. Problems with the online platform, improper addresses, rejected payments, and especially apparent transactions are frequent problems for online buyers. The enhanced Adaptive Ant Colony Optimization is utilized to optimize the rural E-commerce express of transportation. Several innovative routes can lower the downlink transportation cost and reach all collecting places with a fast delivery route. Convolutional Neural Networks were utilized to increase the collective innovation of the E-commerce platform and simplify network communication. E-commerce is a mechanism used to market information services and products. Hence, ERP-AACO-CNN has been designed to integrate Enterprise Resource Planning and E-commerce, and business operations can stream smoothly from the front to the back of the business. Statistics on sales orders, customers, stock levels, price, and essential performance measurement systems. The automated invoices, frequent communications, financial report preparation, product and service delivery, and material requirements planning. The most significant results will likely finance businesses that employ it as a stimulant for a wide-ranging process improvement. In addition, E-commerce is a valuable innovation that connects buyers and sellers in various corners of the globe. Customer satisfaction is projected to be more significant than fault detection at 95.2 % accuracy for the proposed method’s E-commerce system with the superior value. According to client demand, an E-commerce system is the most accurate development at a given input level, and a future ERP is 64.9% efficient. The proposed approach has a 24.5% random error rate and a 13.2% mean square error rate. A comparison of E-commerce and enterprise ERP precision to the proposed technique yields 83.8% better results.
Keywords: Adaptive ant colony optimization, enterprise resource planning, convolutional neural networks, E-commerce system
