Enhancing E-commerce Management with Machine Learning and Internet of Things: Design and Development

China’s rapid progress in e-commerce and logistics warehousing has introduced a new era of efficient and high-quality industries, presenting new problems for training professionals in logistics. The need for improved collaboration within the industrial chain, increased automation rates, and greater production efficiency is driven by the seamless integration of manufacturing and logistics industries and the necessity for skilled workforce development. This research presents an innovative method that employs machine learning and digital twin AI simulation technology to tackle these difficulties. This technology enables the identification and creation of overlapping instructional scenarios in logistics and warehousing, which in turn helps students address errors and irregularities in their learning and practice. Using feature extraction, it detects specific challenges in the course and adaptively modifies teaching methods to enhance training efficiency. Moreover, digital twinning technology is utilized to deconstruct effective warehouse logistics models and include them in educational courses, combining conventional teaching resources and practical examples to enhance learning. The software package utilizes a cohesive Lego-style interface, allowing for the physical retrieval of digital twin courseware and the capacity to adapt to various settings. Thorough monitoring of teaching and learning details enables education management and learners to track progress and improve learning outcomes. Moreover, this study is in accordance with the ideas of the knowledge economy as it highlights the strategic management of knowledge assets to stimulate innovation and enhance competitiveness in the logistics industry.