Forecasting Emerging Product Trends in Smart Supply Chains

Abstract

This paper aims to develop innovative solutions using multi-modal data to predict emerging product trends in the market. By combining data flow, historical product information, and the latest market open data, our solution empowers companies to proactively adjust their supply chain and grasp emerging market trends, leading to supply chain optimization and enhanced consumer satisfaction.  The proposed solution involves the establishment of machine learning models for big data analytics, focusing on product selling, consumer behaviors, and emerging markets. The first part of the solution is applying various quantitative methods to forecast the sales of the products available in the dataset. Then, we can process and analyze unstructured data like product reviews and social media posts by applying natural language processing (NLP) and time series analysis, providing insights into psychological and cultural factors. We use data dashboards to present Integrated Analysis, Sales Forecast, and Customer Review Analysis. The generated Data dashboards can be used for effective supply chain and e-commerce data management. Forecasting product trends allows companies to optimize supply chain management and adjust strategies based on accurate predictions. The approach offers valuable customers and market insights for informed decision-making.