Construction of Business Model of Unmanned Economy Under Digital Technology

In the ever-evolving landscape of modern business, the integration of advanced technologies is paramount for optimizing operations, ensuring efficiency, and staying competitive. The business model for the unmanned economy comprises the challenges related to supply chain and logistics. This paper proposed an architecture of LM-LSTM (Linear Regression and Long Short-Term Memory) model within the context of the Unmanned business model. The proposed model uses the mandami based fuzzy rule for the computation of the unmanned economy. Within the mandami fuzzy linear regression model is adopted for the computation and estimation of the variables related to the unmanned economy. The objective is to provide a comprehensive analysis of the impact of this predictive modeling system on various dimensions of the business. Through the generated rules the LSTM model is utilized for the classification and computation of the features related to supply chain, forecast demand and other parameters in an unmanned economy. The examination of 10 unmanned products in Chinese products are evaluated. The findings of LM-LSTM stated that Sales forecasting, one of the critical aspects of any business, has seen a remarkable improvement in accuracy, with an average Mean Absolute Error (MAE) of 3.00%. This accuracy ensures that products are produced and stocked according to actual demand, preventing costly overstocking or stockouts. The inventory management process has been streamlined, with tailored strategies for each product category. This adaptation has resulted in reduced stockouts, efficient parts sourcing, and minimal overstock situations. Supply chain optimization has significantly reduced lead times, enhancing customer satisfaction through timely product deliveries. Customer behavior analysis, facilitated by LM-LSTM, has led to a notable increase in sales across the product range, with an average increase of 91%. This enhanced customer engagement is coupled with substantial cost savings, with an overall reduction of 118%. Downtime has been minimized, contributing to smoother operations and improved customer service.