Conditional Diffusion Model-Based Data Augmentation for Crop Mapping Using Sentinel-2 Time Series Images

Abstract

The existing inventory status and the amount that suppliers have recently contributed in response to shifts in demand and safety stock requirements must be taken into account via automatic replenishment. Abnormal stock behaviour cannot be promptly identified since many businesses still utilise static inventory sheets, segregated ERP reports, and delayed manual analysis. A real-time KPI dashboard for automatic replenishment inventory performance monitoring is proposed in this research. Calculate critical operating metrics including stockout risk, deviation from safety stock, inventory turnover rate, order fulfilment rate, and replenishment delay by integrating the data streams of inventory, sales, replenishment, suppliers, and demand. KPI trends, anomalous warning signals, and replenishment bottleneck diagnostics are shown in a visual decision layer. Experimental Analysis of Warning Response Time, Decision Accuracy, and Monitoring Efficiency in Different Dashboard Configurations. The suggested dashboard has enhanced stockout warning accuracy from 82.6% to 91.4%, decreased abnormal reaction time from 46.8 minutes to 18.5 minutes, and raised replenishment decision consistency by 13.2%. As a result, the new KPI display will be accessible sooner and function as an impartial standard for intelligent inventory control.