Pocket AISME Questionaire SME

This study investigates the integration of weather-based machine learning (ML) models in improving supply chain resilience and market forecasting accuracy for SMEs in Zambia. Using Structural Equation Modelling (SEM), the study assessed how weather-integrated forecasting systems affect demand prediction, inventory optimization, customer retention, and marketing strategy refinement. SMEs leveraging weather-enhanced ML models achieved a 63.8% improvement in order prediction accuracy, enabling more responsive and data-driven decisions. With some seeing a notable 46.1%  increase in order predictability accuracy and 61.4% been able to spot market opportunities. By embedding analytics into operational workflows, the research contributes to a practical framework for adaptive supply chain management and resilience building in emerging markets. The total effects on spotting market opportunities and system usability for business planning showed a positive relationship. The study also explored the relationship between weather patterns and market characteristics, correlating item popularity with system forecasts. An intuitive application for SMEs to capture market forecast data was developed, focusing on user comfort and application usage challenges. The study also investigated the long-term resilience and sustainability of the forecasting system, impacting changes in business and marketing practices. By embedding analytics into operational workflows, the research contributes to a practical framework for adaptive supply chain management and resilience building in emerging markets, helping SMEs with reduced wastage, better demand planning, and improved customer retention.