Predictive Analytics for Sales Demand Forecasting Using HML Model

Demand forecasting is essential for strategic planning in various industries. Accurate sales forecasts enable better decisions in inventory management, resource allocation, and financial planning. By using advanced machine learning algorithms, sales forecasting can be significantly enhanced. The idea of advancement is creating a Demand Forecasting Software that utilises HML - Hybrid Machine Learning Model (LSTM and XGBoost algorithms) to deliver precise and actionable sales predictions. This software processes datasets from sources like historical sales data, market trends, and customer behaviour. LSTM, a specialised recurrent neural network, captures complex temporal correlations, making it ideal for predicting sales influenced by seasonality, promotions, and other time-dependent factors. XGBoost, excels at identifying interactions between different dataset features, improving predictive accuracy by recognizing intricate patterns. The accuracy rate from the HML model is about 92.5%. This empowers firms to optimise sales strategies and maximise profits by adapting to market changes and continuously learning from new data.