Objective / Business Problem
To accurately assess and forecast product demand for a retail outlet by capturing complex patterns in historical sales data, enabling improved inventory management and operational planning.
Approach / Methodology:
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Employed a Multi-Layer Perceptron (MLP) neural network model, a type of feedforward neural network well-suited for modeling both linear and non-linear relationships inherent in real-world demand data.
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Leveraged the MLP’s architecture of interconnected layers with weighted connections to learn complex demand patterns from historical sales and related input features.
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Trained the model on historical sales data, optimizing weights to minimize prediction error and generalize to unseen data.
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Validated model performance on both training and test datasets to ensure robust predictive capability.
Outcomes and Impact:
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Achieved approximately 95% prediction accuracy on both training and test datasets, indicating strong model generalization.
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Delivered reliable future demand forecasts enabling the client to better manage stock levels, reduce wastage, and improve customer satisfaction.
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Supported strategic decision-making related to procurement, promotions, and resource allocation through data-driven demand insights.
Business Value:
The neural network-based forecasting model enhanced the client’s ability to anticipate market demand accurately, leading to optimized inventory control, cost savings, and improved operational efficiency.
