article · Decision Science Letters
Modern supply chain management relies on interpreting customer sentiment, forecasting product demand, and predicting prices to guide strategic decisions and resource allocation. This research introduces an integrated methodology combining machine learning, deep learning, and probabilistic modelling applied to social media and online platform data. The framework employs the BERT transformer model to evaluate customer sentiment, a Gated Recurrent Unit architecture to forecast demand, and a Bayesian Network to predict pricing. These techniques handle high-dimensional, large-scale data and identify patterns more effectively than conventional statistical approaches. By uniting these models, the integrated approach provides commercial organisations with insight into consumer behaviour and market dynamics, assisting in the management of supply chain uncertainties and the refinement of pricing strategies.
Managing modern supply chains requires processing vast amounts of online information to anticipate market shifts. By combining advanced natural language processing and forecasting algorithms, this methodology offers organisations a practical way to gauge customer opinions, anticipate inventory needs, and adapt pricing dynamically, reducing risks associated with market fluctuations.
The methodology is aimed at commercial businesses seeking to improve pricing, inventory control, and supply chain decisions using web and social media data. Because the work is demonstrated as an integrated computational framework rather than a packaged tool or operational software, it appears to be at an applied research stage requiring further software engineering before commercial adoption.
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In the contemporary business landscape, effective interpretation of customer sentiment, accurate demand forecasting, and precise price prediction are pivotal in making strategic decisions and efficiently allocating resources. Harnessing the vast array of data available from social media and online platforms, this paper presents an integrative approach employing machine learning, deep learning, and probabilistic models. Our methodology leverages the BERT transformer model for customer sentiment analysis, the Gated Recurrent Unit (GRU) model for demand forecasting, and the Bayesian Network for price prediction. These state-of-the-art techniques are adept at managing large-scale, high-dimensional data and uncovering hidden patterns, surpassing traditional statistical methods in performance. By bridging these diverse models, we aim to furnish businesses with a comprehensive understanding of their customer base and market dynamics, thus equipping them with insights to make informed decisions, optimize pricing strategies, and manage supply chain uncertainties effectively. The results demonstrate the strengths and areas for improvement of each model, ultimately presenting a robust and holistic approach to tackling the complex challenges of modern supply chain management.
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DOI: 10.5267/j.dsl.2023.9.003
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