AI-Driven Supply Chain Management in the United States: Machine Learning for Predictive Analytics and Business Decision-Making
DOI:
https://doi.org/10.48047/s7cc5r20Keywords:
Supply Chain Management (SCM); Predictive Analytics; Convolutional Long ShortTerm Memory (ConvLSTM); LADE (Last-Mile Delivery); Data-Driven Decision-MakingAbstract
The growing complexity of the global supply chains, the dynamic market environment, and expedited digitalization require the use of sophisticated data-driven models of operational forecasting and decision-making. This paper presents a proposed AI supply chain management predictive analytics framework based on the approaches of machine learning and deep learning to
improve the predictability and business acumen of the company. Based on the LADE (Last-Mile Delivery) data set, which focuses on based on the United States of America operational data, a range of models, such as linear regression, random forest, XGBoost, and a hybrid Convolutional Long Short-Term Memory (ConvLSTM) network, has been created and measured. The suggested ConvLSTM network combines convolutional layers of localized timebased feature extraction and LSTM layers of long-term sequential dependencies. Compared outputs prove that the ConvLSTM is better than all the baselines with the RMSE of 466.33, MAE of 234.86, and R2 of 0.988, which reflect a good ability to predict and generalize. It has been experimentally confirmed that the hybrid architecture is effective in modeling nonlinear dynamic relationships in the supply chain data to generate robust and accurate revenue forecasts This study is added to the ever-expanding body of AI-powered supply chain intelligence by showing how deep sequential learning structures can be used to deliver actionable recommendations in both strategic and operational management. The next step in future studies includes incorporating more external data streams, the attention-based or transformer design, and the development of real-time decision-support systems that will enhance dynamism and performance in complicated logistics settings.
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