An AI Enabled Framework for Accurate Shoe Demand Forecasting and Supply Chain Optimization Using Enhanced Adaptive Neuro Fuzzy Inference System (EL ANFIS)
Keywords:
Demand forecasting, EL-ANFIS, Neural Networks, Fuzzy Logic, Supply Chain Optimization, Shoe Sales, Inventory Management, MATLAB, AI in SCM, Predictive Analytics.Abstract
The study examines Artificial Intelligence enabled framework used for sophisticated demand forecasting and supply chain optimization using Enhanced Adaptive Neuro-Fuzzy Inference System (EL-ANFIS). Most importantly, accurate forecasting is a need for effective inventory management, production planning, and disruption of supply chains. The model worked on demand forecasting which collects the past sales data from the years 2016-2019, and fuzzy logic combined with neural networks handling nonlinear, dynamic demand patterns. Implemented in MATLAB using a multi-level approach of collecting data, fuzzy rule-based forecasting, and inventory adjustment forecasting. The model generates weekly forecasts about different sizes of shoes that create a real-time scenario in identifying the gaps with demand and inventories and acts before stocks are lost or sales are lost. EL-ANFIS architecture had around five different layers from which each one contributes to a high-precision outcome for demand prediction using fuzzy membership functions and firing strength normalization and consequent parameter learning activities. Comparative analysis results show that the proposed model reaches an overall accuracy of about 99% and is superior compared to the traditional models of ARIMA and SVM at both runtime and cost-effectiveness in running-on-system criteria while adapting to market changes accompanied by a minimal prediction error. Lastly, the paper demonstrates real-case applicability of this particular model in providing solutions that are scalable and intelligent with respect to the modern challenges in the supply chain not only applicable in the footwear industry but also beyond.
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