A Comprehensive Review on the Role of EL-ANFIS in Shoe Demand Prediction and Supply Chain Efficiency
Keywords:
Enhanced Adaptive Neuro-Fuzzy Inference Systems' (EL-ANFIS), XGBoost, ANN, SVM, Artificial Intelligence (AI), Machine Learning (ML), Long Short-Term Memory (LSTM).Abstract
The study gives a wide-ranging review of Enhanced Adaptive Neuro-Fuzzy Inference Systems' (EL-ANFIS) role into modeling shoe demand and its corresponding supply chain. Demand forecasting remains vital for production, inventory, procurement, and distribution optimization in an industry that is undergoing dynamic changes from the very influences of consumer preferences, fashion trends, and seasonal changes. Unfortunately, due to the very complications in the markets of today, traditional methods of forecasting are no more sufficient. Thus, today's advanced technologies such as EL-ANFIS can help. Neural networks' adaptive learning and AIFs fuzzy logic reasoning are synergistically applied in EL-ANFIS to obtain great results in prediction with respect to noise and multidimensionality in data. This paper discusses the architecture of EL-ANFIS, its improved computational efficiency, convergence speed, and fuzzy rules' dynamic modification using genetic algorithms and particle swarm optimization for optimization algorithms. Comparisons are made between EL-ANFIS and traditional ANFIS and other AI forecasting methods, including ANN, SVM, and XGBoost. The study discusses how effectively the demand variability is managed with the EL-ANFIS to mold the supply chain efficiencies and consequently lower inventory holding costs, curb stockouts, and enhance the customers' satisfaction level. It also discusses the obstacles, including the high computational burden and possible overfitting, and gives directions for future studies aimed at enhancing EL-ANFIS applicability in SCM.
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