Enhancing Supply Chain Performance through Proactive Management: A Predictive Analytics Approach Using Weighted Regularized Extreme Learning Machine Model

Author(s)	Narendra Sharad Fadnavis
Country	India
Abstract	Supply chain management (SCM) is a crucial component of any competitive strategy aimed at increasing organizational profitability and productivity. The discipline of SCM has a wealth of literature on strategies and technologies for effective SCM. There has been a deluge of academic and professional activity in recent years devoted to metrics and evaluations of organizational effectiveness. Training the model, selecting features, and preprocessing are its main components. There are three types of normalization used in data preprocessing: min-max, z-score, and decimal scaling. The most accurate method is z-score normalization. To pick features, we employ the sine-cosine algorithm. For this purpose, we trained the model using the WRELM framework. It makes ELM and RELM look antiquated in comparison. According to the numbers, the accuracy rate is 96.20%.
Keywords	Supply Chain Performance, Weighted Regularized Extreme Learning Machine (WRELM), Sine Cosine Algorithm (SCA).
Field	Engineering
Published In	Volume 2, Issue 1, January-February 2020
Published On	2020-01-04

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