Enhancement of Supply Chain Management through Educating Key Processes with the Hub and Spoke Model Based on Multi-Adaptive Spider Monkey Optimization Algorithm

Abstract

Supply chain performance improvement has become a critical issue for gaining a competitive edge for companies. Over the last decade of the evolution of supply chain management (SCM), a steady stream of research and articles dealing with the theory and practice of SCM have been published, but the topic of performance measurement has not received adequate consideration. However, many critical drawbacks prevent the existing performance measurement systems from making a significant contribution to the development and improvement of SCM and thus, several researches is still needed in this area. So, we present a unique multi-adaptive spider monkey optimization algorithm (MASMOA) to enhance the ability of hub and spoke model to educate the key process throughout the SCM. Initially, for the pre-processing step, we use a normalizing technique to remove unwanted noises from the raw dataset, and then we use principal component analysis (PCA) to extract the unwanted features from the pre-processed data. The information was then encrypted and fed into the hub-and-spoke model for data protection purpose. The proposed method is then used to optimize the hub and spoke model's transport process over hubs. The optimized data is then decoded using a decryption technique to transform the encrypted information into actual format. The Rivest–Shamir–Adleman (RSA) algorithm was used for the encryption and decryption operations. Finally, performance measures such as encryption and decryption execution time, security rate, and scalability level are compared to comparable techniques. Using the MATLAB environment, the results are shown.