Statistical techniques for big data analytics in IoT-enabled green supply chain management: a survey

Abstract

In the manufacturing operation, intelligent Supply Chain Management systems (SCMS) can improve the quality of products, reduce cost, and accelerate the decision making process. The incorporation of environmentally sustainable processes into SCMS minimizes the overall environmental impact which is the target of Green Supply Chain Management (GSCM). The intelligence of the GSCM systems makes the business smarter. For this reason, it is always a concern to utilize cutting-edge ideas and technologies to optimize the operation of these systems. Internet of Things (IoT) is a promising Information technological (IT) concept that allows environmental objects to communicate with each other automatically and without human intervention. IoT is one of the most important IT solutions that provides intelligence and sustainability to GSCM systems. The significant feature of IoT is the huge volumes of data, called "big data" generated by the IoT sensors, installed on the different entities of the chain. To this end, big data processing in real time is a need for decision makers to preserve their companies" competitive advantage. There are many big data analytics techniques in the literature to target this issue. Our work will focus on surveying the statistical techniques that can be used in the analysis of big data generated from the IoT sensors situated on the different parts of GSCM to