A review of decision support systems in the internet of things and supply chain and logistics using web content mining

Abstract

The Internet of Things (IoT) has attracted the attention of researchers and practitioners in supply chains and logistics (LSCs). IoT improves the monitoring, controlling, optimizing, and planning of LSCs. Several researchers have reviewed the IoT-based LSCs publications indexed by academic journals focusing on decision-making. Decision support systems (DSS) are in the infancy stage in IoT-based LSCs. This paper reviews the IoT-LSCs from the DSS perspective. We propose a new framework for helping decision-makers implement IoT based on the decisions that need to be made by describing a transition scheme from simple, if-then decisions to analytical decision-making approaches in IoT-LSCs. The IoT Adopter II is an extension of the IoT Adopter framework, in which a new layer called ‘decision’ has been added to enable decision-makers implementing IoT to improve the list of predefined decision-making processes in LSCs. Although academic literature review analysis provides valuable insights, a wide range of related information is available online. This study also utilizes a web content mining approach for the first time to analyze the IoT-LSCs in the decision-making context. The results show that the IoT-LSC field involves two emerging themes, blockchain supply chains and supply chain 5.0, and two mainstream themes, i.e., big data analytics and supply chain management.