Adaptive Supply Chain Systems
Parthasarathi Ramakrishnan, Yongsheng Ma · 2018
Internet of Things (IoT) has evolved in the recent days, connecting almost everything, starting from electronic devices to people, products, and machines together. Data collected from these connections serve as the basis for solving any real-world problems. In a supply chain network, several data are collected across different players and at different levels. The collected data are processed using different techniques to transform data into meaningful information. This gathered information is internal to the organization, helps in improving the internal or immediate supply chain. The information collected can be stored centralized where all the supply chain partners can access and share required information, leading to performance improvement of the entire [1] supply chain. A research framework is proposed which aims to achieve performance improvement along the entire supply chain. The framework starts by explaining how data collection can be done smartly using IoT. Next, how big data analytical tools can be used to transform data into information is discussed. Then, a mathematical model is proposed to measure the performance of internal and immediate supply chain. Then, how information sharing across the different supply chain partners can be achieved using state-of-the-art technologies is explained. Mathematical model proposed is expanded to measure the performance of the external and the entire supply chain. A case study was done to prove the proposed framework. Lastly how this research paves way for future research is discussed.