DOIONLINE 
DOIONLINE Publish In |
International Journal of Industrial Electronics and Electrical Engineering (IJIEEE)-IJIEEE |
 Journal Home Volume Issue |
||||||||
Issue |
Volume-9,Issue-1 ( Jan, 2021 ) | |||||||||
Paper Title |
Autonomous Technology Assessment for Resilient Supply Chains | |||||||||
Author Name |
Deniz Uztürk, Gülçin Büyüközkan | |||||||||
Affilition |
Galatasaray University, Department of Management, Department of Industrial Engineering | |||||||||
Pages |
42-47 | |||||||||
Abstract |
Nowadays, we face an unusual situation where every industries’ value chain is disturbed by a pandemic. New rules need to be applied to better adapt to the changing environments from production to service. Technologies called “4.0 technologies” suggest high-tech solutions to create more resilient chains to the sectors. They lead up to more flexible and agile chains by integrating elaborate technologies, such as the Internet of Things (IoT), robotics, and sensors, to the existing traditional systems. The autonomous supply chain applies to moving goods without human intervention -to some degree or helping to achieve inventory accuracy. To achieve successful automation throughout the supply chain, possible technologies are needed to be well investigated within the company's needs. Selecting the most suitable technology among different alternatives is a vital process for companies because incorrect and unnecessary investments in technology can cause damage to businesses. Therefore, companies must determine appropriate technologies for their specific needs. So, this paper mainly focuses on the autonomous technology assessment process. This procedure is approached as a multi-criteria decision-making (MCDM) concept. 2-Tuple integrated TOPSIS tool is suggested for this process. The 2-Tuple linguistic model enables a flexible linguistic decisionmaking environment for decision-makers (DMs). To test the plausibility of the provided framework, a case study for a warehouse is conducted. The results and analysis are presented, as well. Keywords - 2-Tuple linguistic model, Autonomous robots, MCDM, SAW, TOPSIS | |||||||||
| View Paper | ||||||||||
