ABSTRACT
The COVID-19 pandemic has had a massive impact on the global aviation industry. As a result, the airline industry has been forced to embrace new technologies and procedures in order to provide a more secure and bio-safe travel. Currently, the role of smart technology in airport systems has expanded significantly as a result of the contemporary Industry 4.0 context. The article presents a novel construction of an intelligent mobile robot system to guide passengers to take the plane at the departure terminals at busy airports. The robot provides instructions to the customer through the interaction between the robot and the customer utilizing voice communications. The usage of the Google Cloud Speech-to-Text API combined with technical machine learning to analyze and understand the customer's requirements are deployed. In addition, we use a face detection technique based on Multi-task Cascaded Convolutional Networks (MTCNN) to predict the distance between the robot and passengers to perform the function. The robot can guide passengers to desired areas in the terminal. The results and evaluation of the implementation process are also mentioned in the article and show promise.
ABSTRACT
Purpose: This paper aims to identify and assess global risks in the supply chain performance. Design/methodology/approach: First, global risks are identified and classified according to three criteria: content, probability and context. A set of supply chain performance indicators are then defined by the theory of resource-based view and balanced scorecard. Structural equation modeling is adopted to access risks in the global supply chain. Findings: This article contributes to the supply chain risk management literature by providing a detailed operationalization of global supply chain risk constructs, e.g. natural disasters, war and terrorism, fire accidents, economic and political instability, social and cultural grievances, decease. Empirical results reveal that the supply chain is predominantly regarded as being vulnerable as the proposed model of risks can explain up to 12.6% variance of supplier performance, 25.2% innovation and learning, 23% internal business, 40.6% customer service and 32.4% finance. Research limitations/implications: These risks are relevant contextual variables in strategic supply chain decisions. Supply chain managers should keep in mind acceptable cost/benefit tradeoffs in their firms' mitigation efforts associated with major contingency risks. This research advocates the allocation of scarce resources to adopt the supply chain strategies of avoidance, speculative and postponement. Originality/value: The application of the strategic content/process/context to explain global supply chain performance is an interesting approach. Moreover, globalization trends and the COVID-19 perspectives are considered to be the main reasons for increasing such complex factors. Data on validating research models collected during the COVID-19 pandemic reflect the topicality of this study. © 2022, Emerald Publishing Limited.