ABSTRACT
Despite the increasing degree of automation in industry, manual or semi-automated are commonly and inevitable for complex assembly tasks. The transformation to smart processes in manufacturing leads to a higher deployment of data-driven approaches to support the worker. Upcoming technologies in this context are oftentimes based on the gesture-recognition, − monitoring or–control. This contribution systematically reviews gesture or motion capturing technologies and the utilization of gesture data in the ergonomic assessment, gesture-based robot control strategies as well as the identification of COVID-19 symptoms. Subsequently, two applications are presented in detail. First, a holistic human-centric optimization method for line-balancing using a novel indicator–ErgoTakt–derived by motion capturing. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and the takt-time balancing. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-score and the cycle time of each assembly workstation with respect to the workers' ability. The second application is gesture-based robot-control. A cloud-based approach utilizing a generally accessible hand-tracking model embedded in a low-code IoT programming environment is shown. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
ABSTRACT
Purpose: This article aims to analyze the impact of COVID-19 measures by governments and central banks on International Financial Reporting Standards (IFRS) 9 loan loss provisions (LLPs). Changes in the total amount of LLPs, distribution of outstanding loan balance among IFRS 9 stages and credit risk parameters used for calculation are investigated for each world region where banks report under IFRS. Design/methodology/approach: Data for a global selection of 105 banks reporting under IFRS were collected from 2019 to 2020 annual reports, financial statements, and Pillar III reports. These data provide the basis to empirically analyze the impact of COVID-19 on LLPs. Findings: In most world regions Stage 2 balances increase while Stage 3 balances remain comparatively stable. The credit risk parameters used for computing LLPs remained stable in 2020. However, in China, the impact of COVID-19 on banks was not detected. Mean Stage 1 balances for Chinese banks increased slightly during the pandemic. Aside from the COVID-19 impact, we find that LLPs, credit risk parameters, and loss absorption capacities are significantly lower for banks in Canada, Oceania and Western Europe compared to those in the rest of the world. Originality/value: There exists previous research examining the COVID-19 impact on financial stability, implementation of emergency rules and country-wide analyses to anticipate default rates depending on recovery scenarios. However, this is the first global study on the immediate impact of COVID-19 on LLPs. It reveals the significant differences between world regions and provides implications about their resilience against future credit shocks. © 2022, Emerald Publishing Limited.