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The aim of this study is to enhance the product quality management by proposing a framework for the classification of anomalies in digital voice of customer (VoC), i.e. user feedback on product/service usage gathered from online sources such as online reviews. By categorizing significant deviations in the content of digital VoC, the research seeks to provide actionable insights for quality improvement.

The study proposes the application of topic modeling algorithms, in particular the structural topic model, to large datasets of digital VoC, enabling the identification and classification of customer feedback into distinct topics. This approach helps to systematically analyze deviations from expected feedback patterns, providing early detection of potential quality issues or shifts in customer preferences. By focusing on anomalies in digital VoC, the study offers a dynamic framework for improving product quality and enhancing customer satisfaction.

The research categorizes anomalies into spike, level, trend and seasonal types, each with distinct characteristics and implications for quality management. Case studies illustrate how these anomalies can signal critical shifts in customer sentiment and behavior, highlighting the importance of targeted responses to maintain or enhance product quality.

Despite its contributions, the study has some limitations. The reliance on historical data may not hold in rapidly changing markets. Additionally, text mining techniques may miss implicit customer sentiment.

The findings suggest that companies can enhance their quality tracking tools by digital VoC anomaly detection into their standard practices, potentially leading to more responsive and effective quality management systems.

This paper introduces a novel framework for interpreting digital VoC anomalies within the Quality 4.0 context. By integrating text mining techniques with traditional quality tracking, it offers a novel approach for leveraging customer feedback to drive continuous improvement.

The cable inspection robot is essential in maintaining bridge cables. The purpose of this paper is to summarize the maintenance methods of bridge cables. It summarizes the advantages and disadvantages of the critical structures of the external overall frame, intermediate adhesion device, attachment mechanism and driving method of the cable inspection robot. Finally, it discusses the challenges the cable inspection robot faces and the direction of future research.

This paper summarizes the research progress of the cable inspection robot and details the advantages and disadvantages of critical structures such as the external frame, intermediate adhesion device, attachment mechanism, driving method and safe return device of the robot. Finally, it points out the future direction of cable inspection robots, including lightweight design, hybrid design, multi-robot cooperative work, multi-technology integration and intelligent cable inspection digital twin model.

The cables are the main load-bearing components of a bridge, and their safety is crucial. However, subjected to varying loads and environmental influences over a long period, cables are prone to damage, threatening the bridge’s stability. Cable inspection robots can comprehensively detect and repair cable damage, significantly improving efficiency and safety.

This paper provides a comprehensive review of the current research on cable inspection robots, enabling readers to have a comprehensive and systematic understanding of the critical structures and key technologies of cable inspection robots and providing scientific references for researchers working on cable inspection robots.

Supply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.

The study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.

The results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.

This study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

The present study investigates the impact of supply chain disruption orientation (SCDO) on four supply chain disruption (SCD) mitigation strategies: supply chain integration (SCI), supply chain agility (SCA), supply chain visibility (SCV) and supply chain redundancy (SCR). It also examines the impact of the four mitigation strategies on SCD. The impact of the latter on business performance (BP) is also explored.

This study employs an empirical approach through survey research methodology. It analyzes data collected from 304 managers from pharmaceutical distribution companies in Jordan. Appropriate validity and reliability tests were employed for the study constructs. Path analysis using AMOS software was performed to test the study hypotheses.

SCDO was found to positively affect all SCD mitigation strategies. Furthermore, among the four mitigation strategies examined, SCV exhibited the highest significant impact in reducing SCD, followed by SCA and then SCR. However, the results revealed that SCI did not significantly impact SCD. Additionally, SCD proved to be negatively and significantly related to BP.

The present study fills a gap in the literature regarding the management of SCDs in pharmaceutical supply chains (SCs) generally and SCs of pharmaceutical distribution companies specifically. It also addresses an under-investigated area in the literature concerning the role of SCDO in promoting the adoption of SCD mitigation strategies.