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Hydraulic slide valve failure often results from competing failure modes, termed competitive failure. To enhance prediction accuracy for hydraulic slide valve remaining useful life, the authors propose a method incorporating competitive failure and Monte Carlo simulation. This method allows for more accurate prediction of hydraulic slide valve remaining useful life.

In this paper, the competitive failure mode of the hydraulic slide valve is analyzed by studying the two failure modes of the hydraulic slide valve, and the prediction of the remaining useful life of the hydraulic slide valve is studied by using the sample set generated by Monte Carlo simulation and the competitive failure joint model.

The results show that the proposed prediction method based on competitive failure and Monte Carlo simulation is more accurate than the traditional Bayesian joint model prediction method when dealing with the failure mode competition phenomenon of hydraulic slide valve.

In this paper, the remaining useful life prediction of hydraulic slide valve with competitive failure characteristics is studied, which provides a new idea for the remaining useful life prediction method.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2023-0361/

While many studies have focused on the effectiveness of service recovery after encountering service failure, few have considered for pre-recovery opportunities. Drawing on service guarantee research and the inoculation theory, this study examines the effect of pre-recovery strategy on customer’s satisfaction when faced with possible service failure. This study takes into consideration the uncertainty of service failure, including specific or unpredicted failures.

Three experimental studies were conducted to examine our research hypotheses, which include a total of 1203 subjects recruited from online and offline platforms.

The findings reveal that service guarantees and inoculation messages both effectively mitigate the negative consequences of a service failure. Specifically, the pre-recovery strategy is particularly effective for specific service failures. In addition, we thoroughly examined the mediating role of customer attribution.

This research significantly contributes to the existing literature on service research by delving into the potential positive impact of proactive measures undertaken by service enterprises. Furthermore, the findings of this research offer valuable insights for practitioners.

This study aims to examine the relevance of strategic marketing planning in this agile era and its effect on firms’ international performance and explores conditions under which the influence of planning changes.

Based on contingency theory, a conceptual model is tested based on survey data from internationalizing firms. Data were analyzed using partial least squares -structural equation modeling.

Marketing strategy planning is (still) associated with enhanced performance, and depends on external and internal contingencies. While the planning−performance relationship is amplified by market sensing (external contingency), surprisingly, it is decreased in presence of high tolerance for failure (internal contingency).

Findings seek to transform marketing planning in international business practice by requiring that its implementation receives the attention of senior management.

Marketing strategy planning should not be deemphasized. While planning appears to be undergoing an identity crisis, practitioners’ attention to marketing planning is warranted.

Motivated by recent research indicating the significant challenges posed by imbalanced datasets in industrial settings, this paper presents a novel framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis, aiming to improve fault detection accuracy and reliability.

This study addressing the challenge of imbalanced datasets in predicting hard drive failures is both innovative and comprehensive. By integrating data enhancement techniques with cost-sensitive methods, the research pioneers a solution that directly targets the intrinsic issues posed by imbalanced data, a common obstacle in predictive maintenance and reliability analysis.

In real industrial environments, there is a critical demand for addressing the issue of imbalanced datasets. When faced with limited data for rare events or a heavily skewed distribution of categories, it becomes essential for models to effectively mine insights from the original imbalanced dataset. This involves employing techniques like data augmentation to generate new insights and rules, enhancing the model’s ability to accurately identify and predict failures.

Previous research has highlighted the complexity of diagnosing faults within imbalanced industrial datasets, often leading to suboptimal predictive accuracy. This paper bridges this gap by introducing a robust framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis. It combines data enhancement and cost-sensitive methods to effectively manage the challenges posed by imbalanced datasets, further innovating with a bagging method to refine model optimization. The validation of the proposed approach demonstrates superior accuracy compared to existing methods, showcasing its potential to significantly improve fault diagnosis in industrial applications.

This work examines a repairable machining system’s reliability by considering multiple failure scenarios, including individual component failures, hardware and software malfunctions, failures resulting from shared causes and failures caused by human error. When a system is susceptible to several modes of failure, the primary goal is to forecast availability and other reliability metrics as well as to calculate the expected profit of the repairable machining system.

The process of recovering after a system failure involves inspecting the system and fixing any malfunctions that may have occurred. The repair procedures for all kinds of faults are taken to follow a general distribution to represent real-time circumstances. We develop a non-Markovian stochastic model representing different system states that reveal working, failed, degraded, repair and delayed repair states. Laplace transformation and the supplementary variable technique are used to assess the transient states of the system.

Analytical expressions for system performance indices such as availability, reliability and cost-benefit analysis are derived. The transient probabilities when the system experiences in different states such as failed, degraded and delayed states are computed. The results obtained are validated using Mathematica software by performing a numerical illustration on setting default values of unknown parameters. This ensures the accuracy and reliability indices of the analytical predictions.

By methodically examining the system in its several states, we will be able to spot possible problems and offer efficient fixes for recovery. The system administrators would check to see if a minor or major repair is needed, or if a replacement is occasionally taken into consideration to prevent recurring repairs.

Organizational exploration has recently emerged in the literature as an essential aspect of contemporary organizations. However, little is known about its antecedents and, specifically, the role of contemporary leadership styles. The present study investigates the relationship between empowering leadership and organizational exploration. In addressing this relationship, we examine preoccupation with failure and leader gender as a mediator and a moderator, respectively.

We collected data from a sample of 326 US employees in March 2022. To test the present hypotheses, we used the PROCESS SPSS macro.

Our results supported our moderated mediation model, demonstrating that the indirect relationship of empowering leadership with organizational exploration via preoccupation with failure is stronger for male leaders than for female counterparts.

Concerning the practical implications of this study, organizations should be cognizant of empowering leaders’ impact on desirable outcomes. Additionally, organizations should promote preoccupation with failure to ameliorate organizational exploration. Preoccupation with failure can be achieved when error reporting is encouraged by organizations and supervisors and when a culture that promotes constructive feedback is established.

The present study offers novel insights into the underlying mechanisms and boundary conditions through which empowering leadership relates to organizational exploration. Additionally, it fills a gap in the literature concerning the relationship between empowering leadership and preoccupation with failure. Moreover, it adds to prior research regarding the outcomes of preoccupation with failure, filling the gap regarding the relationship between preoccupation with failure and organizational exploration. Lastly, it expands limited research focused on leader gender as a condition under which the effect of contemporary leadership styles could be enhanced or mitigated.

The research aims to explore the relationships between the digital capabilities of micro, small and medium enterprises (MSMEs), their participation in global value chains and their adoption of innovative business models. Additionally, the study investigates how the prior experiences of entrepreneurial failure may influence or moderate these relationships.

To address these research objectives, the study draws upon data obtained from the Flash Eurobarometer 486 survey, a comprehensive dataset that explores the challenges faced by 13,197 European MSMEs as they navigate the complexities of growth, the integration of sustainable business models and the incorporation of digital technologies. To test the proposed hypotheses, the research employs multivariate logistic regression analysis.

Digital capabilities are found to be positively associated with business model innovation, while integration into global value chains is linked to a higher likelihood of implementing new business models. Interestingly, entrepreneurial failure did not significantly influence the relationship between digital capabilities and the adoption of new business models. In contrast, entrepreneurial failure significantly moderated the impact of global value chain inclusion on business model innovation, particularly in MSMEs with a history of failure.

This article provides practical guidance to entrepreneurs and companies interested in enhancing their digital strategies and engagement in global value chains, considering the entrepreneurs' business histories.

This paper continues the reporting of the Unit's work on a method of investigating how often readers fail to find what they are looking for on the shelves, why they fail, and what particular books they fail to find. The method used is a slight modification of the earlier one—readers were asked to record on a slip provided the details of the book or periodical they were looking for, or subject area they were looking in, their academic status, and the date, and then to place the slip in an adjoining box. From analysis of these ‘failure’ slips it was possible to determine the cause of the reader's failure, the individual book the reader was looking for, and the pattern of failure for different groups of volumes. A new development was surveys of samples of readers, carried out during the fortnight of peak demand. Answers to these surveys provided information on the effect of failure on a reader's work, what action he took after failing, whether he found adequate substitutes, how many books he found by browsing, and what proportion of the books he consulted he borrowed. Some of these answers could be checked by a direct count of the books used in, and borrowed from, the library. The investigations were carried out in three University libraries, each with its own characteristic library structure and teaching patterns. These differences were reflected in the results obtained. It is hoped that the methods of investigation used can be employed by librarians to investigate the effectiveness of some of the services in their own libraries.

Describes research into the problem of estimating the distribution parameters of failure data of industrial equipment subject to two failure modes. It is assumed that time to failure data are available, but it is unknown for each failure which of the two main failure modes caused the event. A loglikelihood method was developed and tested on generated mixed failure mode data. Shortcomings of this method triggered the development of two additional methods, MINESS and MINESS+, which minimize the error sum of squares of the reliability function, after separating the failure data into two sets.

In the design of anisotropic materials, such as advanced fibre‐reinforced composites, the failure envelope has a crucial role. A geometrical representation of the envelope is of particular value to investigate the highly anisotropic nature of the strength of the material. This provides the design engineer with a visualization of the failure envelope by graphically representing it using a CAD package. Use is made of both stress‐based and strain‐based polynomial failure criteria and examples are given of changes in the failure envelope due to changes in fibre orientation for a high strength graphite/epoxy composite lamina.