Search results

This paper aims to find out what kind of export and failure risk patterns exist among young Estonian manufacturing exporters and explore their interlinkages.

The sample consisted of 208 young Estonian manufacturing exporters. Based on internationalization literature, export patterns were detected with a consecutive three-stage clustering of export sales share from total sales, outside-Europe sales share from export sales and number of target markets, while failure risk patterns were detected by clustering failure probabilities obtained from a universal prediction model. The interconnection of export patterns with financial ratios and failure risk patterns was studied with statistical tests.

Six main internationalization patterns existed. In all, 49 per cent of firms exported to a single European market and their export share was constantly very low, while even most of the firms with high export shares (39 per cent of the sample) were also active on one European market. In terms of failure risk patterns, 49 per cent of firms had constantly very low failure risk, while 51 per cent of firms had medium risk. Higher export engagement did not lead to better financial performance or lower failure risk.

This study is the first to find out if firms following different export patterns are also characterized by specific financial performance and failure risk. In addition, studies encompassing young exporters’ specific target markets and failure risk development are rare. While exporters’ and non-exporters’ financial performance differences have been frequently documented in favor of the former, this study found no such differences for different types of young exporters.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

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.

This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions.

A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach.

The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects.

This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures.

Downtime is a process parameter that substantially impacts on the operating hours and results in production losses, thus motivating maintenance engineers to control process plants. Notwithstanding, the impacting nature of process equipment failure on the operating hours in bottling plants remains inadequately examined. In this paper, the cause-and-effect analysis was used to establish the root cause of the downtime problem and Pareto analysis employed to justify the greatest opportunities for improvement in reducing downtime and increasing reliability levels. Weibull analysis is then conducted on the industrial setting. Novel aspect ratios are proposed.

Using the Weibull failure function of machines as a principal facilitator to produce failure predictions, the downtime behaviour of a process plant was modelled and tested with practical data from a bottling process plant. This research was conducted in a Nigerian process bottling plant where historical data were examined.

The analysis of the results shows the following principal outcome: First, the machines with the highest and least downtime values are 2 and 5, respectively, with correspondingly mean values of 22.83 and 4.39 h monthly. Second, the total downtime 92.05 and 142.14 h for the observed and target downtime, with a coefficient of determination of 0.5848 was recorded. Third, as month 1 was taken as the base period (target), all the machines, except M5 had accepted performance, indicating proper preventive maintenance plan execution for the bottling process plant. Availability shows a direct relationship between the failure and uptime of the machines and the downtime impacts on production. Two machines had random failure pattern and five machines exhibited a wear-out failure pattern and probably due to old age and wear of components in the machines.

The major contribution of the paper is the Weibull modelling in a unique application to a bottling plant to avoid current practices that use reliability software that is not easily accessible.

The Fail-Safe Startup: Your Roadmap for Entrepreneurial Success, the new book by entrepreneurship researcher Tom Eisenmann, sets out to help improve the odds by looking more closely at the most prevalent causes of startup failure and how to avoid them.

Eisenmann research led him to identify six distinct patterns that explain a large proportion of startup failures, three relating to early stage failures and three to late stage.

Strong demand from early adopters may lead a founder to scale up prematurely.

Entrepreneurs must research differences in the needs of likely early adopters and mainstream customers during the upfront customer discovery phase.

Entrepreneurs must research differences in the needs of likely early adopters and mainstream customers during the upfront customer discovery phase. 10; 10;The line between visionary entrepreneur and cult leader can become blurry, and a founder?s ?reality distortion field--useful for motivating others to help pursue the founder?s dream?can become a liability.

To solve the reinforcement congestion, mechanical anchorage is increasingly popular in use instead of conventional hook rebar. However, the bond performance between the rebar and concrete and the range of stress transfer between the two are still not well understood. The purpose of this study is to study the bond performance and failure mechanisms between reinforcement and concrete around an anchorage zone in a structural element.

In this study, simulations were carried out by 3D RBSM (Rigid Body Spring Model). This approach divided a problem of interest into elements, namely concrete and steel elements. And to simulate the failure of anchorage of RC, the steel element size is set according to the geometry complexity of the reinforcing bar. By using this method, two simulation cases of anchorage failure were carried out.

This paper shows that simulations demonstrated good agreement with experimental data in terms of anchorage capacity, crack pattern, and failure mode. This indicates that RBSM analysis can simulate the failure behavior governed by complex cracks.

This paper indicates the analytical approach to investigate the anchorage performance of RC.

This paper is concerned with the availability of books known to be held by the Library. It outlines a simple survey method whereby readers record the non‐availability of books which they are looking for. The survey has already been installed at four university libraries, but the results from only one library are considered here. These results show how a librarian can find out the degree of non‐availability or failure in any particular area of the library, the causes of failure, and even the particular items which are in heavy demand and not available. They also provide information on the degree of co‐operation by the readers, the number of titles failing once, twice, three times etc., the overlap of demand for popular books by different groups of borrowers, the waiting time for books that failed, the pattern of demand for particular books over a period of time, the relationship between failure and recall, the correlation of failure from one term to the next, and the relative use of books inside and outside the library. From the results it was possible to recommend certain changes in library procedure which should have reduced failure, and to compare the titles of books failing in the Main Library with the holdings of a departmental library. The time spent on the survey and the total cost of the survey are also given.

Failures may lead to ultimate success in both nature and business. Just as dynamic ecosystems depend on death to replace senescent organisms with vigorous growth, the termination of uneconomic activities is essential to wealth creation. This paper explores the benefits of failures, and uses aspects of the analogy between death and business failure to analyze how failures in business economize upon resources and lead to better firms and greater efficiencies. A distinguishing feature of our work is the analytic use of competitive markets to provide insights into the processes of success and failure. Recognizable patterns of business failures are discussed in an effort to provide entrepreneurs and managers with a basis for understanding and acting upon changing circumstances.

The data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining provides a chance to handle this huge amount of text data and extract meaningful information to improve various processes of an industrial environment. This paper represents the application of classification models on maintenance text records to classify failure for improving maintenance programs in the industry.

This paper is presented as an implementation study, where text mining approaches are used for binary classification of text data. Naive Bayes and Support Vector Machine (SVM), two classification algorithms are applied for training and testing of the models as per the labeled data. The reason behind this is, these algorithms perform better on text data for classifying failure and they are easy to handle. A methodology is proposed for the development of maintenance programs, including classification of potential failure in advance by analyzing the regular maintenance data as well as comparing the performance of both models on the data.

The accuracy of both models falls within the acceptable limit, and performance evaluation of the models concludes the validation of the results. Other performance measures exhibit excellent values for both of the models.

The proposed approach provides the maintenance team an opportunity to know about the upcoming breakdown in advance so that necessary measures can be taken to prevent failure in an industrial environment. As predictive maintenance incurs a high expense, it could be a better replacement for small and medium industrial plants.

Nowadays, maintenance is preventive-based rather than a corrective approach. The proposed technique is facilitating the concept of a proactive approach by minimizing the cost of additional maintenance steps. As predictive maintenance is efficient but incurs high expenses, this proposed method can minimize unnecessary maintenance operations and keep control over the budget. This is a significant way of developing maintenance programs and will make maintenance personnel ready for the machine breakdown.