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The purpose of this paper is to demonstrate a tactical approach to cope with the issues related to low availability of repairable machines or systems because of their poor reliability and maintainability. It not only explores the significance of availability, but also embarks upon a step-by-step procedure to earmark a relevant replenishment plan to check the mean time between failure (MTBF) and the mean time to repair (MTTR) efficiently.

The literature review identifies the extent to which availability depends on reliability and maintainability, and highlights the diversified challenges appearing among repairable systems. Different improvement initiatives have been suggested to avoid downtime, after analyzing the failure and repair time data graphically. Relevant plots and growth curves captured the historical deviations and trends along with the time, which further helps to create more robust action plans to enrich the respective reliability and maintainability of machines. During the case study, the proposed methodology has been tested on four SPMs and successfully validated the claims after achieving around a 98 percent availability at the end.

Graphical analysis is the key to developing suitable action plans to enhance the corresponding reliability and maintainability of a machine or system. By increasing the MTBF, the reliability level can be improved and similarly quick maintenance activities can help to restore the prospect of maintainability. Both of these actions ultimately reduce the downtime or increase the associated availability exponentially.

The work revolves around the availability of SPMs. Moreover, SPMs have been divided only into series sub-systems. The testability and supportability aspects have not been considered thoroughly during the fabrication of the approach.

The work focusses on the availability of systems and proposed frameworks that helps to reduce downtime or its associated expenditure, which is generally being ignored. As a case study-based work especially on SPMs in the auto sector this paper is quite rare and will motivate affiliated engineers and practitioners to achieve future breakthroughs.

In this article we present an application of Gumbel's bivariate exponential distribution model in the context of estimating warranty costs of motor cycles under a new warranty policy. The problem in question is as follows: Under the present two‐dimensional warranty policy, repair costs (termed as warranty costs) of a motorcycle during the age of first six months or within the usage of 8,000 kilometers are borne by the company. To enhance customer satisfaction, the company wanted to bear the repair costs up to an age of one year or a usage of 12,000 kilometers. The problem is to estimate the expected hike in warranty costs if the warranty policy were revised as mentioned above. Using the past data, the problem is solved by studying the underlying renewal process. Gumbel's bivariate exponential distribution function is found to be useful in approximating the renewal function. Some practical difficulties posed by the past data in the analysis are highlighted and tackled in an interesting way.

This study offers empirical evidence from a nationally representative panel dataset of nascent entrepreneurs (PSED-II) regarding when external financing is acquired and how certain factors affect this timing during the cumulative process of nascent entrepreneurs taking actions toward establishing an operational entity. By assessing the relationship between the external financing event and the cumulative set of actions that nascent entrepreneurs undertake to create new businesses, we improve our understanding of how the timing of acquiring external financing affects organizational survival and growth.

We apply nonparametric and semiparametric survival analysis techniques to a nationally representative panel dataset of nascent entrepreneurs. This ascertains the probability of an external financing event at any given moment in time and a set of startup conditions that we hypothesize will affect this timing. First, we use Kaplan–Meier analysis to explore when external financing occurs during new business creation. We then use discrete-time survival analysis to investigate whether certain startup conditions affect when external financing occurs. Finally, we conduct a test of independence to examine the external financing event relative to other startup activities completed during new business creation.

Nascent entrepreneurs tend to acquire external funding relatively late in the new venture startup process – on average, about two-thirds of the way from conceiving of the idea and becoming operational. They tend to take actions that are less resource-demanding early in the startup process to build their organizations to a fundable stage. Net worth tends to speed up the acquisition of external funding as wealthy entrepreneurs tend to ask for funding earlier in the process. Finally, entrepreneurs in capital-intensive industries do not seem to get outside funding before entrepreneurs in other industries.

This study is unique in three ways. First, we investigate the timing of the highly important external financing event. Timing is critical in unpacking and making sense of the very early stages of a new business and in guiding entrepreneurs and students about when to do what. Second, we do so in a subsample of preoperational, nascent, funded entrepreneurs derived from a nationally representative panel dataset of startup attempts. Third, our findings provide a counter-intuitive yet systematic understanding of organizational emergence and very early-stage financing.

The purpose of this paper is to investigate the stochastic comparisons of the parallel system with independent heterogeneous Gumbel components and series and parallel systems with independent heterogeneous truncated Gumbel components in terms of various stochastic orderings.

The obtained results in this paper are obtained by using the vector majorization methods and results. First, the components of series and parallel systems are heterogeneous and having Gumbel or truncated Gumbel distributions. Second, multiple-outlier truncated Gumbel models are discussed for these systems. Then, the relationship between the systems having Gumbel components and Weibull components are considered. Finally, Monte Carlo simulations are performed to illustrate some obtained results.

The reversed hazard rate and likelihood ratio orderings are obtained for the parallel system of Gumbel components. Using these results, similar new results are derived for the series system of Weibull components. Stochastic comparisons for the series and parallel systems having truncated Gumbel components are established in terms of hazard rate, likelihood ratio and reversed hazard rate orderings. Some new results are also derived for the series and parallel systems of upper-truncated Weibull components.

To the best of our knowledge thus far, stochastic comparisons of series and parallel systems with Gumbel or truncated Gumble components have not been considered in the literature. Moreover, new results for Weibull and upper-truncated Weibull components are presented based on Gumbel case results.

The purpose of the article is to improve the understanding of the role of institutional factors in real estate development. The authors take into account zoning (existence and type), type of right of disposal and type of buyer and seller of property in a multivariate econometric estimation. Dependent variable of the analysis is the time between acquisition of empty land and the application for a building permit, a period when many important development decisions have to be made. This indicator is closely related to debated phenomena like land hording and speculation.

The authors estimate a Cox proportional hazard model with the time between acquisition and application for a building permit as dependent variable and institutional indicators and a number of control variables as explanatory variables. Study area is the GZM Metropolis in the South of Poland. This region shows enough variability in institutional arrangements to allow for this type of analysis.

The analysis shows that institutional factors significantly influence the real estate development process. In areas that have not issued a zoning plan, the period until the building permit application is significantly longer. When the state is involved in a transaction (as purchaser or seller), it also takes longer until the building permit application is submitted. Although the instrument is usually intended to speed up development, perpetual usufruct implies a longer period until building permit application. Because of the results the authors get for control variables and for robustness checks, the authors are confident of the results of the analysis.

To the authors’ knowledge, this is the first study that deals with the question how institutional factors influence the timing of real estate development. By using data for a region in Poland, the authors also add to knowledge about real estate development in CEE countries.

The purpose of this paper is to examine the existence of rational speculative bubbles in the Middle East and North African (MENA) stock markets.

To complement shortcomings of the traditional bubble tests, such as unit root tests and cointegration tests, mainly relying on expectations of future steams of dividends, the authors employ fractional integration tests and duration dependence tests.

Despite recent extreme fluctuations of MENA stock markets, fractional integration tests built on autoregressive fractionally integrated moving average models do not support the possibility of bubbles in the MENA stock markets. Similarly, duration dependence tests based on nonparametric Nelson‐Aalen hazard functions not only reject the existence of bubbles but also support equality of hazard functions between domestic and the US‐based investors without regard to the rapid financial liberalization and integration in the MENA stock markets.

The reliable results of bubble tests of the MENA stock markets provide domestic and international investors as well as policy makers with invaluable benchmark to better understand the irregular and highly fluctuating stock market behaviors of the MENA stock markets compared to other developed and emerging stock markets. For domestic and international investors, the formal analysis of MENA stock markets behavior including rational speculative bubbles will help them in their portfolio decisions and hedging purposes. Similarly, the empirical results of bubble tests in the paper will be also helpful to policymakers in MENA countries to take actions to improve the functioning of these dynamic markets.

The purpose of this study is to examine how early-entry and process-based capability development affected firm survival during shakeout in the US brewing industry between 1938 and 1980.

Hazard analysis was conducted on US brewing industry data spanning 42 years from 1938 to 1980.

Both early-entry and later capability developments enhance the probability of survival. In addition, firms which entered early were also more likely to be the firms who continually developed capabilities across the decades.

This study contributes to our understanding of shakeout in traditional, non-high-technology businesses. However, because it is a single industry study, the ability to the generalize findings to other industry contexts is limited.

Early entry can determine survival in industries with stable products and low levels of technological change.

Policy-makers interested in competitive dynamics should take note of the historical conditions that lead to industry consolidation in traditional industries, which, while not as glamorous as the technology sector, provide the core of US industry.

Historical firm characteristics can impact industry structure and firm survival for over a century.

Automated flow line manufacturing systems are becoming more and more relevant in industry, especially in the food and beverage sector. Improving the efficiency of automated flow line manufacturing systems is the core objectives of all companies as measured by the overall equipment effectiveness (OEE) index. The purpose of this paper is to carry out an innovative micro downtime data collection and statistical analysis in the food and beverage sector; it introduces a numerical indicator called “Cost Performance Indicator-CPI” to estimate the performance improvement of investment activities. Moreover this analysis will be used as a basis to carry out a new simulative model to study micro downtime of automatic production lines. In addition, the presented micro downtime data collection and statistical analysis will be used to construct a new simulative model to support improvement activities.

Descriptive and statistical analyses are carried out about OEE, time to repair (TTR) and time to failure (TTF) data. The least efficient production line is identified and principal causes of inefficiency are investigated. Micro downtime (downtime lower than 15 minutes) covers 57 percent of inefficiency. Investigations are carried out into the three principal machines affected by this inefficiency. The study then investigates the causes of micro downtime of these machines using ad hoc data collection and analysis. The probability distributions of TTF and TTR are evaluated and an analysis of micro downtime causes and a cause-effect is carried out. The most attractive investment in terms of recoverable OEE (1.44 percent) and costs is analyzed through the calculation of a CPI. One of the conclusions is to recommend the introduction of a payback period with a variable contribution margin.

This study get the basis for the construction of a new simulative model based on ad hoc micro downtime probability distributions, applied in automated flow line manufacturing systems. It gives an effort to downtime analysis in automated production lines and a guideline for future analysis. Results of this study can be generalized and extended to other similar cases, in order to study similar micro downtime inefficiency of other production lines. The statistical analysis developed could also potentially be used to further investigate the relationship between the reliability of specific machines and that of the entire line.

The case study presents a new detailed micro downtime data collection and statistical analysis in the beverage sector with the application of a numerical indicator, the CPI, in order to drive future actions. In addition, the presented micro downtime data collection and statistical analysis will be used to construct a new simulative model to support improvement activities. Moreover, results can be generalized and used as a basis for other micro downtime analyses involving the main causes of inefficiency in automated production lines.

The role of a reliability analysis for improving the performance of libraries and information services has become extremely crucial. The research question considered here is simple and can be summarized as follows: “How reliable is your library?”. Indeed, reliability considerations go beyond the issue of quality since they incorporate the dimension of time, i.e. they involve a number of different activities and operations that take place throughout the life‐cycle of a library system or service. This paper seeks to address these issues.

Inference modelling techniques based on both parametric and nonparametric methods are reviewed and presented together with illustrative examples. From a wide number of non‐parametric methods that have been made available for reliability estimation, the ones examined here are the Kaplan‐Meier and the Cumulative‐hazard methods. Furthermore, parametric methods and applications, which are based on the Weibull distributional model, are examined.

The nonparametric reliability modelling methods presented are simple to use and suitable for estimating the reliability of information systems and services directly from the available life data. The Weibull analysis is quite useful for a wide range of library management methods, financial applications, for modelling user behaviour within many library settings and systems i.e. digital information systems.

This is among the first implementations of reliability analysis in libraries and information services. This paper provides essential insight to library management researchers and practitioners on how they might incorporate into the library management agenda reliability considerations. The reliability modelling techniques presented can be employed with all types of information services, and not just libraries.

The purpose of this paper is to investigate hazards for minor users while they are exposed to social networks. In particular, it provides the statistical relationship of these hazards with the exposure time as well as the amount of published personal information.

An experiment was conducted that has revealed a huge number of personal information exposed by users of social network applications. Moreover, a significant amount of suspicious activity against minors has been recorded. Experimental data led to the hypothesis that online hazards can be modeled with known statistical distributions. In order to examine this hypothesis, survival analysis techniques, which involve the estimation of certain functions that reflect the relation of a disastrous event with time, were applied.

The results show that the incoming hazards for minor female profiles follow the Logistic distribution, while the corresponding hazards for minor male profiles follow the Normal distribution.

The findings of this work are crucial for developing an effective system for automated grooming recognition in real time by optimizing the detection threshold as a function of time. Thus, the threshold sensitivity can be appropriately adjusted such that lower frequencies of occurrence lead to lower threshold sensitivities, and higher frequencies of occurrence lead to higher threshold sensitivities.