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Literature shows that the economics of early failures in maintenance and electric utilities have not been deeply analyzed. This study aims to focus on quantifying the economic impact that early failures in current transformers have on total maintenance costs. The empirical study is conducted in a regional transmission division of an electric utility located in Mexico.

The utility's database was accessed to collect 219 maintenance records. Clustering techniques were used to identify early failures from a bimodal distribution of failures. Confirmatory goodness-of-fit procedures followed the analysis, and finally, direct and opportunity costs were estimated by adapting the cost-of-quality (PAF) Model.

Around 11% of all maintenance activities are triggered by early failures, and they account for up to US$2.2m during the eight-year period under study, which represents 16% of total maintenance costs. Additionally, opportunity costs represent close to two-thirds of the total costs due to early failures. This was obtained after finding and validating a clear-cut border of 3.5 months between early failures and the rest.

Failures in energy grids and power transmission can have a large economic impact on the power industry and the society in general. Thus, the maintenance function in equipment such as current transformers is a crucial entry of the budget of any electric utility. This study is one of the very few that highlights the magnitude and importance of direct and opportunity costs derived from early failures.

This paper aims to explore the concept of early warning signs (EWSs) in offshore-outsourced software development (OOSD) projects at the team level. It also aims to identify the EWSs of failure in the onshore-offshore project context and understand how they are perceived by responsible managers.

A grounded theory approach is followed by gathering data from 19 failed OOSD projects using project managers from client and vendor sides as the key informants.

This study identified 13 EWSs of failure in five categories of trust and team cohesion, common project execution structures, awareness of shared work context, collaboration between teams and onshore-offshore team coordination capabilities. EWSs were found to comprise two components: early warning issues and early signals of failures.

India-based vendors’ data in the study formed the primary weakness of the work regarding generalizability, even though it brought homogeneity to data. Lack of triangulation of failure data through client or vendor peers proved impossible in this research as failure remains a very sensitive topic. Dual composition of EWSs could be applied to institutionalize an early warning tool in projects.

The paper develops an exploratory model of EWSs of failure and project failure in the OOSD project context. The two-component framework of EWSs allows project managers to eliminate false positives while identifying EWSs. It contributes to the information system failure, risk management and information technology offshoring research streams.

Available information for evaluating the possibility of hospitality firm failure in emerging countries is often deficient. Oversampling can compensate for this but can also yield mixed samples, which limit prediction models’ effectiveness. This research aims to provide a feasible approach to handle possible mixed information caused by oversampling.

This paper uses mixed sample modelling (MSM) when evaluating the possibility of firm failure on enlarged hospitality firms. The mixed sample is filtered out with a mixed sample index through control of the noisy parameter and outliner parameter and meta-models are used to build MSM models for hospitality firm failure prediction, with performances compared to traditional models.

The proposed models are helpful in predicting hospitality firm failure in the mixed information situation caused by oversampling, whereas MSM significantly improves the performance of traditional models. Meanwhile, only partial mixed hospitality samples matter in predicting firm failure in both rich- and poor-information situations.

This research is helpful for managers, investors, employees and customers to reduce their hospitality-related risk in the emerging Chinese market. The two-dimensional sample collection strategies, three-step prediction process and five MSM modelling principles are helpful for practice of hospitality firm failure prediction.

This research provides a means of processing mixed hospitality firm samples through the early definition and proposal of MSM, which addresses the ranking information within samples in deficient information environments and improves forecasting accuracy of traditional models. Moreover, it provides empirical evidence for the validation of sample selection and sample pairing strategy in evaluating the possibility of hospitality firm failure.

In a modern gas turbine‐powered aircraft, engine material cost is the major single contributor to direct maintenance and overhaul cost. Due to the large expenditures involved, even a small percentage improvement in this field will result in the achievement of large savings. By concentrating on the reduction of both scheduled and unscheduled removals, this cost can be minimized. An analysis of the experience gained with the application (o gas turbine engines, of traditional overhaul life development programmes shows that these are of no benefit in determining the limitations of the engine. Experience and control of the total time or take‐off cycles of components, rather than time between overhaul, is the most important factor in the operational development of gas turbine engines. Overhaul life development can be divided into two phases: the initial programme applied to new engines with little operational background, and the developed programme applied to engines with substantial operating experience. In the initial programme, the sampling of two engines every 100 hours from a declared plateau can provide the required information and produces the required stagger in total time of components. In the developed programme large steps in overhaul life can be declared without further sampling. Life development of critical engine components cannot be done in service and requires manufacturer's cyclic testing ahead of airline experience until fail‐safe designs are achieved. Early failure detection of gas turbine engines is a Held of great potential return which to date has been only barely explored. Both engine manufacturers and operators need to develop tools and techniques for effective early failure detection quickly, to gain the large benefits which this field offers. Examples of the application of some early failure detection designs and techniques in Trans‐Canada Air Lines (T.C.A.) are given. These are in the field of visual examination with increased access to the engine, X‐ray examination of flame tubes with the engine installed in the aircraft, more effective use of the oil system to give warning of failure, engine vibration monitoring and automatic flight performance recording.

This paper presents a model for predicting crises in small businesses using early‐warning signals. It summarises the results of two separate studies carried out in Finland (with 72 per cent response) and the UK (26 per cent) on the decision process of corporate analysts (Finland) and bank managers (UK) in predicting the failure of small and medium‐sized enterprises (SMEs). Both studies consist of seven main headings and over 40 sub‐headings of possible factors leading to failure. Weighted averages were used for both studies to show the importance of these factors. There are significant similarities in the results of the two studies. Management incompetence was regarded as the most important factor, followed by deficiencies in the accounting system and attitude towards customers. However, low accounting staff morale was considered a very important factor in Finland but not in the UK. Unlike Finland, the UK results emphasised the importance of accounting systems and internal control. These two studies differ from previous studies as managerial auditing elements like the importance of internal control departments (UK evidence) and budgetary control systems were included. Similarities in the results of these surveys conducted under two separate EU environments imply that it would be interesting and beneficial to extend these studies to other member states.

The purpose of this research is the exploration of online complainants' revenge based on their consumer-brand relationship strength and received webcare. The authors introduce inter-failures (i.e. the perceived number of earlier independent service failures that a customer has experienced with the same brand involved in the current service failure) as the central frame condition.

To test our hypotheses, both a scenario-based online experiment (n = 316) and an online survey (n = 492) were conducted.

With an increasing number of inter-failures, online complainants with a high-relationship strength move from the “love is blind” effect (no inter-failures) to the “love becomes hate” effect (multiple inter-failures), when they ultimately become more revengeful than their low-relationship strength counterparts. In addition, the authors show that in the case of no or few inter-failures, accommodative webcare has a lasting positive effect over no/defensive webcare for both low- and high-relationship complainants. More importantly, however, when consumers have experienced multiple inter-failures, accommodative webcare becomes ineffective (for low-relationship complainants) or boomerangs by cultivating revenge towards the brand (among high-relationship complainants), but not strategic avoidance.

The findings have pronounced implications for the literature on customer–brand relationships following service failures and the literature, which predominantly emphasizes the unconditionally positive effects of accommodative webcare.

This study is the first that simultaneously considers the prior customer–brand relationship, inter-failures and webcare to explain online complainants' revenge.

This paper aims to examine the factors that lead to the failure of startups in India and proposes a ‘Four Dimensional (4D) Strategic Framework’ to drive success.

This study is exploratory and uses a narrative analysis methodology to analyse the accounts of key startup stakeholders – founders, investors, former employees and consumers; to identify their failure factors. A conveniently selected sample of 165 startups was studied to understand better the reasons for their failure within a thematic framework developed from David Feinleib’s (2012) handbook “Why Startups Fail”.

Results indicate that a dearth of capital or running out of money and inadequate sales and marketing strategy, which leads businesses to fall behind rivals and lose money on each transaction, are the most common factors for startup failure in India.

“Startups” are substantial for emerging economies like India because they fuel technological innovation and economic progress and provide for the modern workforce’s needs and aspirations. However, they seem to be typically unprofitable, with a modest probability of survival. Subsisting studies mainly focus primarily on success factors and very few on why startups fail, with significant disagreement on an appropriate methodology. To the best of the authors’ knowledge, this is the first study that analyses failure factors of Indian startups using narrative analysis of its key stakeholders. It aims to aid the conception of profitable entrepreneurship by reducing the failure frequency in the startup and small business ecology.

The purpose of this study is that the effects of surface mount technology (SMT) assembly process on the product lifetime of fine-pitch printed circuit boards (PCBs) were investigated under biased highly accelerated stress testing (HAST).

SMT assembly from a semiconductor SMT assembly process was replicated to test PCBs under the same conditions as SMT-assembled PCBs. The median lives µ and standard deviation s of the test PCBs were calculated from the log-normal distribution. The failure analysis of current leakages was conducted by the focused ion beam, scanning electron microscopy and energy-dispersive X-ray spectroscopy. Using the inverse power law and modified Peck-H’s relationship, the PCB lives at accelerated (by SMT assembly stress) and user conditions were calculated.

The failure analysis demonstrated that SiO₂ and BaSO₄ fillers added for stiffening organic materials promote current leakage failure. Therefore, the hydrophobicity of these fillers is believed to be necessary to suppress the current leakage failure under biased HAST. The inverse power law model indicates that the acceleration life model with SMT assembly stress can be given as follows: L(V) = 271.9(S)^−0.5031. From modified Peck-H’s relationship, after the third SMT assembly, the time required to attain 0.96 per cent failures at 35°C/60 per cent RH/1.9 V and 130°C/85 per cent RH/3.5 V are 129 y and 69.5 h, respectively. The biased HAST at 130°C/85 per cent/3.5 V after the third SMT assembly for 69.5 h on 238 samples could be recommended as an early quality-monitoring procedure.

In the future, the failure modes in an early stage of a bathtub should be analyzed and the life prediction model should be studied accordingly.

Through this study, the lifetime prediction model and early quality-monitoring procedure for organic substrates because of SMT assembly stress were obtained.

The purpose of this paper is to analyze the dependent competing failure reliability of harmonic drive (HD) with strength failure and degradation failure.

Based on life tests and stiffness degradation experiments, Wiener process is used to establish the accelerated performance degradation model of HD. Model parameter distribution is estimated by Bayesian inference and Markov Chain Monte Carlo (MCMC) and stiffness degradation failure samples are obtained by a three-step sampling method. Combined with strength failure samples of HD, copula function is used to describe the dependence between strength failure and stiffness degradation failure.

Strength failure occurred earlier than degradation failure under high level accelerated condition; degradation failure occurred earlier than strength failure under medium- or low-level accelerated condition. Gumbel copula is the optimum copula function for dependence modeling of strength failure and stiffness degradation failure. Dependent competing failure reliability of HD is larger than independent competing failure reliability.

The reliability evaluation method of dependent competing failure of HD with strength failure and degradation failure is first proposed. Performance degradation experiments during accelerated life test (ALT), step-down ALT and life test under rated condition are conducted for Wiener process based step-down accelerated performance degradation modeling.

The Duane reliability growth model has a number of inherent limitations that make it unsuitable for monitoring reliability improvement progress. These limitations are explored and a model based on variance‐stabilizing transformation theory is explained. This model retains the ease of use while also avoiding the disadvantages of the Duane model. It represents a more useful graphical model for portraying reliability improvement at development team meetings. Computer simulations have shown that the new model provides a better fit to the data over the range of Duane slopes normally observed during a reliability growth program. The instantaneous mean time between failures (MTBF) equation for the new model is developed. Computer simulations show that its use results in higher values of instantaneous MTBF than that achieved by the Duane model. The new model also reduces the total test time for achieving a particular specified instantaneous MTBF. Finally, software failure data from an actual project illustrates the calculations and benefits of the new model.