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The purpose of this paper is to suggest a method of selecting efficient customer service programmes and of providing relevant customer information to dealers, based on the analysis of repeat-purchase behaviour data in the automobile industry.

A recurrent event model is proposed and employed to determine which variables affect repurchasing behaviour in consumers' repurchase cycles. Unlike the conventional recurrent event model, the proposed model uses common variables for all strata, as well as stratum-specific variables.

Empirical results show that age, price difference, change in vehicle type, and marketing promotion affect the likelihood of repeat purchase. VIP service centres and repair services are effective marketing tools, and dealers should pay more attention to existing customers having certain characteristics, depending on prior purchase behaviour.

Though many customer service programmes are devised and implemented at great cost, Customer Relationship Management (CRM) data reveal that classic car-care services are the most essential. CRM can provide dealers with essential customer information that enables real purchases.

Collecting primary data on automobile purchase behaviour and customer service usage is difficult, and therefore, customer behaviour strategy is often formulated using basic principles alone. The paper proposes a method to construct a service strategy and formulate deal guidelines based on CRM data and statistical modelling.

The purpose of this paper is to provide the maintenance personnel with a methodology for modeling and estimating the reliability of critical machine systems using the historical data of their inter‐failure times.

The failure patterns of five different machine systems were modeled with NHPP‐log linear process and HPP belonging to stochastic point process for predicting their reliability in future time frames. Besides the classical approach, Bayesian approach was also used involving Jeffreys's invariant non‐informative independent priors to derive the posterior densities of the model parameters of NHPP‐LLP and HPP with a view to estimating the reliability of the machine systems in future time intervals.

For at least three machine systems, Bayesian approach gave lower reliability estimates and a larger number of (expected) failures than those obtained by the classical approach. Again, Bayesian estimates of the probability that “ROCOF (rate of occurrence of failures) would exceed its upper threshold limit” in future time frames were uniformly higher for these machine systems than those obtained with the classical approach.

This study indicated that, the Bayesian approach would give more realistic estimates of reliability (in future time frames) of the machine systems, which had dependent inter‐failure times. Such information would be helpful to the maintenance team for deciding on appropriate maintenance strategy.

With the help of Bayesian approach, the posterior densities of the model parameters were found analytically by considering Jeffreys's invariant non‐informative independent prior. The case study would serve to motivate the maintenance teams to model the failure patterns of the repairable systems making use of the historical data on inter‐failure times and estimating their reliability in future time frames.

The purpose of this paper is to present the results of the application of various models to estimate the reliability in railway repairable systems.

The methodology proposed by the International Electrotechnical Commission (IEC), using homogeneous Poisson process (HPP) and non-homogeneous Poisson process (NHPP) models, is adopted. Additionally, renewal process (RP) models, not covered by the IEC, are used, with a complementary analysis to characterize the failure intensity thereby obtained.

The findings show the impact of the recurrent failures in the times between failures (TBF) for rejection of the HPP and NHPP models. For systems not exhibiting a trend, RP models are presented, with TBF described by three-parameter lognormal or generalized logistic distributions, together with a methodology for generating clusters.

For those systems that do not exhibit a trend, TBF is assumed to be independent and identically distributed (i.i.d.), and therefore, RP models of “perfect repair” have to be used.

Maintenance managers must refocus their efforts to study the reliability of individual repairable systems and their recurrent failures, instead of collections, in order to customize maintenance to the needs of each system.

The stochastic process models were applied for the first time to electric traction systems in 23 trains and to 40 escalators with ten years of operating data in a railway company. A practical application of the IEC models is presented for the first time.

This study investigates whether a firm that has experienced an environmental accident (EA) is less likely to conduct a product recall. If true, it would indicate that EAs tempt firms to hide operational problems that need to be revealed. The logic is that both events are operational failures that damage a firm's reputation and share price. Following an EA, a firm may avoid a discretionary product recall to avoid providing additional evidence of operational incapability and social irresponsibility and thereby triggering amplified reputational and market penalties.

The dataset is compiled from several public and private sources and includes 4,355 product recalls, 153 EAs and 120 firms from the industries that often recall products, including automotive, pharma, medical device, food and consumer products. The study timeframe is 2002–2013. Empirical models are evaluated using hazard modeling.

Results show that EAs reduce the probability of a product recall by 32%, on average. Effect sizes are larger when accidents are more frequent or more severe and when recalls are less severe. Through post hoc analyses, the study finds support for the proposed mechanism that firms avoid recalls due to reputational concerns, provides evidence that EAs can have a lengthy impact on recall behavior, and shows that firms are more likely to avoid recalls managed by the CPSC and NHTSA than recalls managed by the FDA.

Prior studies in operations management (OM) have not examined the impact of one negative event on another. This study finds that EAs tempt firms to hide operational problems that need to be revealed. While recalling fewer defective products is of concern to consumers and regulators, should EAs influence a broader set of discretionary operational decisions, such as closing/relocating a production facility, outsourcing production or conducting a layoff, study implications increase significantly.

The purpose of this paper is to find the proper statistical distribution function, which can cover the failure time of a single machine or a group of machines. To this end, an innovative program is written in an Excel software, capable of assessing at least six statistical distribution functions. This research study intends to show the advantages of applying statistical distribution functions in an integrated model format to create or increase productive reliability machines. Productive reliability is a simultaneous combination of efficiency and effectiveness in reliability.

The method of theoretical research methodology comprises data collection tools, reference books and articles in addition to exploiting written reports of the Iranian Center for Defence’s Standards. The practical research method includes deploying and assessing the proposed model for a selected machine (in this case a computerized numerical control machine).

A comprehensive program in an Excel software having the capability of assessing at least six statistical distribution functions was developed to find the most efficient option for covering the failure times of each machine in the shortest time with the highest precision. This is regarded as the most important achievement of the present study. Furthermore, the advantages of applying the developed model are discussed and a large group of which have direct influences on the productivity of equipment reliability.

The originality of the research was ascertained by managers and experts working in maintenance issues at the different levels of the Defense Industries Organization.

This article provides the answer to the question asked in the title when certain simple conditions apply, and so improves the approximate solutions given by previous writers. The assumptions are: the machine either works perfectly or not at all; there is a constant probability of random failure; the cost of inspection and of restoring the machine to as‐new condition are both fixed: failure can only be detected by inspection.

Suppose that a machine is subject to failures at random with constant probability of p per unit time, and that the only way of telling whether the machine has failed is to test it. Given the above assumptions, presents a simplified equation to determine how often a machine should be inspected.

The purpose of this paper is to review cotemporary maintenance programs and analyze factory production data for an SF₆ gas filled circuit breaker population. Various maintenance techniques and studies are reviewed to understand the reliability of circuit breaker models and the impact manufacturing can have on long term maintenance considerations.

Production and field event data were analyzed using statistical analysis tools. The population data were formatted so that a recurrent event analysis could be conducted to establish the mean cumulative function (MCF) by model and product family (class). Average Field Two‐year Recorded Event Rate (AFTRER) is introduced and compared to commonly used Field Incident Rate (FIR) and Mean‐Time between Failure (MTBF) measures.

Common managerial operating questions can be answered as exhibited for the provided circuit breaker population. This includes the longevity of field issues, the anticipated life cycle of a model or class, and AFTRER for models or classes of interest. These statistical analysis tools are used to make critical production quality and asset management observations and aid in decision‐making.

Due to limitations in existing database systems, the cost of events and explanatory variables related to event rates were not included in the analyses. There remains much work to be done in terms of the installation and retro‐fitting of breakers with conditions monitors in the field.

A framework to analyze maintenance data from fleet of similar assets using recurrent event data analysis is provided. The methods illustrated here would be useful for quality and asset managers to make operating decisions. This includes resource allocation decisions across a network of equipment.

Data analyzed are for power circuit breakers which are a critical element in the operation and reliability of the US power grid.

Using recurrent event data analysis to review and develop solutions to production quality and asset management problems including a comparison of AFTRER to FIR and MTBF measures.

Predicting the final status of an ongoing process or a subsequent activity in a process is an important aspect of process management. Semi-structured business processes cannot be predicted by precise and mathematical methods. Therefore, artificial intelligence is one of the successful methods. This study aims to propose a method that is a combination of deep learning methods, in particular, the recurrent neural network and Markov chain.

The proposed method applies the BestFirst algorithm for the search section and the Cfssubseteval algorithm for the feature comparison section. This study focuses on the prediction systems of social insurance and tries to present a method that is less costly in providing real-world results based on the past history of an event.

The proposed method is simulated with real data obtained from Iranian Social Security Organization, and the results demonstrate that using the proposed method increases the memory utilization slightly more than the Markov method; however, the CPU usage time has dramatically decreased in comparison with the Markov method and the recurrent neural network and has, therefore, significantly increased the accuracy and efficiency.

This research tries to provide an approach capable of producing the findings closer to the real world with fewer time and processing overheads, given the previous records of an event and the prediction systems of social insurance.

The impact of event quality on expenditure and visitors’ loyalty has been an issue seldom analyzed by festival literature. These same incidental relations were not assessed from a temporary perspective in the case of recurrent cultural events. This paper aims to explore these causal relationships and to report on the moderation effect of the temporary inquiry on the editions of the festival.

Nine hypotheses were examined through squares SEM techniques, and the model validation was carried out by assessing the measurement and structural model. In addition, a multi-group analysis was performed to test the temporary moderation effect. Finally, a survey was applied during three successive editions (2013 = 164 cases, 2014 = 154 cases, 2015 = 128 cases).

The local and ephemeral nature of the festival favors immediate consumption, and the budget share increase among categories passes through diversification to stimulate purchases. In this particular context, the moderation induced by the sequence of editions had a conclusive impact on the analyzed relationships, generating the need to focus on the temporary variability to understand and operate the recurrent events.

The strengthening of the festival went through a stage where it opened to other segments of visitors despite the dominant opinion to preserve the local character of the event. In addition, this study clarifies that a retrospective analysis of previous editions, when compared to the stationarity perspective of the festival, allows a better understanding of the required upgrading to preserve visitor loyalty.

El impacto de la calidad del evento en el gasto y la lealtad de los visitantes ha sido un tema pocas veces analizado por la literatura de los festivales. Estas mismas relaciones incidentales no fueron valoradas desde una perspectiva temporal en el caso de eventos culturales recurrentes. El artículo explora estas relaciones causales e informa sobre el efecto moderador de la investigación longitudinal de las ediciones del festival.

Se examinaron nueve hipótesis a través de técnicas de cuadrados MES, y la validación se llevó a cabo mediante la evaluación del modelo estructural y de medición. Además, se realizó un análisis multigrupo para probar el efecto de moderación longitudinal. Los datos analizados provienen de encuestas aplicadas durante tres ediciones sucesivas (2013 = 164 casos, 2014 = 154 casos, 2015 = 128 casos).

El carácter local y efímero del evento favorece el consumo inmediato, y el aumento de la participación en el gasto entre categorías pasa por la diversificación que permite estimular la compra. En este contexto particular, la moderación inducida por la secuencia de ediciones incidió contundentemente en las relaciones analizadas, generando la necesidad de prestar atención a los cambios temporales para comprender y operar los eventos recurrentes.

El fortalecimiento del festival pasó por una etapa donde se abrió a otros segmentos de visitantes a pesar de la opinión dominante de preservar el carácter local del evento. Además, este estudio aclara que un análisis retrospectivo de ediciones anteriores, en comparación con la perspectiva de la estacionariedad del evento, permite gestionar la actualización necesaria que permite preservar la lealtad del visitante.