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The purpose of this paper is to provide comprehensive information on preventive maintenance (PM) planning and methods used in the industry in order to achieve an effective maintenance system.

The literature review is organized in a way that provides the general overview of the researches done in the PM. This paper discusses the literatures that had been reviewed on four main topics, which are the holistic view of maintenance policies, PM planning, PM planning concept and PM planning-based in developing optimal planning in executing PM actions.

PM policy is one of the original proactive techniques that has been used since the start of researches on maintenance system. Review of the methods presented in this paper shows that most researches analyse effectiveness using artificial intelligence, simulation, mathematical formulation, matrix formation, critical analysis and multi-criteria method. While in practice, PM activities were either planned based on cost, time or failure. Research trends on planning and methods for PM show that the variation of approaches used over the year from early 1990s until today.

Research about PM is known to be extensively conducted and majority of companies applied the policy in their production line. However, most analysis and method suggested in published literatures were done based on mathematical computation rather than focussing on solution to real problems in the industry. This normally would lead to the problems in understanding by the practitioner. Therefore, this paper presented researches on PM planning and suggested on the methods that are practical, simple and effective for application in the real industry.

The originality of this paper comes from its detail analysis of PM planning in term of its research focus and also direction for application. Extensive reviews on the methods adopted in relation to PM planning based on the planning-based such as cost-based, time-based and failure-based were also provided.

The paper aims to explore unavailability of dormant systems that are under both preventive and corrective maintenance. Preventive maintenance is considered as a failure based maintenance model, where full renew is realized at the occurrence of every nth failure. It proposes the imperfect corrective maintenance model, where each restoration process deteriorates the system lifetime, probability distribution of which is gradually changed via increasing failure rate.

Basic reliability mathematics necessary for unavailability quantification of a system which undergoes a real aging process with maintenance has been derived proceeding from renewal theory. New renewal cycle was defined to cover the real aging process and the expectation of its length was determined. All events resulting in the failure of studied system were explored to determine their probabilities. An integral equation where the unavailability function characterizing studied system is its solution was derived.

Preventive maintenance is closely connected with the occurrence of the nth failure, which starts its renew. The number n can be considered as a parameter which significantly influences the unavailability course. The paper shows that the real aging process characterized by imperfect repairs can significantly increase the unavailability courses in contrast with theoretical aging. This is true for both monitored and dormant systems.

Although mathematical methods used in this article were inspired and influenced by the work of reference (van der Weide and Pandey, 2015), derivation of final formulas for unavailability quantification considering the new renewal cycle is original. Idea of the real aging process is new as well. This paper fulfils an identified need to manage the maintenance of realistically aging systems.

In this article a model is presented concerning the organisation of the maintenance process of a quality system. This model consists of the composition of two existing models. The point of departure is a three‐level model of quality management. Then each of these three levels has been split up into two components called “system‐structural” and “social‐structural”. After introducing several maintenance concepts on a conceptual level, these concepts are applied to each of these levels and components.

This paper presents part of an EPSRC research project carried out by the Construction Management Research Unit at Dundee University. The objective of the research was to apply integrated logistics support, to the development of cost‐effective maintenance strategies for existing building stock. Techniques such as failure mode and effects analysis (FMEA) and reliability centred maintenance (RCM) were tailored for application to construction projects and a pilot study was carried out on a sample of 18 houses drawn from Dundee City Council housing stock. A comparative analysis of condition survey, FMEA and RCM is provided. The main objective of this paper is to evaluate the benefits of applying RCM to existing building stock. The pilot study shows that the use of RCM can lead to a reduction of 18.5 per cent in maintenance costs.

With the increasing use of crowdfunding platforms in raising funds, it has become an important and oft-researched topic to analyze the critical factors associated with successful or failed crowdfunding. However, as a major subject of crowdfunding, medical crowdfunding has received much less scholarly attention. The purpose of this paper is to explore how contingency factors combine and casually connect in determining the success or failure of medical crowdfunding projects based on signal theory.

The paper adopts the crisp-set qualitative comparative analysis to analyze the causal configurations of 200 projects posted on a leading medical crowdfunding platform in China “Tencent Donation.” Five anecdotal conditions that could have an impact on the outcome of medical crowdfunding campions were identified. Three relate to the project (funding duration, number of images and number of updates) and two relate to the funding participants (type of suffer and type of fund-raiser).

The results show that diversified configurations of the aforementioned conditions are found (six configurations for successful medical crowdfunding projects and four configurations for failed ones).

Despite the fact that there are a considerably large number of medical crowdfunding projects, relatively few researches have been conducted to investigate configurational paths to medical crowdfunding success and failure. It is found that there are certain combinations of conditions that are clearly superior to other configurations in explaining the observed outcomes.

The purpose of this paper is to implement a strategic decision-making framework by selecting clusters of maintainable machines and scheduling their maintenance as part of a company’s manufacturing strategy.

Multi-criteria clustering problem in conjunction with the application of a group technology is used to establish clusters of maintainable machines based on their need for maintenance according to the type of failures they can encounter.

Using the concept of group technology in conducting preventive maintenance will result in the grouping of machines according to the impact of a failure based on the criteria specified by the decision makers. Accordingly, it will facilitate the process of executing the maintenance itself by ordering spare parts and informing the maintenance personnel which will lead to minimize the maintenance cost.

The results presented in this paper are reliable, objective may be used to minimize the total cost of conducting preventive maintenance in a manufacturing environment.

Failure plays a pivotal role in entrepreneurial learning. Knowledge of the learning process that enables an entrepreneur to re-emerge stronger after a failure, though considerable, is fragmented. This paper systematically collects relevant literature, assigns it to the stages of the experiential learning process (concrete experience, reflective observation, abstract conceptualization, active experimentation; Kolb, 1984), evaluates the research coverage of each stage and identifies promising avenues for future research.

This systematic literature review follows the guidelines articulated by Short (2009) and Tranfield et al. (2003), using Web of Science and EBSCO as primary data sources. Kolb’s (1984) experiential learning theory provides a basis for organizing the identified material into a framework of entrepreneurial learning from failure.

The literature provides insights on all stages of the process of entrepreneurial learning from failure. Particularly well elaborated are the nature of failure and its triggering effect for reflection, the factors influencing reflection, the contents of the resulting learning and their application in entrepreneurial re-emergence. Other topics remain under-researched, including alternative modes of recovery, the impact of personal attributes upon reflection, the cognitive processes underlying reflection, the transformation of failure-based observations into logically sound concepts and the application of this learning in non-entrepreneurial contexts.

This review provides the most complete overview of research into the process of entrepreneurial learning from failure. The systematic, theory-based mapping of this literature takes stock of current knowledge and proposes areas for future research.

Managerial nonpecuniary preferences have been emphasised by the behavioural theories of nonprofit organisation but only weakly related to this organisation's market failure theories. The present paper aims to fill this gap by examining the ways in which the market failure‐addressing capacity of nonprofit firms requires recourse to managerial nonpecuniary preferences.

The paper proceeds by examining the ways in which the market failure theories of nonprofit organisation conceptualise this organisation's market failure‐addressing mechanism.

It is shown that the market failure theories of nonprofit organisation can be logically complete only if they include an explanation of managerial motivation consisting in the gratification of nonpecuniary preferences.

Nonprofit firms are thereby shown to address market failures in a way different from that of for‐profit firms. Specifically, whereas for‐profit firms address market failures based on their advantages over market organisation in processing information and aligning incentives, nonprofit firms make the production of goods and services that are undersupplied due to market failures the object of nonprofit managers' nonpecuniary preferences.

The economic theory of nonprofit organisation has been traditionally marked by a dichotomy of the market failure theories and behavioural theories, only the latter of which recognised the role of managerial nonpecuniary preferences. By demonstrating that these preferences are crucial to the former theories as well, this paper integrates these two theorising strands and thus deepens the theoretical understanding of the nonprofit sector.

Influenced by the constantly changing manufacturing environment, no single dispatching rule (SDR) can consistently obtain better scheduling results than other rules for the dynamic job-shop scheduling problem (DJSP). Although the dynamic SDR selection classifier (DSSC) mined by traditional data-mining-based scheduling method has shown some improvement in comparison to an SDR, the enhancement is not significant since the rule selected by DSSC is still an SDR.

This paper presents a novel data-mining-based scheduling method for the DJSP with machine failure aiming at minimizing the makespan. Firstly, a scheduling priority relation model (SPRM) is constructed to determine the appropriate priority relation between two operations based on the production system state and the difference between their priority values calculated using multiple SDRs. Subsequently, a training sample acquisition mechanism based on the optimal scheduling schemes is proposed to acquire training samples for the SPRM. Furthermore, feature selection and machine learning are conducted using the genetic algorithm and extreme learning machine to mine the SPRM.

Results from numerical experiments demonstrate that the SPRM, mined by the proposed method, not only achieves better scheduling results in most manufacturing environments but also maintains a higher level of stability in diverse manufacturing environments than an SDR and the DSSC.

This paper constructs a SPRM and mines it based on data mining technologies to obtain better results than an SDR and the DSSC in various manufacturing environments.

There is considerable interest in the value of user-generated content (UGC) and its antecedents. Despite its growing importance, existing studies have largely ignored the effects of UGC on customers’ responses to recovery efforts in the fashion industry. The aim of this paper is to examine the extent to which UGC influences customers’ responses to providers’ service failure and recovery efforts, particularly how millennials’ interactions impact recovery efforts.

The study uses a phenomenological hermeneutics and adopts theoretical sampling to collect empirical data from three European countries (France, Italy and the UK). The authors interviewed 60 millennials who had online service failure experiences in online fashion. This methodological framework was designed to illustrate the close relationships between subject and object as well as identify that data analysis and collection are undertaken in relation to consistent iterative interpretations in an evolving process of study. Drawing on multi-theoretical lenses, using actor–network and social influence theories, this study advances understanding through the development of a new conceptual model relating to individual characteristics.

Using actor–network theory and social influence theory, this study developed a conceptual model of four customer groups’ responses to service failure based on the severity of service failure and the level of customers’ online response following service failure.

The authors suggest some pragmatic implications of their conceptual model and explain how awareness of different customer groups can lead to effective decision-making for marketers. This study provides a set of practical insights that brand managers can use to recover service failures.