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This study aims at introducing a method based on the failure mode, effects and criticality analysis (FMECA) to aid in selecting the most suitable formwork system with the minimum overall cost.

The research includes a review of the literature around formwork selection and analysis of data collected from the building construction industry to understand material failure modes. An FMECA-based model that estimates the total cost of a formwork system is developed by conducting a two-phased semi-structured interview and regression and statistical analyses. The model comprises material, manpower and failure mode costs. A case study of fifteen buildings is analysed using data collected from construction projects in the UAE to validate the model.

Results obtained indicate an average accuracy of 89% in predicting the total formwork cost using the proposed method. Moreover, results show that the costs incurred by failure modes account for 11% of the total cost on average.

The analysis is limited to direct costs and costs associated with risks; other costs and risk factors are excluded. The proposed framework serves as a guide to construction project managers to enhance decision-making by addressing the indirect cost of failure modes.

The research proposes a novel formwork system selection method that improves upon the subjective conventional selection process by incorporating the risks and uncertainties associated with the failure modes of formwork systems into the decision-making process.

The purpose of this paper is to investigate the extent at which the reliability of an aluminium wire rolling mill can be improved by ameliorating current control and maintenance practices.

The paper deals with the discrimination of the most critical components by substantial shop-floor failure data. The research work narrates a method for evaluating maintainability criticality index for each failure cause of identified critical components through two different MCDM approaches: one based on grey-complex proportional risk assessment (COPRAS-G) and the other based on preference section index (PSI).

The primary findings of this research work are to prioritize the maintenance activities by comparing results obtained through different failure analysis models. It is proposing improvements in the maintenance plan of critical components like bearings, gears and shafts of an aluminium wire rolling mill which are commonly representing the most critical components in a large range of industrial processes.

The limitation of the proposed study is that the failure models may not represent failures due to the first instant every time as adequate of design as such components are not checked for a high failure rate.

The proposed study is an interdisciplinary work which will help to understand about the working lives of components and associated failures. It will lead to reengineer new tools efficiently and to gain the maintenance excellence.

Originality mainly consists in the contemporary application of two non-identical MCDM-based methods (COPRAS-G and PSI). It will help to elucidate maintenance issues of major process industries and recommended deliverable keys.

The purpose of this paper is to explore the possibilities of improving the quality of existing maintenance task of the atomizer of milk powder manufacturing unit of a dairy plant. Looking to the past business volume and expected growth, the milk powder manufacturing unit forms a noticeable sector of processing plant. The lack of quality in maintenance standards leads to reliability losses of about 20–25% with low productivity and profit. Such facts and challenges of keeping the system in ready-state motivate a definite maintenance plan to be modeled based on a live failure analysis to be executed during shutdown or scheduled period.

The deliverables are achieved by collecting the historical failure data i.e. downtime and failure frequencies; from January 2020 to July 2020 at Dudhsagar dairy, Gujarat, India. Reliability modeling is done in a view to understand the failure pattern behavior of the milk powder manufacturing unit. The atomizer is discriminated as a critical component based on these data and their functional failures, failure causes, effects and repercussions of failures with existing control and maintenance practices has been modeled based on live shop-floor study. Scores are assigned on 1 to 10 levels by analyzing attributes effects from lowest to highest concern respectively for every modes of failure through realistic brain-storming among maintenance team by incorporating some advanced attributes like maintainability, economic safety, economic cost and spares with basic criteria in this study. The maintainability criticality index (MCI) is narrated by these score values through multi-attribute decision-making (MADM) based failure analysis models like Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

The primary findings of this research work are to propose improvements in the quality of the maintenance plan of critical component like; atomizer of a milk powder manufacturing unit which is commonly representing critical component in a major range of industrial processes. The case study recommended four silent maintenance strategies i.e. scheduled maintenance scheduled discard, scheduled failure finding and redesign as a qualified maintenance plan for the atomizer based on MCI and rankings of its potential failure causes. The results are helpful in upgrading quality standards for the maintenance activities of a process industry of alike or of dissimilar kinds in accordance with the failure analysis.

Originality mainly consists of investigating the scope of enhancing the existing maintenance practices through actual failure analysis with the help of TOPSIS. The criteria employed in this study are probability of chances of failure, degree of detectability and degree of severity as basic criteria along with some advanced criteria like; maintainability, spare parts, economic cost, economic safety are selected based on the outcome of shop-floor study and reliability modeling. The notable past failure statistics (downtime, frequency of failures) of a milk powder manufacturing unit were recorded and these data are analyzed based on reliability to extract an explicative component i.e. atomizer.

The paper presents an efficient methodology that was developed for the reliability prediction and the failure mode effects and criticality analysis (FMECA) of electronic devices using fuzzy logic.

The reliability prediction is based on the general features and characteristics of the MIL‐HDBK‐217FN2 technical document and a derating plan for the system design is developed in order to maintain low components’ failure rates. These failure rates are used in the FMECA, which uses fuzzy sets to represent the respective parameters. A fuzzy failure mode risk index is introduced that gives priority to the criticality of the components for the system operation, while a knowledge base is developed to identify the rules governing the fuzzy inputs and output. The fuzzy inference module is Mamdani type and uses the min‐max implication‐aggregation.

A typical power electronic device such as a switched mode power supply was analyzed and the appropriate reliability indices were estimated using the stress factors of the derating plan. The fuzzy failure mode risk indices were calculated and compared with the respective indices calculated by the conventional FMECA.

Further research efforts are needed for the application of fuzzy modeling techniques in the area of reliability assessment of electronic devices. These research efforts can be concentrated in certain applications that have practical value.

Practical applications can use a fuzzy FMECA modeling instead of the classical FMECA one, in order to obtain a more accurate analysis.

Fuzzy modeling of FMECA is described which can calculate fuzzy failure mode risk indices.

The aim of this paper is to develop a new tool for reliability and failure mode analysis by integrating the conventional aspects of the popular failure mode and criticality analysis (FMECA) procedure with economic considerations. Here FMECA is approached as a multi‐criteria decision making technique which integrates four different factors: chance of failure, chance of non‐detection, severity, and expected cost. To aid the analyst to formulate an efficient and effective priority ranking of the possible causes of failure, the analytic hierarchy process technique is adopted. With this technique, factors and alternative causes of failure are arranged in a hierarchic structure and evaluated only through the use of a series of pairwise judgements. With this new approach to failure investigation, the critical FMECA problem concerning the (direct) evaluation of failure factors is also by‐passed. The principles of the theory and an actual application in an Italian refrigerator manufacturing company are reported in the paper.

Emergency shutdown (ESD) systems play a critical role in ensuring safety and availability of oil and gas production. The systems are operated in on-demand mode, and the detection and prediction of their failures is deemed challenging. The purpose of this paper is to develop a logical data-driven approach to enhance the understanding and detectability of ESD system failures.

The study was conducted in close collaboration with the Norwegian oil and gas industry. The study and analyses were supported by industrial data, failure data generated in a test facility in Norway and domain experts.

The paper demonstrated that there is a considerable potential to improve the decision process and to reduce the workload related to ESD systems by means of a logical data-driven approach. The results showed that the failure analysis process can be executed with more clarity and efficiency. Common cause failures could also be identified based on the suggested approach. The study further underlined the requirements regarding relevant data, new competence and technical supports in order to improve the current practice.

The paper leveraged the value of real-time data in identifying failures through mapping of the interrelationships between data, failure mechanisms and decisions. The failure analysis process was re-designed, and the understanding and decision making related to the system was improved as a result. The process developed for ESDs can further be adapted as a common practice for other low-demand systems.

Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.

Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.

Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.

The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.

This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.

This study focuses on the application of reliability-centered maintenance (RCM) to a textile industry steam boiler. The study aims to demonstrate the development and application of RCM to a steam boiler used in the textile industry.

RCM is a structured process that develops maintenance activities needed on physical resources in their operational environment to realize their inherent reliability by logically incorporating an appropriate mixture of reactive, preventive, condition-based and proactive maintenance methods. A detailed analysis of the RCM approach is presented to develop preventive maintenance (PM) program and improve the reliability and availability of the steam boiler system.

The research reveals that the identification of PM tasks is a good indicator of the PM program's efficiency and can serve as an important maintenance-related downtime source. It is also discovered that the majority of maintenance programs that claim to be proactive are, in fact, reactive. This article also shows how RCM may be successfully implemented to any system, resulting in increased system reliability.

The paper focuses on a pilot study of the development and implementation of the RCM technique to a textile industry steam boiler. It is suggested that the developed RCM model can be applied to the entire plant.

The paper presents a comprehensive RCM model framework as well as an RCM decision framework, providing maintenance managers and engineers with a step-by-step approach to RCM implementation. The proposed framework is significant in that it may be utilized for both qualitative and quantitative analysis at the same time.

This study proposes a new model for assessing supply chain sustainability risk integrating subjectivity and objectivity of decision-maker. Research has shown the vacancy of study in dealing with the above issue. To fill this research gap, a new decision support model considering the subjectivity and objectivity of decision-makers in assigning the weight of the supply chain risk reprioritization criteria is presented and demonstrated using a case example.

This study adopts a new decision support model for assessing supply chain sustainability risk based on additional failure mode and effect analysis (FMEA) parameters and its integration with preference selection index (PSI) methodology and the Shannon entropy. A case example of the supply chain small and medium enterprise (SME) producing handy crafts has been used in this study.

The result of the study reveals critical sustainability risk dimensions and their risk elements demanding management attention to support realization to a more sustainable business operation.

The use of a single case study is often associated as a limitation in the research studies, and this study is based on findings from SMEs in the handy craft sector in a developing country. Nonetheless, future studies may focus on replicating this study using more samples. This preliminary study provides academics and practitioners with an exemplar of supply chain sustainability risk assessment from the SME in a developing country.

The result of this study is beneficial for practitioners, particularly owner-managers of SMEs who can use this study as guidance on how to identify and select the critical sustainability risks and plan mitigating strategies accordingly.

Scientific effort on appraising criticality of supply chain sustainability risks considering subjectivity and objectivity of decision-maker simultaneously is missing in earlier studies. To the best of the author’s knowledge, this is the first paper applying the PSI and Shannon entropy method and using it for evaluating the impact of supply chain risk based on five sustainability pillars. The findings and suggestions for future research initiatives will provide new insights for scholars and practitioners in managing SME supply chain sustainability risks.

The purpose of this study was to assess the readiness of the Swedish manufacturing industry to implement dynamic, data-driven preventive maintenance (PM) by identifying the gap between the state of the art and the state of practice.

An embedded multiple case study was performed in which some of the largest companies in the discrete manufacturing industry, that is, mechanical engineering, were surveyed regarding the design of their PM programmes.

The studied manufacturing companies make limited use of the existing scientific state of the art when designing their PM programmes. They seem to be aware of the possibilities for improvement, but they also see obstacles to changing their practices according to future requirements.

The results of this study will benefit both industry professionals and academicians, setting the initial stage for the development of data-driven, diversified and dynamic PM programmes.

First and foremost, this study maps the current state and practice in PM planning among some of the larger automotive manufacturing industries in Sweden. This work reveals a gap between the state of the art and the state of practice in the design of PM programmes. Insights regarding this gap show large improvement potentials which may prove important for academics as well as practitioners.