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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.

Patient safety is a top priority globally. A robust healthcare system requires strategic collaboration between research and development. The author analysed over 300 cases from seven hospitals using the failure modes, effects, and criticality analysis (FMECA) tool to understand the underlying causes of medical errors.

The author studied seven hospitals and 300 cases using FMECA to prioritise activities. The findings showed that high-priority events occurred less frequently but had the potential to cause the most harm. Team members evaluated independently to ensure unbiased evaluations. This approach is useful for setting priorities or assessing difficulties.

Poor communication and lack of coordination among staff in a healthcare organisation caused misunderstandings, ineffective decision-making, delays in patient care, and medical errors. Implementation of effective communication and coordination protocols can help avoid these problems.

The study recommends using FMECA to identify and prioritise failures and conducting in-depth analyses to understand their root causes. It also highlights the importance of interdisciplinary knowledge and soft skills for healthcare staff.

This study reveals the significance of FMECA in healthcare risk management and benchmarking. FMECA helps identify system failures, develop prevention strategies, and evaluate effectiveness against industry benchmarks. It offers healthcare professionals a valuable tool to enhance patient safety and improve healthcare quality.

This paper aims to simulate vital corrective actions (CAs) affecting system availability through a system dynamics approach based on the results obtained by analyzing the causal relationships among failure modes and effects analysis elements.

A stock and flow diagram has been developed to simulate system behaviors during a timeframe. Some improvement scenarios regarding the most necessary CAs according to their strategic priority and the possibility of eliminating root causes of critical failure modes in a roller-transmission system have been simulated and analyzed to choose the most effective one(s) for the system availability. The proposed approach has been examined in a steel-manufacturing company.

Results indicated the most effective CAs to remove or diminish critical failure causes that led to the less reliability of the system. It illustrated the impacts of the selected CAs on eliminating or decreasing root causes of the critical failure modes, lessening the system’s failure rate and increasing the system availability more effectively.

Results allow managers and decision-makers to consider different maintenance scenarios without wasting time and more cost, choosing the most appropriate option according to system conditions.

This study innovation would be the dynamic analysis of interactions among failure modes, effects and causes over time to predict the system behavior and improve availability by choosing the most effective CAs through improvement scenario simulation via VENSIM software.

The purpose of the study is to examine how risk factors contribute to the occurrence of defects in a process. By analyzing these risk factors in relation to process quality, the study aims to help organizations prioritize their resources and efforts toward addressing the most significant risks. These challenges, integrated with the emerging concept of Quality 4.0, necessitate a comprehensive risk assessment technique.

Fuzzy logic integrated with an analytic network process is used in the process failure mode and effects analysis for conducting risk identification and assessment under uncertainty. Through a mathematical model, the linkage of risk with Six Sigma is established and, finally, a value–risk matrix is developed for illustrating and analysing risk impact on process quality.

A case study on fused filament fabrication demonstrates the proposed methodology’s applicability. The results show its effectiveness in assessing risk factors’ impact on Six Sigma metrics: defects per million opportunities/sigma level.

By integrating qualitative assessments and leveraging available data, this approach enables a more comprehensive understanding of risks and their utilization for an organization’s quality improvement initiatives.

This approach establishes a risk-centric Six Sigma assessment method in accordance with the requirement of ISO 9001:2015 and in the context of Quality 4.0.

This study aims to quantify and prioritize the main causes of lean wastes and to apply reduction methods by employing better waste cause identification methodologies.

We employed fuzzy techniques for order preference by similarity to the ideal solution (FTOPSIS), fuzzy analytical hierarchy process (FAHP), and failure mode effect analysis (FMEA) to determine the causes of defects. To determine the current defect cause identification procedures, time studies, checklists, and process flow charts were employed. The study focuses on the sewing department of a clothing industry in Addis Ababa, Ethiopia.

These techniques outperform conventional techniques and offer a better solution for challenging decision-making situations. Each lean waste’s FMEA criteria, such as severity, occurrence, and detectability, were examined. A pairwise comparison revealed that defect has a larger effect than other lean wastes. Defects were mostly caused by inadequate operator training. To minimize lean waste, prioritizing their causes is crucial.

The research focuses on a case company and the result could not be generalized for the whole industry.

The study used quantitative approaches to quantify and prioritize the causes of lean waste in the garment industry and provides insight for industrialists to focus on the waste causes to improve their quality performance.

The methodology of integrating FMEA with FAHP and FTOPSIS was the new contribution to have a better solution to decision variables by considering the severity, occurrence, and detectability of the causes of wastes. The data collection approach was based on experts’ focus group discussion to rate the main causes of defects which could provide optimal values of defect cause prioritization.

The purpose of this paper is to propose a new combined finite-discrete element method (FDEM) to analyze the mechanical properties, failure behavior and slope stability of soil rock mixtures (SRM), in which the rocks within the SRM model have shape randomness, size randomness and spatial distribution randomness.

Based on the modeling method of heterogeneous rocks, the SRM numerical model can be built and by adjusting the boundary between soil and rock, an SRM numerical model with any rock content can be obtained. The reliability and robustness of the new modeling method can be verified by uniaxial compression simulation. In addition, this paper investigates the effects of rock topology, rock content, slope height and slope inclination on the stability of SRM slopes.

Investigations of the influences of rock content, slope height and slope inclination of SRM slopes showed that the slope height had little effect on the failure mode. The influences of rock content and slope inclination on the slope failure mode were significant. With increasing rock content and slope dip angle, SRM slopes gradually transitioned from a single shear failure mode to a multi-shear fracture failure mode, and shear fractures showed irregular and bifurcated characteristics in which the cut-off values of rock content and slope inclination were 20% and 80°, respectively.

This paper proposed a new modeling method for SRMs based on FDEM, with rocks having random shapes, sizes and spatial distributions.

One of the primary challenges associated with excavation near buildings is the significant decrease in the bearing capacity of nearby foundations during the initial stages before the stabilization of the excavation wall. This study aims to investigate the correlation between excavation height and foundation-bearing capacity under actual field conditions.

This paper uses a three-dimensional rotational failure mechanism to propose a novel method for estimating foundation-bearing capacity using the upper bound limit analysis approach.

The study delineates two distinct zones in the excavation height versus bearing capacity diagram. Initially, there is a significant reduction in foundation-bearing capacity at the onset of excavation, with decreases of up to 80% compared to its undisturbed state. Within a specific range of excavation heights, the bearing capacity remains relatively constant until reaching a critical height. Beyond this threshold, the entire soil mass behind the excavation wall becomes unstable. The critical excavation height is notably influenced by the soil's internal friction angle, excavation slope angle and soil cohesion parameter. Notably, when the ratio of excavation height to foundation width is less than 0.4, changes in slope angle have no significant impact on bearing capacity.

The bearing capacity estimates derived from the method proposed in this paper are deemed to reflect real-world scenarios closely compared to existing methodologies.

Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

Reliability study plays a significant role in supporting the operation of any machinery working in a dynamic and harsh environment. This quality is inherently uncertain and a stochastic variable of any system. This study will be beneficial for designing an appropriate maintenance schedule, reducing unplanned production downtime and reducing maintenance cost of electrical motor operated particularly in dynamic and harsh environmental industries.

This study focused on the effects of operating conditions (OCs) on the operational reliability and remaining useful life (RUL) of machinery. A probabilistic graphical method called Bayesian network (BN) was used for studying the effect of OCs on the system performance. The developed methodology has been demonstrated by analyzing the operational reliability and predicting the RUL of electrical motors operated in a heavy mining machinery.

The failure probabilities estimated from the historical data of the motor system are failure likelihood, and OCs are the evidence in the developed BN model. It has been observed that the performance and RUL of the motor are significantly influenced by OCs and maintenance. A threshold value of reliability at which the motor system requires maintenance or replacement has been proposed to guide management in decision making.

The Bayesian approach for studying the covariate of motor reliability and RUL estimation is a novel approach. This study will be beneficial for designing an appropriate maintenance schedule, reducing unplanned production downtime and reducing maintenance cost of electrical motor operated particularly in dynamic and harsh environmental industries.

The design of cantilever pile walls (CPWs) presents several common challenges. These challenges include soil variability, groundwater conditions, complex loading conditions, construction considerations, structural integrity, uncertainties in design parameters and construction and monitoring costs. Accordingly, this paper is to provide a detailed literature review on the design criteria of CPWs, specifically in cohesionless soil. This study aims to present a comprehensive overview of the current state of knowledge in this area.

The paper uses a literature review approach to gather information on the design criteria of CPWs in cohesionless soil. It covers various aspects such as excavation support systems (ESSs), deformation behavior, design criteria, lateral earth pressure calculation theories, load distribution methods and conventional design approaches.

The review identifies and discusses common challenges associated with the design of CPWs in cohesionless soil. It highlights the uncertainties in determining load distribution and the potential for excessive wall deformations. The paper presents various approaches and methodologies proposed by researchers to address these challenges.

The paper contributes to the field of geotechnical engineering by providing a valuable resource for geotechnical engineers and researchers involved in the design and analysis of CPWs in cohesionless soil. It offers insights into the design criteria, challenges and potential solutions specific to CPWs in cohesionless soil, filling a gap in the existing knowledge base. The paper draws attention to the limitations of existing analytical methods that neglect the serviceability limit state and assume rigid plastic soil behavior, highlighting the need for improved design approaches in this context.