Search results

Steam explosions are a major safety concern in many modern furnaces. The explosions are sometimes caused by water ingress into the furnace from leaks in its high-pressure (HP) cooling water system, coming into contact with molten matte. To address such safety issues related to steam explosions, risk based inspection (RBI) is suggested in this paper. RBI is presently one of the best-practice methodologies to provide an inspection schedule and ensure the mechanical integrity of pressure vessels. The application of RBIs on furnace HP cooling systems in this work is performed by incorporating the proportional hazards model (PHM) with the RBI approach; the PHM uses real-time condition data to allow dynamic decision-making on inspection and maintenance planning.

To accomplish this, a case study is presented that applies an HP cooling system data with moisture and cumulated feed rate as covariates or condition indicators to compute the probability of failure and the consequence of failure (CoF), which is modelled based on the boiling liquid-expanding vapour explosion (BLEVE) theory.

The benefit of this approach is that the risk assessment introduces real-time condition data in addition to time-based failure information to allow improved dynamic decision-making for inspection and maintenance planning of the HP cooling system. The work presented here comprises the application of the newly proposed methodology in the context of pressure vessels, considering the important challenge of possible explosion accidents due to BLEVE as the CoF calculations.

This paper however aims to optimise the inspection schedule on the HP cooling system, by incorporating PHM into the RBI methodology, as was recently proposed in the literature by Lelo et al. (2022). Moisture and cumulated feed rate are used as covariate. At the end, risk mitigation policy is suggested.

In this paper, the proposed methodology yields a dynamically calculated quantified risk, which emphasised the imperative for mitigating the risk, as well as presents a number of mitigation options, to quantifiably affect such mitigation.

This research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.

To obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.

The study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.

The study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.

The research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.

This work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

Although trade volumes in e-retailing have increased significantly in recent years, logistics service failures are inevitable, especially at the delivery stage. Therefore, it is essential to provide customers with effective recovery strategies to increase their satisfaction and repurchase intentions. There is a lack of empirical evidence on whether the response time or the discount offered in compensation is more crucial for customers. Therefore, this study aims to determine whether the response time or the discount offered for high and low criticality failures has a greater impact on customer satisfaction levels and repurchase intentions for female and male customers.

A scenario-based experimental design method has been adopted to collect data, and 697 participants aged 18 and 58 years have been reached. The research utilized a between-subjects design, incorporating three factors: gender (female vs male), criticality (high vs low) and compensation (7 days: 10% discount, 10 days: 20% discount and 14 days: 30% discount). Six scenarios depicting the failure of an online retailer were created, and factorial univariate ANOVA was conducted to test the hypotheses.

The study's results show that in terms of customer satisfaction, female customers attach more importance to the response time in the case of high criticality and the amount of discount offered in the case of low criticality. On the other hand, male customers give more importance to the response time in terms of customer satisfaction when they experience a high or low criticality failure. In the case of low criticality, response time is more important for male customers to increase their repurchase intentions, while the amount of the discount is more important for female customers.

The study demonstrates the relative importance of the response time and discount amount according to the criticality level of failures and to guide business managers in terms of the recovery strategies they will implement. It focuses on gender differences and determine whether the response time or discount amount is more important for male and female customers in high or low-criticality situations.

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.

The paper follows Jason Cope's (2011) vision of a holistic perspective on the failure-based learning process. By analyzing the research since Cope's first attempt, which is often fragmentary in nature, and providing novel empirical insights, the paper aims to draw a new comprehensive picture of all five phases of entrepreneurial learning and their interplay.

The study features an interpretative phenomenological analysis of in-depth interviews with 18 failed entrepreneurs. Findings are presented and discussed in line with experiential learning theory and Cope's conceptual framework of five interrelated learning timeframes spanning from the descent into failure until re-emergence.

The study reveals different patterns of how entrepreneurs experience failure, ranging from abrupt to gradual descent paths, different management and coping behaviors, and varying learning effects depending on the new professional setting (entrepreneurial vs non-entrepreneurial). Analyzing the entrepreneurs' experiences throughout the process shows different paths and connections between individual phases. Findings indicate that the learning timeframes may overlap, appear in different orders, loop, or (partly) stay absent, indicating that the individual learning process is even more dynamic and heterogeneous than hitherto known.

The paper contributes to the field of entrepreneurial learning from failure, advancing Cope's seminal work on the learning process and -contents by providing novel empirical insights and discussing them in the light of recent scientific findings. Since entrepreneurial learning from failure is a complex and dynamic process, using a holistic lens in the analysis contributes to a better understanding of this phenomenon as an integrated whole.

The design check regarding the fire resistance of concrete slabs can be easily performed using tabulated values. These tables are based on experimental results, but the level of safety, which is obtained by this approach, is not known. On the other hand, performance-based methods are more accepted, but require a target reliability as performance criterion. Hence, there is a need for calibration of the performance-based methods using the results of the “traditional” descriptive approach.

The calibration is performed for a single span concrete slab, where the axis distance of the reinforcement is chosen according to Eurocode 2 for a defined fire rating. A “standard” compartment is selected to cover typical fields of application. The opening factor is considered as parameter to obtain the maximum peak temperatures in the compartment. A Monte Carlo simulation, in combination with a response surface method, is set up to calculate the probabilities of failure.

The results indicate that the calculated reliability index for a standard is within the range, which has been used for the derivation of safety and combination factors in the Eurocodes. It can be observed that members designed for a fire rating R90 have a significant increase in the structural safety for natural fires compared to a design for a fire rating R30.

The level of safety, which is obtained by a design based on tabulated values, is quantified for concrete slabs. The results are a necessary input for the calibration of performance-based methods and could stimulate discussions among scientists and building authorities.

New product introductions, particularly line extensions (LEs), are common in consumer goods categories. Despite their commonality, the success of LEs are not guaranteed. The purpose of this study is to provide brands that introduce LEs a benchmark about what success to expect.

This study investigates the success of 36,994 LEs in each quarter for the first three years after introduction. Four indicators are calculated using consumer panel data to benchmark how long LEs survive (failure rate), how competitive they are in the category (market share) and how they are adopted by category buyers (penetration and repeat buyer rate).

Most LEs survive after the first year, but many cease to exist or perform well in the long term. Around 50% of LEs fail a year after launch, but this failure rate halves once seasonal LEs are removed. Failure rates start to approach 80% after three years. Most LEs do not perform better than existing products. Around three in four LEs have a market share or penetration near or below the category norm. Although this percentage decreases the longer after launch, most LEs are still below the category norm.

These new product success benchmarks provide guidelines to practitioners about what success the “typical” LE will achieve. This research can help guide new product investment decisions because it provides context on what is feasible to achieve.

Four market success measures are used, a departure from past benchmarking research which uses practitioner evaluation on metrics seldom used in practice. The authors provide guidelines about when and how to measure LE and new product success more broadly.

This paper is a response to the failure of construction firms to use sufficient attention to their working capital management (WCM) practices, resulting in operational challenges, and leading to the collapse of firms in most developing countries. Hence, this study aims to explore the empirical perspective of WCM practices among large building construction firms (LBCFs) in Ghana, to help achieve the Sustainable Development Goal 9.

The study collected primary data through structured survey questionnaires from LBCFs in Ghana. The CEOs/Directors, General Managers and Accountant/Finance of LBCFs in Ghana formed the unit of analysis based on a simple random sampling technique. Mean score, standard deviation and one-sample t-test were used to perform the empirical analysis of the study.

According to this study's empirical results, LBCFs appear to have effective WCM practices in place. This was evidenced in the surveyed responses which indicate that the sector’s WCM practices sound good based on the mean scores and statistically significant as the t-values > 1.664. Notably, LBCFs in Ghana pay their suppliers early to reduce the fear of adverse effect of late payments on their credit history, making them conservative in their approach toward financial management.

This is a pioneering paper in a developing country like Ghana, highlighting the significance of gaining an in-depth understanding of WCM practices among LBCFs. The findings of this study are expected to provide valuable information to industry players toward ensuring WCM efficiencies and can serve as a solid foundation for further empirical studies.

The purpose of this study was to examine entrepreneurs’ learning before, during and after entrepreneurial failure and understand the relationship between learning and recovery from failure.

A qualitative multiple case study was carried out based on entrepreneur interviews who have experienced the failure of their businesses.

The study finds that entrepreneurs learn both during the company’s lifespan and post-failure, with distinct types and intensities of learning at different life cycle phases. It highlights the link between learning and emotions during the failure process, revealing entrepreneurs’ limited awareness of their knowledge gaps, particularly during successful business phases, and shows the difference between women and men.

One limitation of this study is that the companies are all located in northern and central Portugal, and the number of entrepreneurs starting new ventures post-failure is limited. Another is a lack of comprehensive measurement of the economic impact, especially on the health of individuals who have experienced the impact of failure. The absence of concrete data hampers understanding and the development of targeted support mechanisms for these individuals.

This study stands out for its unique approach, thoroughly exploring the intricate, profound and significant experiences during a crisis, such as a business failure, from the entrepreneurs’ perspective. It delves into their learning processes before, during and after the failure, providing a comprehensive understanding. This study evidence that significant learning occurs during the operation of the business, and not during or after failure, due to the limitations imposed by the pain and disorientation it causes. Therefore, if recovery does not occur, learning does not happen either. It also highlights the differences between women and men in their learning experiences, adding a new dimension to the research.

Adhesive bonding is critical to the effectiveness and structural integrity of 3D printed components. The purpose of this study is to investigate the effect of joint configuration on failure loads to improve the design and performance of single lap joints (SLJs) in 3D printed parts.

In this study, adherends were fabricated using material extrusion 3D printing technology with polyethylene terephthalate glycol (PETG). A toughened methacrylate adhesive was chosen to bond the SLJs after adherend printing. In this study, response surface methodology (RSM) was used to examine the effect of the independent variables of failure load, manufacturing time and mass on the dependent variable of joint configuration; adherend thickness (3.2, 4.0, 4.8, 5.6, 6.4, and 7.2 mm) and overlap lengths (12.7, 25.4, 38.1, and 50.8 mm) of 3D printed PETG SLJs.

The strength of the joints improved significantly with the increase in overlap length and adherend thickness, although the relationship was not linear. The maximum failure load occurred with a thickness of 7.2 mm and an overlap of 50.8 mm, whilst the minimum failure load was determined with a thickness of 3.2 mm and an overlap of 12.7 mm. The RSM findings show that the optimum failure load was achieved with an adherend thickness of 3.6 mm and an overlap length of 37.9 mm for SLJ.

This study provides insight into the optimum failure load for 3D printed SLJs, reducing SLJ production time and mass, producing lightweight structures due to the nature of 3D printing, and increasing the use of these parts in load-bearing applications.