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To meet energy demand and tackle the challenges posed by global warming, Bagasse-based Cogeneration Power Generation (BCPG) plant in sugar mills have tremendous potential due to large-scale supply of renewable fuel called bagasse. To meet this goal, an integrated framework has been proposed for analyzing performance issues of BCPG.

Intuitionistic Fuzzy Lambda-Tau (IFLT) approach was implemented to compute various reliability parameters. Intuitionistic Fuzzy Failure Mode and Effect Analysis (IF-FMEA) approach has been implemented for studying risk issues results in decrease in plant's availability. Moreover, IF- Technique for Order Performance by Similarity to Ideal Solution (IF-TOPSIS) is implemented to verify accuracy of IF-FMEA approach.

For membership and non-membership functions, availability decreases to 0.0006% and 0.0020% respectively for spread ±15% to ±30%, and further decreases to 0.0127% and 0.0221% for spread ±30% to ±45%. Under risk assessment failure causes namely Storage tank (ST3), Valve (VL6), Transfer pump (TF8), Deaerator tank (DT11), High pressure heater and economiser (HP15), Boiler drum and super heater (BS22), Forced draft and Secondary air fan (FS25), Air preheater (AH29) and Furnace (FR31) with Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance (IFHWED) based output scores – 0.8988, 0.9752, 0.9400, 0.8988, 0.9267, 1.1131, 1.0039, 0.8185, 1.0604 were identified as the most critical failure causes.

Reliability and risk analysis results derived from IFLT and IF-FMEA approaches respectively, to address the performance issues of BCPG is based on the quantitative and qualitative data collected from the industrial experts and maintenance log book. Moreover, to take care of hesitation in expert's knowledge, IF theory-based concept is incorporated so as to achieve more accuracy in analysis results. Reliability and risk analysis results together will be helpful in analyzing the performance characteristics and diagnosis of critical failure causes, which will minimize frequent failure in BCPG.

The framework will help plant managers to frame optimal maintenance policy in order to enhance the operational aspects of the considered unit. Moreover, the accurate and early detection of failure causes will also help managers to take prudent decision for smooth operation of plant.

The results obtained ensure continuous operation of plant by utilizing the bagasse as fuel in boiler and also mitigate the wastages of fuel. If this bagasse (green fuel) is not properly utilized, there remains a dependency on coal-based power plants to meet the power demand. The results obtained are useful for decreasing dependency on coal, and promoting bagasse as the green, and alternative fuel, the emission by burning of these fuels are not harmful for environment and thereby contribute in preventing the environment from harmful effect of GHGs gases.

IFLT approach has been implemented to develop reliability modeling equations of the BCPG unit, and furthermore to compute various reliability parameters for both membership and non-membership function. The ranking results of IF-FMEA are compared to IF-TOPSIS approach. Sensitivity analysis is done to check stability of proposed framework.

This study's main objective is to examine the effect of learning from entrepreneurial failure on performance, with a type of failure as a moderator variable. Interactions between internal and external causes of failure and learning from entrepreneurial failure are also investigated, as well as entrepreneurs' aspects (i.e. age, experience and education) and organisational contextual factors (i.e. size, sector and location).

This study employed a hypothetico-deductive approach through a survey of 250 purposively sampled entrepreneurs who had suffered business failures. The survey data were subjected to regression analysis and moderated regression using WarpPLS software and an independent sample t test for an in-depth analysis.

The results indicated that learning from entrepreneurial failure positively affected business performance, an effect moderated by the type of failure, particularly with large failures. Only perceived internal causes of failure exerted a positive effect on learning from entrepreneurial failure; the external causes did not. The effect of failure on business performance was stronger on entrepreneurs who were older and experienced, had non-university educations and operated small- and medium-sized enterprises (SMEs) outside Java–Bali islands.

This study's findings provide empirical evidence that supports the experiential learning theory and attribution theory in explaining the interaction between learning and failure, its cause, its consequences and its magnitude as perceived by entrepreneurs of SMEs in Indonesia, where the rate of failure is relatively high. The authors’ study also emphasises the roles of the entrepreneur and organisational contextual factors, which matter in learning to improve performance.

An integrated intuitionistic fuzzy (IF) modelling-based framework for examining the performance analysis of a packaging unit (PU) in three different stages has been proposed.

For the series and parallel configuration of PU, a mathematical model based on the intuitionistic fuzzy Lambda–Tau (IFLT) approach was developed in order to calculate various reliability parameters at various spreads. For determining membership and non-membership function-based reliability parameters for the top event, AND/OR gate transitions expression was employed.

For 15%–30% spread, unit’s availability for the membership function falls by 0.006442%, and it falls even more by 0.014907% with an increase in spread from 30% to 45%. In contrast, for 15%–30% spread, the availability of non-membership function-based systems reduces by 0.007491% and further diminishes. Risk analysis has presented applying an emerging approach called intuitionistic fuzzy failure mode and effect analysis (IFFMEA). For each of the stated failure causes, the output values of the intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED)-based IFFMEA have been tabulated. Failure causes like HP1, MT6, FB9, EL16, DR23, GR27, categorized under subsystems, namely hopper, motor, fluidized bed dryer, distributor, grader and bin, respectively, with corresponding IFFMEA output scores 1.0975, 1.0190, 0.8543, 1.0228, 0.9026, 1.0021, were the most critical one to contribute in the system’s failure.

The limitation of the proposed framework lies in the fact that the results obtained for both reliability and risk aspects mainly depend on the correctness of raw data provided by the experts. Also, an approximate model of PU is obtained from plant experts to carry performance analysis, and hence more attention is required in constructing the model. Under IFLT, reliability parameters of PU have been calculated at various spreads to study and analyse the failure behaviour of the unit for both membership and non-membership function in the IFS of [0.6,0.8]. For both membership- and non-membership-based results, availability of the considered system shows decreasing trend. To improve the performance of the considered system, risk assessment was carried using IFFMEA technique, ranking all the critical failure causes against IFHWED score value, on which more attention should be paid so as to avoid sudden failure of unit.

The livelihood of millions of farmers and workers depends on sugar industries. So perpetual running of these industries is very important from this viewpoint. On the basis of findings of reliability parameters, the maintenance manager could frame a correct maintenance policy for long-run availability of the sugar mills. This long-run availability will generate revenue, which, in turn, will ensure the livelihood of the farmers.

Mathematical modelling of the considered unit has been done applying basic expressions of AND/OR gate. IFTOPSIS approach has been implemented for ranking result comparison obtained under IFFMEA approach. Eventually, sensitivity analysis was also presented to demonstrate the stability of ranking of failure causes of PU.

This study aims to endeavor to shed light on the underlying causal mechanisms behind the failure of startups by examining the failure process in such organizations. To achieve this goal, the study conducted a comprehensive review of the literature on the definition of failure and its various dimensions, resulting in the compilation of a comprehensive list of causes of startup failure. Subsequently, the failure process was analyzed using a behavioral strategy approach that encompasses rationality, plasticity and shaping, as well as the growth approach of startups based on dialectic, teleology and evolution theories.

The proposed research methodology was a case study using process tracing, with the sample being a failed platform in the ride-hailing technology sector. The causal mechanism was further explicated through the combined application of the behavioral strategy approach and interpretive structural modeling analysis.

The findings of the study suggest that the failure of startups is a result of interlinked causes and effects, and growth in these organizations is driven by dialectic, teleology and evolution theories.

The outcomes of the research can assist startups in formulating an effective strategy to deliver the right value proposition to the market, thereby reducing the chances of failure.

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.

Warranty service plays a critical role in sustainability and service continuity and influences customer satisfaction. Considering the role of social networks in customer feedback channels, one of the essential sources to examine the reflection of a product/service is social media mining. This paper aims to identify the frequent product failures through social network mining. Focusing on social media data as a comprehensive and online source to detect warranty issues reveals opportunities for improvement, such as user problems and necessities. This model will detect the causes of defects and prioritize improving components in a product-service system based on FMEA results.

Ontology-based methods, text mining and sentiment analysis with machine learning methods are performed on social media data to investigate product defects, symptoms and the relationship between warranty plans and customer behaviour. Also, the authors have incorporated multi-source data collection to cover all the possibilities. Then the authors promote a decision support system to help the decision-makers using the FMEA process have a more comprehensive insight through customer feedback. Finally, to validate the accuracy and reliability of the results, the authors used the operational data of a LENOVO laptop from a warranty service centre and classifier performance metrics to compare the authors’ results.

This study confirms the validity of social media data in detecting customer sentiments and discovering the most defective components and failures of the products/services. In other words, the informative threads are derived through a data preparation process and then are based on analyzing the different features of a failure (issues, symptoms, causes, components, solutions). Using social media data helps gain more accurate online information due to the limitation of warranty periods. In other words, using social media data broadens the scope of data gathering and lets in all feedback from different sources to recognize improvement opportunities.

This work contributes a DSS model using multi-channel social media mining through supervised machine learning for warranty-service improvement based on defect-related discovery to unravel the potential aspects of social networks analysis to predict the most vulnerable components of a product and the main causes of failures that lead to the inputs for the FMEA process and then, a cost optimization. The authors have used social media channels like Twitter, Facebook, Reddit, LENOVO Forums, GitHub, Quora and XDA-Developers to gather data about the LENOVO laptop failures as a case study.

Bearings are critical components used to support loads and facilitate motion for rotating and sliding parts of the machinery. Bearing malfunctions can cause catastrophic failures. Hence, failure analysis and endeavors to improve bearing performance are essential discussions for worldwide designers, manufacturers and end users of vital machinery. This study aims to investigate a type of roller bearing from the railway industry with premature failures. The task arises because locomotives’ maintenance and service life quality are vital to railway operations while providing transportation services for the nation. To assist in maintaining the designated locomotives, the present study scrutinizes the causes of failure of heavy-duty roller bearings from locomotive bogie axleboxes.

It is intended to inspect this bearing service life and statistically scrutinize its design parameters to reveal the failures’ shortcomings and origins. The significant measures include examinations of their failures’ primary and vital factors by comparing them with a real-life service history of 16 roller bearings of the same type. The bearings come from the axleboxes of a locomotive bogie with an axle load of 20 tons. The bearing loads are estimated using the EN13104 standard document and confirmed by the finite element method using ABAQUS engineering software. To validate the finite element modeling results, the bearings’ stress analysis is performed using the Hertzian contact theory that demonstrated perfect conformity. The said methods are also used to search for the areas susceptible to failures in these bearings. With the inclusion and exploitation of the bearing maintenance conditions and the logbook recordings of the locomotives for the past seven years, the critical cause for this type of bearing’s failures is surveyed and discussed.

With the inclusion and exploitation of the bearing maintenance conditions and the logbook recordings of the locomotives for the past seven years, the critical cause for this type of bearing’s failures is surveyed and discussed. As a crucial result, it is found that deprived maintenance and inadequate lubrication are the root causes of the loss of the selected bearings.

For the designated locomotives, the origins of the heavy-duty roller bearing failures and its design shortcomings are revealed by examining and comparing them with a real-life service history of many of the same types of bearings. The novelty of the research is in using the combination of the methods mentioned above and its decent outcome.

Instantaneous unloading with equal force is usually used to simulate the sudden failure of cables. This simulation method with equivalent force requires obtaining the magnitude and direction of the force for the failed cable in the normal state. It is difficult, however, to determine the magnitude or direction of the equivalent force when the shape of the cable is complex (space curve). This model of equivalent force may be difficult to establish. Thus, a numerical simulation method, the instantaneous temperature rise method, was proposed to address the dynamic response caused by failures of the cables with complex structural form.

This method can instantly reduce the cable force to zero through the instantaneous temperature rise process of the cable. Combined with theoretical formula and finite element model, the numerical calculation principle and two key parameters (temperature rise value and temperature rise time) of this method were detailed. The validity of this approach was verified by comparing it with equivalent force models. Two cable-net case with saddle curved surfaces were presented. Their static failure behaviors were compared with the dynamic failure behaviors calculated by this method.

This simulation method can effectively address the structural dynamic response caused by cable failure and may be applied to all cable structures.

An instantaneous temperature rise method (ITRM) is proposed and verified. Its calculation theory is detailed. Two key parameters, temperature rise value and temperature rise time, of this method are discussed and the corresponding reference values are recommended.

Quality failures in grid infrastructure construction would cause large-scale collapses in power supply and additional expenditures by reworks and repairs. Learning from quality failures (LFQF) extracts experience from previous quality events and converts them into preventive measures to reduce or eliminate future construction quality issues. This study aims to investigate the influence factors of LFQF in the construction of grid infrastructure.

The related factors of LFQF, including quality management (QM) practices, quality rectification, and individual learning, were identified by reviewing literature about organizational learning and extracting experience from previous failures. A questionnaire survey was distributed to the grid companies in North, Northeast, Northwest, East, Central, and Southwest China. 381 valid responses collected and analyzed using structural equation modeling (SEM) to test the influence of these factors on LFQF.

The SEM results support that QM practices positively affect individual learning and LFQF. Quality rectification indirectly impacts LFQF via individual learning, while the results did not support the direct link between quality rectification and LFQF.

The findings strengthen practical insights into extracting experience from poor-quality issues and continuous improvement. The contributory factors of LFQF found in this study benefit the practitioners by taking effective measures to enhance organizational learning capability and improve the long-term construction quality performance in the grid infrastructure industry.

Existing research about the application of LFQF still stays at the explorative and conceptual stage. This study investigates the related factors of LFQF, including QM practices, quality rectification, and individual learning, extending the model development of learning from failures (LFF) in construction QM.

This paper aims to examine the factors that lead to the failure of startups in India and proposes a ‘Four Dimensional (4D) Strategic Framework’ to drive success.

This study is exploratory and uses a narrative analysis methodology to analyse the accounts of key startup stakeholders – founders, investors, former employees and consumers; to identify their failure factors. A conveniently selected sample of 165 startups was studied to understand better the reasons for their failure within a thematic framework developed from David Feinleib’s (2012) handbook “Why Startups Fail”.

Results indicate that a dearth of capital or running out of money and inadequate sales and marketing strategy, which leads businesses to fall behind rivals and lose money on each transaction, are the most common factors for startup failure in India.

“Startups” are substantial for emerging economies like India because they fuel technological innovation and economic progress and provide for the modern workforce’s needs and aspirations. However, they seem to be typically unprofitable, with a modest probability of survival. Subsisting studies mainly focus primarily on success factors and very few on why startups fail, with significant disagreement on an appropriate methodology. To the best of the authors’ knowledge, this is the first study that analyses failure factors of Indian startups using narrative analysis of its key stakeholders. It aims to aid the conception of profitable entrepreneurship by reducing the failure frequency in the startup and small business ecology.