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Healthcare 4.0 (H4.0) is the term corresponding to Industry 4.0 (I4.0). Like other industries, the healthcare industry has also gone through several technological changes and got nourished with them. The health care system of many countries is not mature enough to swiftly transform from its current state to the H4.0 state. Even the matured healthcare systems are facing several challenges while adopting H4.0 practices. The implementation of H4.0 has many challenges (or barriers) to be overcome. This paper has attempted to understand and analyze the challenges that the current Indian hospital management system faces while implementing H4.0. This study uses an integrated approach to the decision-making process to understand the importance of these barriers from the perspective of the Indian health care system. With the help of the Analytical Hierarchy Process (AHP), Interpretative Structural Modeling (ISM) and MICMAC analysis, the paper has mapped the importance of barriers which need to be overcome for to make implementation of H4.0 possible.

PurposeAutomobile maintenance garages require varieties of equipment for their smooth functioning. However, the purchase of the right equipment from alternatives is a tough task as it depends on several economic, technical, and environmental considerations, etc. Moreover, there are different sellers for such equipment, whose features would be satisfying the purchase criteria in varying levels or degrees. Hence, this purchase decision becomes a complex decision-making problem.Design/methodology/approachAn integrated multi-criteria decision-making approach that includes the combination of fuzzy AHP (analytic hierarchy process) and GRA (grey relational analysis) is used for the purchase decision-making of garage equipment. Various purchase decision criteria regarding garage equipment are assimilated through literature and interaction with garage professionals. The weightage of each purchase criteria of garage equipment is derived using fuzzy AHP. After the establishment of weights, various equipment suppliers are evaluated according to their conformance to the criteria using the GRA method.FindingsThe methodology of FAHP helped in ranking the different purchasing criteria based on their importance. It follows the following sequence: cost of ownership, technical specifications, operational characteristics, reliability and maintenance, after-sales support, commercial features, environmental pollution, and end of life characteristics. GRA methodology has been applied for the purchase of the best common rail test bench among alternatives according to their fulfillment of the purchase criteria requirements that are evaluated by a team of experts.Originality/valueThe integrated approach developed in this work for garage equipment purchase will help garage management to prioritize each supplier of the equipment based on their level of conformance to the purchase criteria.

Purpose There is an increasing trend of outsourcing maintenance activities of heavy equipment, including belt conveyor installations. However, there are numerous challenges in maintenance outsourcing. This paper aims to identify and analyze various challenges of outsourcing maintenance activities associated with belt conveyor installations. Design/methodology/approach This paper identifies maintenance outsourcing challenges of belt conveyor installations through literature review, field visits and expert opinion. An integrated structural hierarchical framework of the identified challenges is developed through analytic hierarchy process and decision-making trial and evaluation laboratory. Findings The paper has identified eight challenges, namely, attainment of organizational strength by contractors, legal and financial challenges for contractors, attainment of necessary technician skills by contractors, maintenance data acquisition and analysis challenges, facilitation with modern equipment, gadgets and instrumentation, service quality challenges, health, safety and environment-related challenges and spares supply chain management challenges. The segregation of driver and dependent challenges, including their hierarchical framework had been established in this work. Research limitations/implications A comprehensive list of challenges and their prioritization in maintenance outsourcing of belt conveyor installations had been established. This will help the organizations who own and operate these installations to make judicious decisions regarding outsourcing maintenance. Originality/value This paper significantly contributes to the literature on maintenance outsourcing of heavy machinery installations like a belt conveyor system based on the input of different stakeholders. This study will lead to the development of frameworks for maintenance contractor selection for such installations.

In this paper, a multistage reciprocating compressor system is considered to study, analyze and improve its reliability by incorporating the redundancies using Markov approach. In Markov method, the reliability is assessed by creating state space model and deriving probabilistic equations of all the states. The developed equations are further solved with the help of a MATLAB. Constant failure and repair rates are considered for the different components in the system. Standby redundancy has been employed to enhance the system reliability. System reliability value for the basic system model, i.e. Model 1 at 10,000[Formula: see text]h is 0.1252 that has been increased by using standby redundancies in Model 4 with no repair to 0.6428 and with repair to 0.9374. This work will be helpful to the manufacturers, users, researchers and others by the application, and implementation of the proposed methodology.

PurposeThe purpose of this paper is to identify and analyze the challenges associated with the implementation of the concept of Maintenance 4.0 in industries.Design/methodology/approachThe challenges in the implementation of Maintenance 4.0 are identified through a literature survey and interaction with professionals from the industry and academia. A structural hierarchy framework that integrates the methodologies of ISM and MICMAC is used for the analysis of Maintenance 4.0 implementation challenges. The framework establishes the interrelationship among challenges and segregates them into driving, linkage, dependent and autonomous groups.FindingsA novel concept of Maintenance 4.0 under the aegis of Industry 4.0 is gaining appreciation worldwide. However, there are challenges in the adaptation of Maintenance 4.0 concepts among industries. The various challenges as well as their impact on the objective of implementation of Maintenance 4.0 are identified.Practical implicationsThe practicing engineers, academicians, researchers and the concerned industries can infer from the results to improve upon the causes of such challenges and promote the implementation of Maintenance 4.0 most efficiently and effectively.Originality/valueThis paper is a novel, unique and first of its kind that addresses the most contemporary challenges in the implementation of Maintenance 4.0 concepts in industries.

Purpose This paper aims to put forward a labelling system capable of reflecting the level of different Industry 4.0 (I4.0)features present in a manufacturing system and further propose a comparative index to collectively estimate and compare the system automation level. Design/methodology/approach Data for the empirical study were collected from interactions with the practising managers and experts. A relationship among the six I4.0 features is developed with fuzzy cognitive maps. Findings The paper proposed a simple and easy-to-understand labelling system for I4.0 systems, which indicates the automation level in each of six dimensions of any manufacturing system. The system is further strengthened by a proposed automation comparative index (ACI), which collectively reflects the automation level on a scale of “0” to “1”. Thus, the labelling system and parameter could help in comparing the level of automation in the manufacturing system and further decision-making. Research limitations/implications Only seven industrial sectors are illustrated in the paper, but the proposed concept of the classification scheme and ACI find their applicability on a large spectrum of industries; thus, the concept can be extended to other industrial sectors. Furthermore, a threshold value of ACI is a differentiator between a I4.0 and other automated systems. Both aspects have the scope of further work. Practical implications The way and pace by which the industrial world takes forward the concept of I4.0, soon it will need a labelling system and a parameter to assess the automation level of any automated system. The scheme assesses the automation level present in a manufacturing system. It will also estimate the level of the presence of each of all six attributes of an I4.0 system. Both labelling system and ACI will be the practical tools in the hands of the practising managers to help compare, identify the thrust areas and make decisions accordingly. Originality/value To the best of the authors’ knowledge, this is the first study of its kind that proposed the labelling system and automation comparison index for I4.0 systems.

The Indian manufacturing industry is gradually adopting the sustainable productivity-oriented processes of the fourth industrial revolution (4IR), but the sports goods manufacturing (SGM) sector has lagged far behind. In India, the city of Meerut is one of the hubs of SGM, providing about 25,000 direct jobs and exporting goods worth around ₹4.86 billion (~US$75 million). The thousands of mini, micro and small household manufacturing units of the Meerut sports goods manufacturing (MSGM) cluster widely operate with the centuries-old labour-intensive manufacturing practices. MSGM cluster has a vast untapped potential to increase productivity, quality and sustainability of processes. To remain relevant in this competitive era, a transformation of the sports goods manufacturing industry (SGMI) is the need of the hour. For this, the requirement for an integrated transformational framework capable of transforming the processes of the manufacturing cluster from its current state to intelligent manufacturing is felt. It will help the SGMI sustain and uplift its workforce’s employability, income and living standards. This transformation also invites the other allied industries to operate in the cluster, generating employment and business opportunities. In Decision-Making Trial and Evaluation Laboratory (DEMATEL)-based analysis of the MSGM cluster, cause–effect relations among eleven prevalent transformational challenges are deduced. From both prominence ranking and cause–effect relations for the eleven prevalent transformational challenges, ‘management’s willingness’, ‘government support system’, and ‘awareness and clear understanding of benefits (ROI)’ are observed among the prominent factors to be overcome. A transformational framework for SGMI from its present state to the I4.0 state is proposed. The study and proposed scalable framework with causal relationships will serve as a road map and tool for the smooth and effective transformation of the SGMI cluster.

In the present era of technological advancement and rapid change, mankind faces different kinds of challenges. The product lifecycle has been shortened dramatically, and the manufacturing industry is moving from mass production to the high personalization of mass products. This era is widely known as the fourth industrial revolution or Industry 4.0 (I4.0). It seems that soon it will be inevitable for every society. The paper briefly focuses on empirical insights into the impact of I4.0 on various sectors of society. The traditional approaches to manufacturing, servicing, and public policy planning will no longer remain effective and would be transformed. I4.0 should be observed as an Opportunity 4.0 and draw attention to the industry, academia, and policymakers’ in changing scenarios. Expectations from them are also underlined. Its favorable features in the post-COVID-19 era are also discussed. This short article invites attention to foresee beyond I4.0, e.g. Industry 5.0 and more such progressions.

Indian MSMEs involved in manufacturing lag behind in implementation of sustainable manufacturing practices. Therefore, Sustainability assessment plays a vital role in adopting and implementing the sustainable techniques and practices in manufacturing sectors. One of the reasons may be the lack of awareness about indicators which enable qualitative as well as quantitative assessment of level of sustainability. This further enable the organization to implement the practices, methods, technologies that enable higher sustainability of the organization. Present study attempts to identify the indicator which are vital to improving the sustainably of the organization. In this study, a set of indicators were identified and categorized based on Triple bottom line (environmental, economic and social) approach of sustainability. Further an expert opinion cum Fuzzy-AHP Based Approach was used to prioritize and rank these indicators. Present research work will be useful for industry practitioner and manger to identify the priority areas of focus to achieve improved sustainability of manufacturing organization.

Injection moulding (IM) appears to be one of the most important techniques for manufacturing plastics products worldwide. As IM is a complicated process, so it is technically very difficult to take into account all the changes taking place during the process. To enhance efficiency of the process and to have consistent product quality, proper input parameter’s setting is critically important. The numbers of input process parameters are: melt temperature, mold temperature, injection pressure, injection speed, injection time, holding pressure, holding time and cooling time. The responses/output may be mechanical properties, defects e.g., warpage, shrinkage, sink mark, residual thermal stress etc., and aesthetic requirement for example surface finish/roughness. Literature review shows that most of the researchers have optimized the process parameters and established a correlation between the parameters and the behavior of the material under investigation. Others performed optimization research regarding some specific areas of IM viz. cooling channel design, location of the gate, runner system design etc. Some of the studies were focused on monitoring control system to monitor the process during production to ensure quality and minimize the defects. Researchers have used many different techniques to model and optimize the IM manufacturing process. These techniques include mathematical modelling, simulation, design of experiments (DOE) and artificial intelligence (AI). Some of the examples of these techniques are Taguchi method, evolutionary algorithm (EA), artificial neural networks (ANN), genetic algorithms (GA), fuzzy logic, response surface methodology (RSM), particle swarm optimization (PSO), analysis of variance (ANOVA) etc. This paper aims to review and present the various studies concerning the process parameters and their influence over the quality of product. Throughout this review, the process parameters, response, material used and the technique applied to optimization research in the field of IM are highlighted.

PurposeThe study aims to analyze the barriers in the adoption of Industry 4.0 (I4.0) practices in terms of prioritization, cluster formation and clustering of empirical responses, and then narrowing them with identification of the most influential barriers for further managerial implications in the adoption of I4.0 practices by developing an enhanced understanding of I4.0.Design/methodology/approachFor the survey-based empirical research, barriers to I.40 are synthesized from the review of relevant literature and further discussions with academician and industry persons. Three widely acclaimed statistical techniques, viz. principal component analysis (PCA), fuzzy analytical hierarchical process (fuzzy AHP) and K-means clustering are applied.FindingsThe novel integrated approach shows that lack of transparent cost-benefit analysis with clear comprehension about benefits is the major barrier for the adoption of I4.0, followed by “IT infrastructure,” “Missing standards,” “Lack of properly skilled manpower,” “Fitness of present machines/equipment in the new regime” and “Concern to data security” which are other prominent barriers in adoption of I4.0 practices. The availability of funds, transparent cost-benefit analysis and clear comprehension about benefits will motivate the business owners to adopt it, overcoming the other barriers.Research limitations/implicationsThe present study brings out the new fundamental insights from the barriers to I4.0. The new insights developed here will be helpful for managers and policymakers to understand the concept and barriers hindering its smooth implementation. The factors identified are the major thrust areas for a manager to focus on for the smooth implementation of I4.0 practices. The removal of these barriers will act as a booster in the way of implementing I4.0. Real-world testing of findings is not available yet, and this will be the new direction for further research.Practical implicationsThe new production paradigm is highly complex and evolving. The study will act as a handy tool for the implementing manager for what to push first and what to push later while implementing the I4.0 practices. It will also empower a manager to assess the implementation capabilities of the industry in advance.Originality/valuePCA, fuzzy AHP and K means are deployed for identifying the significant barriers to I4.0 first time. The paper is the result of the original conceptual work of integrating the three techniques in the domain of prioritizing and narrowing the barriers from 16 to 6.

PurposeIn the present study, the thermal performance of engine radiator using conventional coolant and nanofluid is determined experimentally for the different flow rates. Further, the study implemented the Integrated Taguchi-GRA-PCA for optimising the heat transfer performance.Design/methodology/approachNanofluids were prepared by taking ethylene glycol and water (25:75 by volume) with volume fraction of 0.01, 0.03 and 0.05% of TiO2 nanopowder. Experimental Data were collected based on the design of experiments (DOE) L9 orthogonal array using Taguchi method. Statistical analysis via Grey relation analysis (GRA) and principal component analysis (PCA) were done to determine the role of experimental parameters on heat transfer coefficient and rate of heat transfer. Impact of three control factors, vol. % of TiO2 concentration (φ), flow rate (LPH), and sonication time (min) on the performance characteristics on heat transfer coefficient and ratio of heat transfer rate is analysed to get the best combination of the parameters involved.FindingsAnalysis revealed the importance of parameters on heat transfer coefficient and can be sorted in terms of contributions from higher to lower degree. Finally, ANOVA test has been conducted to validate the effect of process parameters. The major controllable parameter is φ (concentration), contributing about 32.74%, then flow rate contributing 32.5% and finally sonication time showing small contribution of 18.57%.Originality/valueA grey relational analysis integrated with principal component analyses (PCA) are implemented to get the optimum heat transfer coefficient and ratio of heat transfer rate. The novelty of the work is to adopt and implement the Integrated Taguchi-GRA-PCA first time for the purpose of thermal performance analysis of engine nano-coolant for radiator.

This paper presents a novel concept to enhance the reconfigurability issues (i.e. convertibility, flexibility, etc.) of a small-scale reconfigurable machine tool (RMT) by combining multiple modules (both basic and auxiliary parts) and manufacturing operations on a single workstation. The proposed reconfigurable machine will reduce the overall space, transport time, ramp up time, reconfiguration time and interim storage space. Further, various characteristics of reconfigurability (such as scalability; convertibility, flexibility, etc.) and enablers of the machine tool have been discussed. The proposed design is illustrated with a figure, which is designed on a 3D design tool (solid-works software platform).This machine tool will perform six major cutting and finishing operations, namely, hacksaw cutting, milling, drilling, grinding, buffing and lathe operations on a single work platform.

This paper aims at developing a road map to assist decision-makers to facilitate the successful implementation of the integrated Just-in-Time (JIT)-lean practices for improved performance of the In...

This work investigates the problem of optimal placement and geometry of piezoelectric sensors and actuators (S/A) to improve active vibration control performance of smart laminated beam. Theoretica...

Short life cycle of the products, large product variants and unanticipated changes in customer demand in a dynamic social context pose a significant challenge for design of an appropriate manufacturing system. Reconfigurable machine tool, which is the foundation block for reconfigurable manufacturing system is envisaged as a solution that caters to the current market forces. This paper discusses the reconfiguration mechanisms, which enable transformation of conventional machine tools, such as, drilling, lathe and milling machines into reconfigurable machines that will achieve the objectives. It is expected that, this work will help designers and practicing engineers by making them aware of the reconfiguration mechanisms, which have become a necessity for the very survival of the manufacturing sector by lowering the overall capital costs and, therefore, the per unit fixed costs.

Purpose Recent years have witnessed a significant rise in Indian healthcare establishments (HCEs) which indicate that there is a constant need to improve the healthcare quality services through the adoption and implementation of TQM enablers. The purpose of this paper is to identify such enablers and then propose a ranking model for TQM implementation in Indian HCEs for improved performance. Design/methodology/approach The study identifies 20 TQM enablers through comprehensive literature survey and expert’s opinion, and classifies them into five main categories. The prominence of these enablers is established using a recently developed novel multi-criteria decision making (MCDM) method, i.e. best-worst method (BWM). The importance of the various main category and sub-category enablers is decided on the basis of their weights which are determined by the BWM. In comparison to other MCDM methods, such as analytical hierarchy process, BWM requires relatively lesser comparison data and also provides consistent comparisons which results in both optimal and reliable weights of the enablers considered in this paper. Further, a sensitivity analysis is also carried out to ensure that the ranking (based on the optimal weights) of the various enablers is reliable and robust. Findings The results of this study reveal that out of five main category enablers, the “leadership-based enablers (E1)” and the “continuous improvement based enablers (E5)” are the most and the least important enablers, respectively. Similarly, among the 20 sub-category enablers, “quality leadership and role of physicians (E14)” and “performing regular survey of customer satisfaction and quality audit (E52)” are the most and the least dominating sub-category enablers, respectively. Research limitations/implications This study does not explore the interrelationship between the various TQM enablers and also does not evaluate performance of the various HCEs based on the weights of the enablers. Practical implications The priority of the TQM enablers determined in this paper enables decision makers to understand their influence on successful implementation of the TQM principles and policies in HCEs leading to an overall improvement in the system’s performance. Originality/value This study identifies the various TQM enablers in HCEs and categorizes them into five main categories and ranks them using the BWM. The findings of this research are quite useful for management of the HCEs to properly understand the relative importance of these enablers so that managers can formulate an effective and efficient strategy for their easy and smooth implementation which is necessary for continuous improvement.

PurposeThe purpose of this paper is to present the multi-objective optimization of the dynamic response of isotropic and laminated composite folded plates. The dynamic analysis has been carried out using the finite element method based on the first-order shear deformation theory.Design/methodology/approachHamilton’s principle has been employed for the derivation of the governing equations. Natural frequencies are obtained using the eigenvalue extraction method. The optimal combination of the crank angle, lamination scheme and boundary conditions on the natural frequencies of folded plates for their safe and optimal dynamic design has been obtained. The analysis has been carried out using finite element approach based on FSDT to obtain the dynamic equation of single- and double-fold laminated plates. In total, 15 experiments as per Taguchi’s standardL15orthogonal array have been performed. Further, standard deviation (SD) based TOPSIS method is used to perform multi-response optimization of folded plates in order to rank the combination of the input parameters.FindingsSD integrated with TOPSIS reveals that Experiment No. 8 (crank angle=90° and anti-symmetric lamination scheme=0°/90°/0°/90°), Experiment No. 14 (crank angle=150° and anti-symmetric lamination scheme=0o/90o/0o/90o), Experiment No. 2 (crank angle=30° and anti-symmetric lamination scheme=0°/90°/0°/90°) and Experiment No. 3 (crank angle=30° and symmetric lamination scheme=0°/90°/0°/90°) occupy rank 1 for one fold, one end clamped, one fold, two ends clamped, two folds, one end clamped and two folds, two ends clamped conditions, respectively, in order to maximize the modal response corresponding to the fundamental mode.Originality/valueSD-based technique for order of preference by similarity to ideal solution (TOPSIS) method is used to rank the process parameters. The optimum combination of the input parameters on the multi-response optimization of dynamics of the folded plates has also been evaluated using the analysis of mean (ANOM).

The traditional structure of machines (such as lathe, milling, shaper, slotter, drilling and planer) has become questionable because of the modular concepts (such as modularity, scalability, convertibility, mobility and flexibility) and reconfiguration becomes a promising approach towards modular manufacturing machines, in which manufacturing techniques are independent of changes. In this area, reconfigurable machine tool (RMT) forms a new class of modular machines in current manufacturing scenario where the manufacturing industry put a strong pressure on good quality and price reduction. The capabilities of the machines tool and manufacturing systems in reconfigurable manufacturing system (RMS) change with each reconfiguration (both software and hardware modules). In this case, an approach is presented for reconfiguration of horizontal shaper machine for developing the modular shaper cum slotter machine in a manufacturing system by adding some auxiliary (like Scott Russel mechanism) and some basic modules and the reconfiguration features of traditional shaper and slotter machine are also discussed. The proposed approach is illustrated with a figure, which has been designed on 3-D design tool (solid-works software platform). It is expected that, this work will help designers and practising engineers by making them aware of the reconfiguration mechanisms on traditional shaper machine, which have become a necessity for the very survival of manufacturing by lowering the operational costs.

The Micro, Small and Medium Enterprises engaged in manufacturing activities are facing various environmental challenges encompassing material and energy consumption; waste generation and disposal; water and air pollution; etc. Hence, it becomes important to adopt policies, strategies, and technologies, which may help to bring down the adverse impacts of manufacturing on the environment. In this context, the current study identified the 15 critical environmental sustainability indicators to gauge the impact of their manufacturing activities on the environment taking a case of lock manufacturing industries. To understand the interdependence among the selected indicators, the study further utilizes an integrated DEMATEL-MMDE-ISM approach to analyse the inputs of industry professionals. The results of the study highlighted that design of products, which can be disassembled, reused, or recycled and are free from hazardous materials play a significant role in enhancing the environmental sustainability of the concerned industry. The study is expected to aid decision-makers (industry practitioners and academic researchers) to identify strategic areas in order to achieve higher environmental sustainability in manufacturing organizations.

The Micro, Small and Medium Enterprises (MSMEs) face numerous environmental challenges encompassing resource and energy conservation; waste generation and disposal; water and air pollution; and so on. As a result, it becomes critical to implement policies, strategies, and technologies that can help in reducing the adverse impacts of manufacturing activities on the environment. In this context, the current study identified 15 critical environmental sustainability indicators to assess the impact of manufacturing activities on the environment by taking a case study of lock manufacturing MSMEs. To understand the interdependence among the selected indicators, the study further utilizes an integrated DEMATEL-MMDE-ISM approach to analyze the inputs of industry professionals. The results of the study highlighted that green product design, which facilitates the product to be disassembled, reused, or recycled and are free from hazardous materials, plays a significant role in enhancing the environmental sustainability of the concerned industry. Green product design significantly affects 12 other indicators out of 15 under consideration; thus, incorporating green practices in the design and development of a product leads to significant improvement in environmental sustainability. The study is expected to aid decision-makers (industry practitioners and academic researchers) to identify strategic areas in order to achieve higher environmental sustainability in manufacturing organizations.

Hearing sense is one of the key elements which can have severe impact on the driver’s task quality. A cross-sectional study based on questionnaire has been studied. Determination effect of prolonged exposure to noise was the primary aim of this study. For the study, various bus drivers concerned in private universities and public transports with job temporal order of 8 h were selected from Faridabad town in Haryana (India). Subjects were classified into two groups based on working experience. Subsamples of 50 drivers having experience of 10 years and over 10 years above were taken from the population. Subjects were asked ordered questions, and also the responses were collected on a five-point scale. Subsequently, result revealed that 55% of the drivers had sound knowledge about the hazardous effect of continuous exposure of noise on their health. Also, 35% drivers agree that they had the hearing disability due to prolonged exposure to noise. A paired sample t-test is performed to reach a conclusion. It was evidently proved that the drivers having an experience more than 10 years were subjected to greater risk in comparison to drivers comprising less than 10 years of experience.

Manufacturing companies have since been continuously facing volatile market conditions, primarily due to rapidly changing customers demand and quick introduction of new products. Therefore, to stay competitive in globally ever-changing manufacturing environment, manufacturing sectors need to adopt and implement those manufacturing systems which are capable to keep pace with the changes occurring and this is why a new concept, namely “reconfigurable manufacturing system (RMS)”, has come into being. The manufacturing organizations have to meet the requirements of RMS by developing a suitable system which keeps their entity globally viable. The main objective of this paper is to identify and analyse the factors which directly or indirectly influence reconfigurable manufacturing system. In this present work, local and global weights have been calculated to measure the global ranking of all the possible RMS factors (e.g. modularity, scalability, integrability, flexibility, convertibility and diagnosability) based on which the best and worst alternatives are selected right at the design stage of RMS. All these factors have been mapped together through multi-criteria decision-making approach known as best–worst method (BWM). Finally, sensitivity analysis has been done to validate the proposed results. The proposed multi-decision approach is quite versatile from the point of view that it provides an opportunity to integrate all possible factors and sub-factors which could impact manufacturing processes.

Solar photovoltaic (SPV) systems are widely used as a renewable energy source to produce electricity to endusers. SPV system convert free and unlimited sunlight into electricity without carbon dioxide emission or any other air pollutants. The stochastic nature of SPV system may be responsible for degradation of performance in terms of availability, production, etc. Therefore, the assessment of the performance measures like reliability and quality of its components is vital concern that may open the new vista of research. There are various reliability assessment techniques used in conventional (thermal power plant, hydropower plant, nuclear power plant, etc.) and non-conventional power system (SPV power plant, wind power plant, etc.) like Fault Tree Analysis (FTA), Failure Mode and Effect Analysis (FMEA), etc. FTA is one of the powerful reliability assessment techniques, which gives modes of the failure and its consequences, that are demonstrated on SPV systems. FTA can further be broadly classified into two: qualitative and quantitative analysis. Qualitative FTA can be solved by determining a minimal cut set (MCS) and quantitative FTA can be solved by calculating the failure probability of each the basic event. In conventional FTA failure probability of each event must be known, to assess for quantitative FTA. However, to quantify probabilistic failure, data of all basic events seem to be inadequate. To overcome this problem, conventional FTA may be integrated with fuzzy linguistic scale method. In this research work, failure possibilities in terms of a linguistic variable of unknown failure data for all basic events, which lead the Top Events for SPV systems are assigned by the experts from academics, research, industries and practicing engineers. For Aggregated fuzzy number based on expert’s opinion, a consistency agreements method is used. Fussell-Vesely (F-V) importance measures is also implemented to rank the basic events and MCS to apportion the most critical event.

Purpose Proposing an improved model for evaluating criticality of non-value added (waste) in operation is necessary for realizing sustainable manufacturing practices. The purpose of this paper is concerning on improvement of the decision support model for evaluating risk criticality lean waste occurrence by considering the weight of modified FMEA indices and the influence of waste-worsening factors causing the escalation of waste risk magnitude. Design/methodology/approach Integration of entropy and Taguchi loss function into decision support model of modified FMEA is presented to rectify the limitation of previous risk reprioritization models in modified FMEA studies. The weight of the probability components and loss components are quantified using entropy. A case study from industry is used to test the applicability of the integration model in practical situation. Findings The proposed model enables to overcome the limitations of using subjective determination on the weight of modified FMEA indices. The inclusion of the waste-worsening factors and Taguchi loss functions enables the FMEA team to articulate the severity level of waste consequences appropriately over the use of ordinal scale in ranking the risk of lean waste in modified FMEA references. Research limitations/implications When appraising the risk of lean waste criticality, ignorance on weighting of FMEA indices may be inappropriate for an accurate risk-based decision-making. This paper provides insights to scholars and practitioners and others concerned with the lean operation to understand the significance of considering the impact of FMEA indices and waste-worsening factors in evaluating criticality of lean waste risks. Practical implications The method adopted is for quantifying the criticality of lean waste and inclusion of weighting of FMEA indices in modified FMEA provides insight and exemplar on tackling the risk of lean waste and determining the most critical waste affecting performability of company operations. Originality/value Integration of the entropy and Taguchi loss function for appraising the criticality of lean waste in modified FMEA is the first in the lean management discipline. These findings will be highly useful for professionals wishing to implement the lean waste reduction strategy.