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The purpose of this paper is to investigate the effect of four controllable parameters (fuel mixture, evaporation bubble point temperature, expander inlet temperature and condensation dew point temperature) of a solar-driven organic Rankine cycle (ORC) on the first-law efficiency, the exergetic efficiency, the exergy destruction and the volume flow ratio (expander outlet/expander inlet).

Nine experiments as per Taguchi’s standard L9 orthogonal array were performed on the solar-driven ORC. Subsequently, multi-response optimization was performed using grey relational and principal component analyses.

The results revealed that the grey relational analysis along with the principal component analysis is a simple as well as effective method for solving the multi-response optimization problem and it provides the optimal combination of the solar-driven ORC parameters. Further, the analysis of variance was also employed to identify the most significant parameter based on the percentage of contribution of each cyclic parameter. Confirmation tests were performed to check the validity of the results which revealed good agreement between predicted and experimental values of the response variables at optimum combination of the input parameters. The optimal combination of process parameters is the set with A3 (the best fuel mixture in the context of optimal performance is 0.9 percent butane and 0.1 percent pentane by weight), B2 (evaporation bubble point temperature=358 K), C1 (condensation dew point temperature=300 K) and D3 (expander inlet temperature=370 K).

In this research, the Taguchi-based grey relational analysis coupled with the principal components analysis has been successfully carried out, whereas for any optimized solution, it is required to have a real-time scenario that may be taken into consideration by the application of different soft computing techniques like genetic algorithm, simulated annealing, etc. The results generated are purely based on theoretical modeling, and, for further research, experimental analyses are required to consolidate the generated results.

This piece of research work will be helpful to users of solar energy, academicians, researchers and other concerned persons, in understanding the importance, severity and benefits obtained by the application, implementation and optimization of the cyclic parameters of the solar-driven ORC.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The 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.

Hamilton’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 standard L₁₅ orthogonal 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.

SD 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=0^o/90^o/0^o/90^o), 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.

SD-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).

In 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.

Nanofluids 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.

Analysis 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%.

A 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.

Automobile 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.

An 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.

The 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.

The 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.

A classifier technique is one of the important tools which may be used to classify the data or information into systematic manner based on certain criteria pertaining to get the accurate statistical information for decision making. It plays a vital role in the various applications, such as business organization, e-commerce, health care, scientific and engineering application. The purpose of this paper is to examine the performance of different classification techniques in lift index (LI) data classification.

The analyses consist of two stages. First, the random data are generated for lifting task through computer programming, which is then put into the National Institute for Occupational Safety and Health equation for LI estimation. Based on the evaluated index, the task may be classified into two groups, i.e. high-risk and low-risk task. The classified task is considered to analyze the performance of different tools like Artificial Neural Network (ANN), discriminant analysis (DA) and support vector machines (SVMs).

The work clearly demonstrates the accuracy and computational ability of ANN, DA and SVM for data classification problems in general and LI data in particular. From the research it may be concluded that SVM may outperform ANN and DA.

The research is limited to a particular kind of data that may be further explored by selecting the different controllable parameters and model specification. The study can also be applied to realistic problem of manual loading. It is expected that this will help researchers, designers and practicing engineers by making them aware of the performance of classification techniques in this area.

The objective of this research work is to assess and compare the relative performance of some well-known classification techniques like DA, ANN and SVM, which suggest that data characteristics considerably impact the classification performance of the methods.

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.

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.

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.

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.

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.

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.

Evaluation of the extent of transformability of an existing system into an industry 4.0 (I4.0) compatible system is indispensable for both the technical and economic planning for implementing I4.0. This paper aims to propose a procedure to evaluate the transformability of an existing manufacturing system into an I4.0 system.

Six significant components of a manufacturing system and their five levels of modifications essential for the decision of transformation are identified. Based on expert opinion on facilitation and the impact of the transformation of one component on the transformation of others, a graph theory-based procedure for estimation of transformability index (TI) along with its relative and threshold values is proposed.

The paper introduced the concept of transformability into manufacturing systems. It proposed a simple procedure for calculating the ideal, relative and threshold value for TI to assess the suitability of the up-gradation of any manufacturing system into the I4.0 system.

Though the proposed procedure is based on six system components and their five levels of facilitation, it is quite versatile and able to integrate new components and different facilitation levels according to system requirements for their impact analysis in the transformation process. It can be extended to other domains like services and health care. Further, it can be used to estimate and establish the transformability criteria of a factory/service unit/industry from its current state to any regime.

The proposed method for deducing the TI, relative transformability index (RTI) and their threshold values would be a handy tool for decision-makers to assess the upgrading suitability of the entire manufacturing system and its component for use in the new regime or scrapping. It would provide mathematical and scientific support to the transformability decisions by assessing the influence of transforming one component to others and the system. This study would pave the way for further explorations in the domain of transformability.

In the light of available literature and best of the author’s knowledge, this study is the first of its kind that has applied the concept of transformability of existing manufacturing systems toward I4.0 compatible systems and proposed a procedure to estimate TI, RTI and their threshold values.

The 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.

For 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.

The 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.

The 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.

The 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.

PCA, 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.