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The purpose of this paper is to assess the perceptions of safety officers on Occupational Health and Safety (OHS) norms extended to the workforce in Indian industries, understand implementation levels and find out deficiencies existing therein.

In this study, the authors utilized factor analysis to develop an instrument specifically responsible for assessing OHS norms in three major industrial sectors through a broad‐based questionnaire survey. The pattern of influence of input parameters on outputs such as injury level and material damage is difficult to establish, possibly due to existence of some nonlinear relationship among them. Therefore, a neural network approach is adopted to carry out sensitivity analysis and identify important deficient items.

Exploratory factor analysis has been carried out on the responses to the designed questionnaire. In total, nine factors with 23 items have been extracted and interpreted. As neural networks are capable of mimicking human cognitive process, the perceptions mechanism of safety officers can be easily modeled via neural networks. Sector‐wise deficient items have been identified and strategies for their improvement have been proposed.

The major limitation may be the number of industrial sectors considered in the study. Although the proposed model is quite generic, its performance needs to be tested with other categories of industries.

Although perceptions of safety officers on their immediate work environment help to formulate constructive safety policy and procedures, involvement of a few representatives from the workforce during the implementation level may assist to substantially reduce injury level and material damage, since the workers are more conversant with work practices, are exposed to risk environments and can sustain injuries if accidents occur.

The paper uses advanced statistical and intelligent techniques for assessment of OHS practices. A comparative evaluation of present practices among three major types of Indian industry has been made. Further, the paper proposes an OHS instrument for Indian industry. The paper offers new directions for researchers to devise a comprehensive methodology that aims at reducing occupational health risks.

The purpose of this paper is to develop a comprehensive framework to identify and classify key medical tourism enablers (MTEs) and to study the direct and indirect effects of each enabler on the growth of medical tourism in India.

In this paper, an integrated approach using interpretive structural modeling (ISM) and Fuzzy Matrice d'Impacts Croisés Multiplication Appliquée á un Classement (FMICMAC) analysis has been developed to identify and classify the key MTEs, typically identified by a comprehensive review of literature and expert opinion. The key enablers are also modeled to find their role and mutual influence.

The key finding of this modeling helps to identify and classify the enablers which may be useful for medical tourism decision makers to employ this model for formulating strategies in order to overcome challenges and to become a preferred medical tourism destination. Integrated model reveals enablers such as medicine insurance coverage, international healthcare collaboration, and efficient information system as dependent enablers. No enabler is found to be autonomous enablers. The important enablers like healthcare infrastructure facilities and global competition are found as the linkage enablers. Research in medicine and pharmaceutical science, medical tourism market, transplantation law, top management commitment, national healthcare policy, competent medical and para-medical staffs are found as the independent enablers. Integrated model also establishes the direct and indirect relationship among various enablers.

The research provides an integrated model using ISM and FMICMAC to identify and classify various key enablers of medical tourism in India. In conventional cross-impact matrix multiplication applied to classification analysis, binary relationship of various enablers is considered. FMICMAC analysis helps to establish possibility of relationship among various enablers so that low-key hidden factors can be identified. The low-key hidden factors may initially exhibit marginal influence but they may show significant influence later on during analysis. The uncertainty and fuzziness of relationship among various enablers can be conveniently handled by FMICMAC and expert opinions can easily be captured. This research will help medical tourism decision makers to select right enablers for the growth of medical tourism in India.

Success of software projects depends on identification of project risks and managing the risks in a proactive manner. Risk management requires thorough insights into interrelationship of various risk factors for proposing strategies to minimize failure rate. The purpose of this paper is to develop a comprehensive structural model to interrelate important risk factors affecting the success of software projects.

Specifically, this study reveals how interpretive structural modelling helps the risk managers in identifying and understanding the interrelationship among various risk factors. A total of 23 risk factors (or risk sources) have been identified through an extensive literature review.

Necessary modelling information has been gathered from expert through a structured questionnaire survey. Matrice d’Impacts croises-multipication appliqué an classment analysis has been employed to classify the risk factors into four clusters such as autonomous, dependent, linkage and independent based on their driving and dependence power. Risk factors with strong dependence and weak driving power need urgent attention from managerial perspective.

The proposed model is useful for software managers/practitioners to address risk factors associated with complicated projects.

In today's competitive global marketplace, performance management has been identified as a key strategic consideration towards achieving an efficient supply chain management. The task of estimating supply chain performance extent is seemed a complex problem entitled with multiple subjective performance measures and metrics; subjected to decision-making environment which involves an inherent vagueness, inconsistency and incompleteness associated with decision-makers (DMs) (expert panel) commitment towards assessment of various subjective (quantitative) evaluation indices. Consequently, it becomes difficult towards making a comparative study on performances of alternative supply chains. It is, therefore, indeed essential to conceptualize and develop an efficient appraisement platform helpful for benchmarking of alternative supply chains based on their performance extent. The paper aims to discuss these issues.

The work explores the concept of grey numbers combined with multi-objective optimization by ratio analysis (MOORA) in perceptive to evaluate best alternative from among available alternative supply chains.

The method has been found fruitful to facilitate such a multi-criteria group decision-making (MCGDM) problem under uncertain environment and provides an appropriate compromise ranking order with respect to available possible alternatives.

Supply chain performance appraisement provides necessary means by which an organization can assess whether its supply chain is performing well, whether it has been improved or degraded as compared to the past record. The purpose of this research is to develop and to empirically test a multiple-indices hierarchical appraisement model for benchmarking of supply chain performance and its impact on competitiveness of manufacturing industries.

This year, Benchmarking: An International Journal (BIJ) is celebrating its 25th anniversary. The purpose of this paper is to show the historical connections and professional impact of the journal over the years. This is accomplished through a detailed bibliometric analysis (BA) of its content from 1994 to 2018. This paper also identifies leading trends that have affected BIJ during this time frame.

In this paper, the authors employ BA on data retrieved from the Scopus database. The authors used various indicators such as the most productive authors, institutions and countries, as well as the most cited papers, citing journals and articles most cited by BIJ between 1994 and 2018. Additionally, the authors have used the Gephi, VOSviewer software to map journals, keywords and institutions with bibliographic coupling and co-citation analysis.

The journal has improved its impact, productivity and reputation in the period under review. Articles published in BIJ have been cited in other journals more than 15,000 times. The most prominent themes among the articles published in BIJ are benchmarking, supply chain management and performance improvement. Significant contributions have come from authors and institutions around the globe. Co-authorships, though global, have centered around India, the USA and the UK. There is also a thematic similarity between articles from India and Thailand, and the USA and Germany, indicating high academic collaboration or prevalence of similar issues.

This paper provides evidence of the impact of BIJ, and in so doing, it helps authors to decide on the appropriateness of this publication for their research. The reader will also understand leading trends occurring in BIJ over the history of the journal.

This is the first paper that traces the progress of BIJ covering a time span of 25 years.