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Today, proficient practices are required to stimulate along various boundaries of the supply chain (SC) to exploit manufacturing resources economically, effectually and gracefully for retaining operational excellence. Accordingly, varieties of paramount practices, i.e. Lean, Agile, Resilient and Green practices, are integrated in present study with the objective to develop a Decision Support Framework (DSF) to select robust supplier under the extent of Lean-Agile-Resilient-Green (LARG) practices for a manufacturing firm. The framework is developed and validated in the Indian automotive sector, where the primary data is collected based on perceptions of the respondents working in an automotive company.

LARG metrics can ponder ecological balance, customer satisfaction, associations, effectiveness and sustainability and thus, the study consolidated LARG practices in one umbrella to develop a DSF. The analytical approach under DSF is developed by the integration AHP, DEMATEL, ANP, Extended MOORA and SAW techniques in present study to evaluate a robust supplier under the aegis of LARG practices in SC. DSF is developed by scrutinizing and categorizing LARG characteristics, where the selected LARG characteristics are handled by fuzzy sets theory to deal with the impreciseness and uncertainty in decision making.

The study has identified 63 measures (15 for Lean, 15 for Agile, 14 for resilient and 19 for Green) to support the robust supplier selection process for manufacturing firms. The findings of study explicate “Internal communication agility”, “Interchangeability to personnel resources”, “Manufacturing flexibility”, “degree of online solution”, “Quickness to resource up-gradation”, “Manageability to demand and supply change”, “Overstocking inventory practices” as significant metrics in ranking order. Additionally, “Transparency to share information”, “Internal communication agility”, “Manufacturing Flexibility”, “Green product (outgoing)” are found as influential metrics under LARG practices respectively.

A technical DSF to utilize by the managers is developed, which is connected with knowledge-based theory and a case of an automobile manufacturing firm is presented to illustrate its implementation. The companies can utilize presented DSF to impose service excellence, societal performance, agility and green surroundings in SC for achieving sustainable outcomes to be welcomed by the legislations, society and rivals. The framework represents an important decision support tool to enable managers to overcome imprecise SC information sources.

The study presented a proficient platform to review the most significant LARG alternative in the SC. The study suggested a cluster of LARG metrics to support operational improvement in manufacturing firms for shifting gear toward sustainable SC practices. The present study embraces its existence in enrolling a high extent of collaboration amongst clients, project teams and LARG practices to virtually eradicate the likelihood of absolute project failure.

This paper aims to provide a method and decision support tool to enhance swift reconfiguration of Plug&Produce (P&P) systems in the presence of continuously changing production orders.

The paper reviews different production scenarios and system design and configuration methods and more particularly specifies the need of decision support tools for P&P systems that integrate configuration and planning activities. This problem is then addressed by proposing a method that helps reduce the solution space of the reconfiguration problem and allows the timely selection of the most promising reconfiguration alternative.

The proposed method was found to be helpful in reducing the reconfiguration alternatives that need to be considered and in selecting the most promising one for different orders. The advantages and limitations of this method are identified, and an illustrative test case of the approach is presented, corroborating the method applicability in the absence of large queues in the system.

This paper addresses a less explored domain within the P&P systems research field, which is the system reconfiguration. It proposed a method to support system validation and reconfiguration jointly with an illustrative test case. This represents an original contribution to the P&P research field, and it can have impact in improving agility and decreasing the complexity of reconfiguration activities to cope with constantly changing production orders.

The purpose of this paper is to encourage discussion about the potential role of metrics in research assessment.

The paper considers the results of the UK’s 2014 Research Excellence Framework, and the potential of metrics to reduce the size of future exercises.

The paper suggests that a battery of non-robust metrics selected by the higher education institutions could support a greatly reduced peer-reviewed exercise.

Policy makers should reconsider the role of metrics in research assessment.

The paper provides an alternative vision of the role of metrics in research assessment, and will be of interest to policy makers, bibliometricians, and anyone interested in HEI research assessment.

In this paper, the four popular multiple-criteria decision-making (MCDM) methods in fuzzy environment are utilized to reflect the vagueness and uncertainty on the judgments of decision-makers (DMs), because the crisp pairwise comparison in these conventional MCDM methods seems to be insufficient and imprecise to capture the right judgments of DMs. Of these methods, as Fuzzy analytic hierarchy process (F-AHP) is used to calculate criteria weights, the other methods; Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS), Fuzzy Grey relational analysis (F-GRA) and Fuzzy Preference Ranking Organization METhod for Enrichment of Evaluations (F- PROMETHEE II) are used to rank alternatives in the three different ways for a comparative study.

The demand for green products has dramatically increased because the importance and public awareness of the preservation of natural environment was taken into consideration much more in the last two decades. As a result of this, especially manufacturing companies have been forced to design more green products, resulting in a problem of how they incorporate environmental issues into their design and evaluate concept options. The need for the practical decision-making tools to address this problem is rapidly evolving since the problem turns into an MCDM problem in the presence of a set of green concept alternatives and criteria.

The incorporation of fuzzy set theory into these methods is discussed on a real-life case study, and a comparative analysis is done by using its numerical results in which the three fuzzy-based methods reveal the same outcomes (or rankings), while F-GRA requires less computational steps. Moreover, more detailed analyses on the numerical results of the case study are completed on the normalization methods, distance metrics, aggregation functions, defuzzification methods and other issues.

The designing and manufacturing environmental-friendly products in a product design process has been a vital issue for many companies which take care of reflecting environmental issues into their product design and meeting standards of recent green guidelines. These companies have utilized these guidelines by following special procedures at the design phase. Along the design process consisting of various steps, the environmental issues have been considered an important factor in the end-of-life of products since it can reduce the impact on the nature. In the stage of developing a new product with the aim of environmental-friendly design, the green thinking should be incorporated as early as possible in the process.

The case study was inspired from the previous work of the author, which was realized in a hot runner systems manufacturer, used in injection molding systems in a Canada. In a new product development process, the back- and front-ends of development efforts mainly determine the following criteria: cost, risk, quality and green used in this paper. The case study showed that the three fuzzy MCDM methods come to the same ranking outcomes. F-GRA has a better time complexity compared to the other two methods and uses a smaller number of computational steps. Moreover, a comparative analysis of the three F-MCDM methods; F-PROMETHEE II, F-TOPSIS and F-GRA used in ranking for green concept alternatives using the numerical results of the case study. For the case study; as seen in table 20, the three F-MCDM methods produced the numerical results on the rankings of the green concept alternatives as follows; {Concept A-Concept C–Concept B–Concept D}.

Inclusion of environmental-related criteria into concept selection problem has been gaining increasing importance in the last decade. Therefore, to facilitate necessary calculations in applying each method especially with its fuzzy extension, it can be developed a knowledge-based (KB) or an expert system (ES) to help the DMs make the required calculations of each method, and interpret its results with detailed analysis.

The objective of the research was to propose a F-AHP based F-MCDM approach to green concept selection problem through F-PROMETHEE II, F-TOPSIS and F-GRA methods. As the F-AHP is used to weight evaluation criteria, the other methods are respectively used for ranking the concept alternatives and determine the best concept alternative.

The introduction of “altmetrics” as new tools to analyze scientific impact within the reward system of science has challenged the hegemony of citations as the predominant source for measuring scientific impact. Mendeley readership has been identified as one of the most important altmetric sources, with several features that are similar to citations. The purpose of this paper is to perform an in-depth analysis of the differences and similarities between the distributions of Mendeley readership and citations across fields.

The authors analyze two issues by using in each case a common analytical framework for both metrics: the shape of the distributions of readership and citations, and the field normalization problem generated by differences in citation and readership practices across fields. In the first issue the authors use the characteristic scores and scales method, and in the second the measurement framework introduced in Crespo et al. (2013).

There are three main results. First, the citations and Mendeley readership distributions exhibit a strikingly similar degree of skewness in all fields. Second, the results on “exchange rates (ERs)” for Mendeley readership empirically supports the possibility of comparing readership counts across fields, as well as the field normalization of readership distributions using ERs as normalization factors. Third, field normalization using field mean readerships as normalization factors leads to comparably good results.

These findings open up challenging new questions, particularly regarding the possibility of obtaining conflicting results from field normalized citation and Mendeley readership indicators; this suggests the need for better determining the role of the two metrics in capturing scientific recognition.

Workforce analytics is an evolving measurement approach in human resource (HR) planning and strategy implementation. Workforce analytics can help organizations manage one of their most important resources: their human capital. The purpose of this paper is to propose a diversity metric, called the D-Metric, as a new tool for HR planning. The D-Metric can be used to assess the demographic representativeness of employees across skill categories of an organization’s workforce compared to its relevant labor markets.

The authors present a real example and discuss possible applications of the D-Metric in HRM strategic planning and diversity research.

The D-Metric is a statistic useful in assessing demographic representativeness in the occupational categories of an organization’s workforce compared to the demographics of its relevant labor markets. The methodology could be implemented to assess an organization’s work force representativeness on dimensions such as race, sex, age and pay levels. When the labor market is unitary, without measurable variance, a substitute metric, the U-Metric also presented in this paper, can be used.

Use of the D-Metric requires publicly available labor market data with variance across labor market segments.

There currently is no published metric that evaluates the representativeness of an organization’s work force relative to its relevant labor markets. Many organizations seek a demographically representative workforce to better understand their diverse customer segments. Monitoring the representativeness of an organization’s work force, as captured in Equal Employment Opportunity (EEO-1) forms in the USA, for example, is an important component of HR management strategy. From a legal perspective, the D-Metric or the alternative U-Metric, could be useful in showing progress toward a demographically representative work force.

This paper aims to look into contemporary thinking within the brand equity paradigm, with a view to establishing avenues for further research on the drivers of brand equity formation, enabling a more in-depth understanding of the antecedents of brand equity and its determinants, as well as the development of an improved instrument to measure brand equity. The brand equity paradigm and its importance for marketing theory have been in the research focus for more than two decades. There is no agreement in the literature how to develop a unique measure of brand equity, neither what are the sources, drivers or determinants.

The authors develop the relating conceptual study through the differentiation and integration as specific conceptual goals. The authors present a taxonomic framework of brand equity grounded on a synthesis of contemporary approaches to the theme.

The authors identify gaps in the brand equity literature. The analysis and development of the conceptual study in this paper shall serve as beacons for future research and provide valuable theoretical insights on the determinants of brand equity formation and the development of better brand equity measurement tools.

The authors synthesized contemporary approaches in the field, identified research gaps and proposed open questions that should be tackled, as well as provided avenues for future research. The authors argue that creation of a unifying brand equity theory should be based on three pillars: stakeholder value, marketing assets and brand financial performance outputs.

The purpose of this paper is to examine the relationship between electronic journal downloads and citations and whether online electronic resource usage can be adopted as an alternative to citation for evaluation of scholarly discourse.

A consolidated 16 publishers’ COUNTER usage data of UGC-Infonet members was collected from INFLIBNET Centre. The usage was meticulously filtered from UGC-subscribed journals and institutional subscriptions. The quantitative data were analysed to establish the relationship between download, impact factor (IF) and price. Multiple regression analysis was used to assess the influence of price and IF on usage and to predict the usage when they are known and the threshold for significance was set at p < 0.05.

There exists a relationship between IF and downloads of journals in UGC-Infonet. Journal IF and price significantly influence usage, where journal IF plays an important role in the intensity of the use. Also, the top 25 hottest downloaded papers were journals with IF; hence, no journal without IF featured in the top 25 most downloaded journals in the consortia. The relationship between the top 25 IF journals in the consortia and download is strong (r = 0.368537).

The only account that reports on the relationship between journal IFs and downloads for UGC-Infonet consortia. Also, the influence of usage behaviour with respect to citation and price of a journal.