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The purpose of this paper is to develop data envelopment analysis (DEA) models and algorithms for efficiency improvement when the inputs and output weights are restricted and there is fixed availability of inputs in the system.

Limitation on availability of inputs is represented in the form of constant sum of inputs (CSOI) constraint. The amount of excess input of an inefficient decision-making unit (DMU) is redistributed among other DMUs in such a way so that there is no reduction in their efficiency. DEA models have been developed to design the optimum strategy to reallocate the excess input.

The authors have developed the method for reallocating the excess input among DMUs while under CSOI constraint and parameter weight restrictions. It has been shown that in this work to improve the efficiency of an inefficient DMU one needs the cooperation of selected few DMUs. The working of the models and results have been shown through a case study on carbon dioxide emissions of 32 countries.

The limitation of the study is that only one DMU can expect to benefit from the application of these methods at any given time.

Results of the paper are useful in situations when decision maker is exploring the possibility of transferring the excess resources from underperforming DMUs to the other DMUs to improve the performance.

This strategy of reallocation of excess input will be very useful in situations when decision maker is exploring the possibility of transferring the excess resources from underperforming DMUs to the other DMUs to improve the performance. Unlike the existing works on efficiency improvement under CSOI, this work seeks to address the issue of efficiency improvement when the input/output parameter weights are also restricted.

The industrial revolution changed the market landscape significantly in all industrial sectors. It has a noteworthy impact on enhancing the quality of goods and services. The quality aspect is of utmost concern and determines the success or failure of any product. Therefore, the presented study analyses the key barriers and solutions of Quality 4.0.

Twenty barriers and fifteen solutions were identified using a literature review and investigated using a hybrid approach. Barrier weights were evaluated with the help of the fuzzy AHP method. Furthermore, the computed weights were used to perform computations in the next step using fuzzy-TOPSIS to prioritize the ranking of identified solutions.

The research results show that “Lack of applying advanced analytics to uncover Quality 4.0 initiatives” and “Lack of integrating data from various sources across the organization” are the topmost barriers. Furthermore, “Implement a leadership development program focused on Quality 4.0” and “Cross-departmental peer learning environment” are the topmost solutions.

Managers and industrialists can benefit from Quality 4.0 through improved decision-making, process efficiency, supply chain collaboration, agile quality management, enhanced customer experience and a culture of continuous improvement. This results in better quality, operational effectiveness and a competitive edge.

The solutions need to be mapped with barriers to adopting Quality 4.0. Furthermore, the research results involve novelty by prioritizing the solutions to overcome the anticipated barriers.

This paper aims to consolidate and review the literature in the field of market-calibrated option pricing analysis. By doing so, the paper brings out the gaps in the extant literature and makes suggestions for future researchers in the field.

The methodology used in this research is inspired by the works of Ferreira et al. (2016), Jabbour (2013), Lage Junior and Godinho Filho (2010), Seuring (2013) and Sharma et al. (2018). A total of 1,500 papers written on the pricing of options globally are collated from the Web of Science ranging across 2010-2018.

Most of the research papers present mathematical proposals to value options; without calibrating it with real market data points. The authors bring out five important gaps in the extant literature.

This is arguably the first study that consolidates the literature in the field of market calibrated option pricing analysis with a view to suggest directions for future researchers.

This study examines the role of Big Five personality traits namely openness to experience, conscientiousness, extraversion, agreeableness and neuroticism in regulatory-focused job crafting, i.e. promotion- and prevention-focused job crafting and their inter-relationships.

Survey data collected from 444 executives of Indian public sector energy companies were analysed using structural equation modelling.

Big Five personality traits differentially influence individuals' ways of managing job demands through promotion- and prevention-focused job crafting. These influences are easily identifiable in case of openness to experience, conscientiousness and neuroticism.

Findings of the study may help organizations in developing an effective recruitment, job designing and job allocation process, devise a framework for uncertainty management, encourage their employees to undertake personality-aligned job crafting to manage their ever-emerging jobs and enhance person–job fit.

This study, for the first time, provided a comparative influence of Big Five personality traits on both forms of regulatory-focused job crafting. These findings may be relevant for job demand management in a dynamic business environment.

This paper aims to explore the relationship between the various variables present in the packaging plastic waste management system in the cosmetics industry.

In this paper, the authors deal with plastic packaging waste in the cosmetic industry with the help of system dynamics. The model broadly divides the system into six sections – Cosmetic Packaging, Waste Generation, Waste Collected, Waste Sorted, Waste Treated and Waste Dumped. Businesses have been investing in each section depending on their progress and targets. The authors are looking at case studies of two leading cosmetic brands, L'Oréal and L'Occitane en Provence, to validate the industry practices against our model.

From a business perspective, using the case study methodology for L'Oréal and L'Occitane, the authors inferred that out of the various investment vehicles available, companies are targeting technological advancement and third-party collaborations as they have the potential to offer the greatest visible change. However, most of these investments are going toward the treatment subsection. Still, there is a scope for improvement in the collection and sorting subsystems, increasing the efficiency of the whole chain.

There has been a lot of research on packaging plastic waste management in the past, but only a few of them focused on the cosmetic industry. This study aims to connect all the possible variables involved in the cosmetic industry’s packaging plastic waste management system and provide a clear output variable for various businesses looking to manage their packaging waste because of their products efficiently.