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In the presented research, the authors have conducted the literature review and organised real interviews of fruit retailers (FRs) to construct the advanced hierarchical structural (AHS) chain of macro-micro parameters for measuring the performances of defined fruit supply bazaars (FSBs). Apart from this, the purpose of this paper is to develop the grey set-based scorecard model for solving the proposed AHS chain of macro-micro parameters.

The performance of FSBs is linked with the supply of fruits towards clients under a feasible rate, which circuitously depends upon the evaluation of the economic locality of FSBs. The authors developed an advanced hierarchical structure of macro-micro parameters via a literature survey and considered these parameters based on the sampling score of FRs corresponding to select feasible FSBs/alternatives. Furthermore, the authors developed a grey set-based scorecard model for undertaking the incomplete information of FRs against the hierarchical structure.

It is found that the work is well suited for FRs as they can measure the performances of defined FSBs in accordance with their own opinions under the proposed AHS of macro-micro parameters. Apart from this, the work is useful for benchmarking the vegetable supply bazaars (VSBs) on the replacement of AHS. The proposed hierarchical structure with a grey-based scorecard model is flexible in its nature and can undertake more than 1,000 macro-micro parameters and FRs to access potential decision.

The conducted research work has a precise value for evaluating the economic FSB locality. The overall performance scores of considered FSB localities are computed as (∂₁)=1.991, (∂₂)=2.567 and (∂₃)=2.855, where (∂₃) is found to be more significant than available FSBs. This work can be used for opting the economic locality of VSB too.

The main purpose of present study is to model the replacement policy under uncertainty for managerial application based on grey-reliability approach by considering the subjective views of quality control circle (QCC). The study objectively links the optimality between individual replacement and group replacement policies for determining the minimum operational costs. The integrated framework between QCC, replacement theory, grey set theory and supply chain management is presented to plan replacement actions under uncertainty.

The study proposes the concept of grey-reliability index and built a decision support model, which can deal with the imprecise information for determining the minimum operational costs to plan subsequent maintenance efforts.

The findings of the study establish the synergy between individual replacement and group replacement policies. The computations related to the numbers of failures, operational costs, reliability index and failure probabilities are presented under developed framework. An integrated framework to facilitate the managers in deciding the replacement policy based on operational time towards concerning replacement of assets that do not deteriorate, but fails suddenly over time is presented. The conceptual model is explained with a numerical procedure to illustrate the significance of the proposed approach.

A conceptual model under the framework of such items, whose failures cannot be corrected by repair actions, but can only be set by replacement is presented. The study provides an important knowledge based decision support framework for crafting a replacement model using grey set theory. The study captured subjective information to build decision model in the ambit of replacement.

The occurrence of COVID-19 has impacted the wide-reaching dimensions of manufacturing, materials, procurement, management, etc., and has loaded disruptions in the wide range of supply chain (SC) activities. The impact of COVID-19 has twisted supplier performance and influenced stakeholders’ thinking towards selecting supplier sources and making strategic sourcing decision for convinced arrangement of construction management (CM) resources. Nowadays, suppliers are intently evaluated by stakeholders in post-COVID-19 phase to induce agile availability of CM resources. Accordingly, this paper aims to demonstrate competent CM dimensions under post COVID-19 scenario for ease managing construction projects by the stakeholders.

The authors have implicated Grey Sets Theory along with decision-making trial and evaluation laboratory (DEMATEL) technique for understanding significant outcomes. Varieties of diverse decision aspects responsible for strategically influencing supplier sourcing decision is projected under post COVID-19 scenario for handling construction projects by the stakeholders.

This study investigated sustainable construction management dimensions (SCMD) at the stage of resource deliveries and client aspirations under post COVID-19 situation. The study demonstrated “Lead time” as the most crucial, “Product Range” as the second and “Customers dealings and relationship” as the third crucial aspect considering by the stakeholders for selecting supplier sources based on the attainment of performance score of 0.1338, 0.1273 and 0.1268, respectively. It is found that high lead time stimulates the stakeholders to divert their orders to other competent supplier sources holding a low degree of lead time as compared.

The present study rollovers its existence by serving critical thinking, conceptual modelling, criteria identification and evaluation under CM domain for drafting effectual strategies by the suppliers. The study investigated the impact of COVID-19 on stakeholders’ decision-making and enlisted SCMD that strategically stimulated them in choosing supplier sourcing decision.

The present study realizes the insights of stakeholders in the post COVID-19 scenario related to the supplier sources based on performance score. The study quantified sustainable supplier attribute for construction work and practices. The study analysed the expectations of the stakeholders purchasing different varieties of construction materials from supplier sources for civil works in the post COVID-19 scenario.

Sustainable manufacturing facilitates the development of products with lower environmental impact. Additive manufacturing (AM) processes are incorporated with sustainable characteristics such as minimum material consumption, energy efficiency and minimum transportation. The purpose of this paper is to report a study on sustainability evaluation of AM process using a grey-based approach.

Sustainable AM process is gaining importance. From this viewpoint, this paper presents the evaluation of sustainability of AM process. The evaluation model includes 3 enablers, 18 criteria and 54 attributes. Grey-based approach is used for sustainability evaluation. Expert inputs are used for computing the grey index. Expert inputs are obtained and they are aggregated at three levels to calculate the overall grey performance index, which indicates sustainability level of AM processes. Furthermore, weaker areas are identified through determination of grey performance importance index (GPII) values.

The calculated grey index is (3.510, 16.177), which implies that AM process is sustainable. Weaker attributes are determined on the basis of the computation of GPII values.

The study has been executed on the basis of the opinion from experts with practical experience. Hence, the inferences are found to be practically feasible. The identified weaker attributes from the study would enable the manufacturers and practitioners to focus more on weaker areas for enhancing the sustainability of AM processes. The study has made an evaluation from sustainability perspective of AM processes, which would enable practitioners to assess AM processes from TBL sustainability orientation.

The development of sustainability evaluation model and application of a grey-based approach for assessment of AM process are the original contributions of this study.

In the present era, executives are shifting keenly toward industrial Internet of things (IIoTs) spheres. It is observed that IIoTs spheres become a key for each industry to grow up and bear the largest entrepreneurship opportunities globally and is linked to improve the shifting sphere of publics (SSPs). The core objective of research work is SSPs, which is nexus on secondary objectives. The authors proposed the two DSSs ( decision support systems) to full fill secondary objectives as discussing: In case of first objective, the authors proposed a fuzzy-DSS, which assists the executives to identify the weak and poor performing IIoTs spheres so that performance of IIoTs spheres can be accelerated. In case of second objective, grey-DSS aids the same executives to evaluate and benchmark alternative partner under considered IIoTs spheres so that the best partner can be chosen by company 4.0.

The authors conducted the significant systematic literature review and realistic empirical survey in the context of industry IIoTs spheres and extract the appropriate IIoTs spheres. Next, the authors built a framework by compiling the global standardized IIoTs spheres. The framework is utilized to build the two DSSs such as fuzzy- and grey-DSS (to full fill secondary objectives). The both DSSs are simulated by acting on a case study. The authors implemented the fuzzy set coupled with degree of similarity approach on proposing framework as a part of first case-objective and hybrid technique accompanied with grey set on same framework as a part of second case-objective, respectively.

A South African automobile parts manufacturing company is investigated as a case study company 4.0 for the prototype testing and simulation of DSSs. The performance gaps are computed and measured by subtracting each sphere's weight of functional units (FUs) from evaluated ideal weight. The weak performing spheres and FUs are suggested to be improved in future as a part of first objective. Next, A3 parts supplier/partner is advised as the best alternative by simulating the grey-DSS under IIoTs framework as a part of second case-objective. Both secondary objectives (two DSSs) are framed to attain the core objective (SSPs).

As discussed, the core objective of research work is to attain the SSPs, linked to secondary objectives. The research work integrates the knowledge and thinking of SSPs as well as IIoTs researchers to create the novel mathematical and statistical IIoTs in focusing on advance SSPs networks. The research work is momentous for entire Industry 4.0 companies, which troubles to bear more entrepreneurship opportunities (improving the SSPs) at global standard.

The purpose of this paper is to develop an agility evaluation approach to determine the most suitable agile system for implementing mass customization (MC) strategies. Evaluating the alternatives and comparing across them, the best practices of the efficient organization can be identified and transferred to different organizations.

Grey relation approach is a simple mathematical technique useful in situations where the information is not known precisely. Grey relation approach has been applied to measure the agility of various organizations based on agile entities and accordingly the organizations are ranked. The ranking so obtained is compared with the ranking obtained by a popular multi‐attribute decision making (MADM) process known as Fuzzy TOPSIS (technique for order preference by similarity to ideal solution) to test the robustness of the proposed method. It is to be noted that grey theory considers the condition of the fuzziness and can deal flexibly with the fuzziness situation.

It is demonstrated that the grey approach is an appropriate method for solving MADM problems in an uncertain situation with less computational efforts. The alternatives can easily be benchmarked and the best agile system can be selected. As the ranking obtained through grey relation approach closely agree with the ranking found from Fuzzy TOPSIS method, the robustness of the proposed approach is validated. Both the methods lead to choosing a suitable agile system related to mass customization.

In this paper, the proposed approach has been compared with Fuzzy TOPSIS method to test the robustness of the method. Other MADM approaches may be used for comparison purpose to gain insight into the methodology of the proposed approach.

An alternative approach for MADM is proposed to obtain good decisions in an uncertain environment and used for agility evaluation in selected organizations. As agile manufacturing is relatively a new concept, certain and complete information on systems are not available. In such situations, the proposed method can deal with the issue conveniently and results in workable solutions.

Agile practices are important for executing business dealings proficiently in today’s scenario, as they thrust on implicating strategies for meeting quick market requirements. These practices are noteworthy from the point of competitiveness and for fulfilling customer’s demands speedily and promptly. The purpose of this paper is to appraise agile supplier selection dilemma based on analytical hierarchy process (AHP), which accompanied grey information. The authors drafted a group of momentous agile supplier selection measures, which can be utilized by the group of industrial and manufacturing industries to measure the status of agile parameters in their partner firms. G-TOPSIS approach to handling the case of agile supplier selection problem is presented by the authors in this work.

The conception of AHP, grey theory and TOPSIS techniques is fused in this study, under the application arena of agile supply chain management (ASCM). The AHP principals are implicated in the first phase to define the priority importance weights of agile measures and, additionally, grey theory and TOPSIS principals are fused in the second phase to fabricate a significant agile supplier selection model.

A merged approach accompanying multiple measures is developed for aiding decision making and for modeling qualitative characteristics of agile arena under grey domain. The present work can be utilized to access the agile performance characteristics of the organization and can define the status of their partner suppliers. The technical guidelines of AHP and G-TOPSIS approach are explained in this study to be implicated in distinguish decision fields. An educational podium for dispensing the theoretical knowledge on supply chain management, ASCM and agility is presented in this study.

A second-level hierarchical structure is built by the authors to facilitate the managers in taking effective decision pertaining to agile measure in their organizations. The lists of qualitative characteristics are catalogued from the literature review in this study. The built model can undertake risk associated in defining the nature of agile criterions as grey concept can undertake risk associated with the system. Thus, the authors implicated G-TOPSIS approach to handling the case of agile supplier selection problem in this study. The presented hierarchical structure will capably assist the industrial and manufacturing firms to react toward random and unpredictable market requirements, along with attaining organizations goals and profits.

Adopting the circular economy (CE) notion in the supply chain perspective is necessary for the sustainability viewpoint. However, such practices are deficient, especially in developing countries like India, because of several obstacles. The purpose of this study was to create an approach for circular supply chain management (CSCM) adaption in Indian rubber industries by identifying and evaluating its associated obstacles.

A hybrid approach of analytic hierarchy process (AHP) and the grey-based ELECTRE method had been employed in this research to obtain the mutual rankings of the identified obstacles based on their impressions on the CSCM prosperity criteria through a case study and involving diverse expert's opinions.

Presented study's findings illustrate that “Lack of consumer knowledge and consciousness towards environmental sustainability” was found to be the top-ranked obstacle followed by “Unwillingness towards supply chain re-structuring”.

The obstacles' prioritized rankings could help leaders to create sequential strategies for adapting a resilient CSCM structure by systematically eliminating these obstacles. Moreover, the pinpointed critical obstacles could be investigated further in separate studies and generate future studies' scope.

During the extensive literature survey, it had been found that the CSCM practices are in the fledgling stage in the developing country's context. Moreover, studies related to CSCM adaption in rubber-based manufacturing industries were much lacking. Presented work is peculiar, aiming to accelerate the CSCM adaption in the industrial rubber sector in developing countries like India.

The purpose of this paper is to introduce a methodology to help evaluate, select, and monitor sustainable supply chain performance measurement that can be integrated into a performance management system (PMS).

Grey‐based neighborhood rough set theory is used to help arrive at a core set of important business and environmental performance measures for sustainable supply chains. The supply chain operations reference (SCOR) model is used to develop both business and environmental measures for supply chain sourcing.

A case illustration shows the applicability of the methodology. A sensitivity analysis shows that variations in outcome considerations may greatly influence the set of key performance measures for a sustainable supply chain PMS.

The methodology and presentation is conceptual, yet the tool can provide very useful interpretations for both researchers and practitioners.

The tool can be valuable for companies that are trying to identify key environmental and business performance measures for their supply chains. It helps save resources by not requiring the management of a burdensome and complex set of performance measures.

This is one of the few approaches that helps to clearly identify and narrow the set of performance measures for sustainable supply chains. It attempts to do so with minimal information loss. It is also the first time that grey techniques have been integrated with neighborhood rough set methodology.

The purpose of this paper is to design an inference engine to measure the level of readiness of each bank before starting the corporate sustainability auditing process. Based on the output of the designed inference engine, the audition team can decide about the audition resources and the auditing process.

In this paper, the hybrid rough and grey set theory are used to design and create a rule model system to measure the sustainability level of banks. First, 16 rule models are extracted using rough set theory (RST), and the cross-validation of each model is done. Then, the grey clustering is used to combine the same condition attributes and improve the validity of the final model. A total of 16 new rule models are extracted based on the decreased condition attributes, and the best model is selected based on the cross-validation results.

By comparing the accuracy of rough-gray’s rule models and as a result of decreasing the condition attributes, a proper increase in the accuracy of all models is obtained. Finally, the Naive/Genetic/object-related reducts model with 95.6% accuracy is selected as an inference engine to measure new banks’ readiness level.

Sustainability measurement of banks based on RST is a new approach in the field of corporate sustainability. Furthermore, using the grey clustering for combining the condition attributes is a novel solution for improving the accuracy of the rule models.