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Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

The purpose of this paper is to present current state of understanding on jet electrodeposition manufacturing; to compare various experimental parameters and their implication on as deposited features; and to understand the characteristics of jet electrodeposition deposition defects and its preventive procedures through available research articles.

A systematic review has been done based on available research articles focused on jet electrodeposition and its characteristics. The review begins with a brief introduction to micro-electrodeposition and high-speed selective jet electrodeposition (HSSJED). The research and developments on how jet electrochemical manufacturing are clustered with conventional micro-electrodeposition and their developments. Furthermore, this study converges on comparative analysis on HSSJED and recent research trends in high-speed jet electrodeposition of metals, their alloys and composites and presents potential perspectives for the future research direction in the final section.

Edge defect, optimum nozzle height and controlled deposition remain major challenges in electrochemical manufacturing. On-situ deposition can be used as initial structural material for micro and nanoelectronic devices. Integration of ultrasonic, laser and acoustic source to jet electrochemical manufacturing are current trends that are promising enhanced homogeneity, controlled density and porosity with high precision manufacturing.

This paper discusses the key issue associated to high-speed jet electrodeposition process. Emphasis has been given to various electrochemical parameters and their effect on deposition. Pros and cons of variations in electrochemical parameters have been studied by comparing the available reports on experimental investigations. Defects and their preventive measures have also been discussed. This review presented a summary of past achievements and recent advancements in the field of jet electrochemical manufacturing.

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.

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.

Good management strives to align and corporate processes for more attention being paid to supply chain management. Firms realize that greater co-operation and improved coordination can help to manage the entire supply chain more efficiently. The imperfect quality item is one of the most important issues that affect the expected profit of green supply chain. The imprecise cost with screening process of poor quality items posed in supply chain is the subject of this study.

The present study explores production model for imperfect items having uncertain cost parameters with three-layer supply chain encompassing supplier, manufacturer and retailer. The model is considering the impact of business tactics such as order size, production rate, production cost and appropriate times in various sectors on collaborative marketing systems. Due to imprecise cost parameters, the pentagonal fuzzy numbers are set to fuzzify the total cost and defuzzifition by using graded mean integration.

This study offers an explicit condition in uncertain environment to manage the imperfect quality item to increase the potential profit of the supply chain. The influence of changes in parameter values on the optimal inventory policy under fuzziness is provided managerial insights.

This model makes a significant contribution to fuzzy inference. The results of the study provide a trading strategy for the industry to avoid losses. The prescribed study can be suitable for the industries like sculpture, jewelry, pottery, etc.

Functionally graded materials are a special class of composites in which material are graded either continuously or layered wise depending upon its applications. With such variations of materials, the properties of structure vary either lengthwise or thickness wise. This paper aims to investigate models for effective estimation of material properties, as it is necessary for industries to identify the properties of composites or functionally graded materials (FGM’s) before manufacturing and also to develop novel material combinations.

Available models were compared for different material combinations and tested with experimental data for properties such as Young’s modulus, density, coefficient of thermal expansion (CTE) and thermal conductivity. Combinations of metal–ceramic and metal–metal were selected such that their ratios cover a wide range of materials.

This study reveals different models will be required depending on the material used and properties to be identified.

The results of the present work will help researchers in the effective modeling of composites or FGM’s for any analysis.

This paper presents a comparison and review of various analytical methods with experimental data graphically to find out the best suitable method. For the first time, the Halpin-Tsai model was extended in the analysis of the CTE which shows good approximations.

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 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.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.

Task complexity is one of the significant factors that influences and is used for forecasting employee performance and determining labor cost. However, the complexity level of tasks is unstructured, dynamic and complicated to perform. This paper develops a new method for evaluating the complexity level of tasks in the production process to support production managers to control their manufacturing systems in terms of flexibility, reliability to production planning and labor cost.

The complexity level of tasks will be analyzed based on the structuralist concept. Using the structure of task, the factors that significantly affect the task complexity in an assembly line will be defined, and the complexity level of the task will be evaluated by measuring the number of task components. Using the proportional 2-tuples linguistic values, the difference between the complexity levels of tasks can be compared and described clearly.

Based on the structure of the task, three contributory factors including input factors, process-operation factors and output factors that significantly affect the task complexity in an assembly line are identified in the present study. The complexity level of the task is quantified through analyzing the details of the three factors according to two criteria and six sub-criteria within the textile case study.

The proposed approach provides a new insight about the factors that have an effect on the complexity of tasks in production and remedies some of limitations of previous methods. The combination of experts' experience and scientific knowledge will improve the accuracy in determining the complexity level of tasks.