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The objective of the study is to identify and analyse drivers of smart manufacturing using integrated grey-based approaches. The analysis facilitates industry practitioners in the identification of preference of drivers through which smart manufacturing can be implemented. These drivers are explored based on existing literature and expert opinion.

Modern manufacturing firms have been adopting smart manufacturing concepts to sustain in the global competitive landscape. Smart manufacturing incorporates integrated technologies with a flexible workforce to interlink the cyber and physical world. In order to facilitate the effective deployment of smart manufacturing, key drivers need to be analysed. This article presents a study in which 25 drivers of smart manufacturing and 8 criteria are analysed. Integrated grey Technique for Order Preference by Similarity to Ideal Solution (grey TOPSIS) is applied to rank the drivers. The derived ranking is validated using “Complex Proportional Assessment – Grey” (COPRAS-G) approach.

In total, 25 drivers with 8 criteria are being considered and an integrated grey TOPSIS approach is applied. The ranking order of drivers is obtained and further sensitivity analysis is also done.

In the present study, 25 drivers of smart manufacturing are analysed. In the future, additional drivers could be considered.

The study presented has been done with inputs from industry experts, and hence the inferences have practical relevance. Industry practitioners need to focus on these drivers in order to implement smart manufacturing in industry.

The analysis of drivers of smart manufacturing is the original contribution of the authors.

The purpose of this research article is to investigate the critical risk factors associated with the digitalization impact on the Indian construction industry, as these firm plans to implement digitalization in order to improve their construction management processes.

In this research article, risk factors and dimensions are taken from diverse industries and validated in the construction domain by industry and academic experts. Further, multi-criteria decision-making techniques are employed to assess the Indian construction sector scenario quantitatively. The interrelationship and weightage of risk factors and dimensions are determined by the Fuzzy Decision-making Trial and Evaluation Laboratory Analytic Network Process (DANP) method. However, the method Grey Technique for the Order of Prioritization by Similarity to Ideal Solution (TOPSIS) is used to determine the ranking of each risk factor.

This study finds 14 critical risk factors along with four risk dimensions. Operational and financial dimensions are significant risk dimensions. Whereas the threat of high outlay and low yield is a significant risk factor in construction. Besides that, the interrelationship among risk factors, the weightage of each factor and the ranking of critical risk factors are also identified.

This research article uses Fuzzy DANP and Grey TOPSIS techniques as exploratory research methods with a limited group of construction professionals from a leading Indian construction firm. Furthermore, comprehensive confirmatory research can also be performed with a large group of construction experts using advanced analytical techniques to validate the ranking of critical risk factors.

The findings of this study provide insight into the knowledge of construction firms by emphasizing significant risk factors related to digitalization in construction operations. Another finding of this study shows that the risks associated with digitalization in construction are similar to those in manufacturing, where high outlay and low yield hold a significant role in the transformation process.

The research is unique since there have only been limited studies in the Indian construction scenario to analyze the significant risks associated with digitalization. Furthermore, this study demonstrates that the combination of Fuzzy DANP and Grey TOPSIS techniques may be used successfully to prioritize risks in construction digitalization, which is still in its early phases.

Collaboration methods are unique strategies that can help organizations hedge against external and internal supply chain risks without stressing their relationships with supply chain partners. However, selecting the most appropriate collaboration method from a given set of strategies is a multifaceted challenge. This paper aims to address this issue.

The decision maker's dilemma of fighting data uncertainty in input parameters to check the efficacy of a given collaboration or mitigation approach is tackled by the integration of Grey theory with the technique for order of preference by similarity to ideal solution (TOPSIS) method. The proposed technique is applied and tested for an Indian diesel generator-set manufacturer to identify the most apposite set of sustainable collaboration strategies.

The results showed that when a firm is bidding for different horizontal collaboration strategies across its supply chain system technology and resource-sharing-centered collaboration strategies are the prominent option. In the case of the company's vertical collaboration deployment, the focus should be kept on information sharing to achieve impactful collaboration. The outcome of the analysis helped the Indian manufacturer to adopt transparent order and production information sharing with its regional distributors and core suppliers within its supply chain.

This study demonstrates from a methodological perspective the successful application of the Grey-TOPSIS approach that effectively captures data uncertainty. It also integrates sustainability parameters in collaboration strategy criteria selections.

The food supply chain is exposed to severe environmental and social issues with serious economic consequences. The identification and assessment of risk involved in the food supply chain can help to overcome these challenges. In response, the purpose of this paper is to develop a risk assessment framework for a typical food supply chain.

An integrated methodology of grey analytical hierarchy process and grey technique for order preference by similarity to the ideal solution is proposed for developing a comprehensive risk index. The opinion of the experts is used to illustrate an application of the proposed methodology for the risk assessment of the food supply chain in India.

Valuable insights and recommendations are drawn from the results, which are helpful to the practitioners working at strategic and tactical levels in the food supply chain for minimising the supply chain disruptions.

The risk quantification for the case organisation is primarily based on inputs collected from the experts working for Indian food supply chain, and so the generalisation of the results is limited to the context of developing countries. However, the generalisability of the proposed risk quantification methodology and key insights developed in the food supply chain will assist practitioners in policy making.

The risk priorities established by this research would enable an implementation of systematic risk mitigation strategies and deployment of necessary resources for leveraging the efficiency of food supply chain.

Specifically, this research has delivered a risk quantification framework and strengthened the inquiry of risk management for the food supply chain.

Decision making is the task of selecting the most appropriate alternative among a finite set of possible alternatives with respect to some attributes. The attributes may be subjective or objective (or combination of both), depending upon the situation; requirements may also be conflicting. In practice, most of the real-world decision-making problems are based on subjective evaluation criteria which are basically ill-defined and vague. Since subjective human judgment bears ambiguity and vagueness in the decision making; application of grey numbers set theory may be proved fruitful in this context. The paper aims to discuss these issues.

Owing to the advantages of grey numbers set theory in tackling subjectivity in decision making; the crisp-TODIM needs to be extended by integrating with grey numbers set theory in order to facilitate decision making consisting of subjective data. Hence, the unified objective of this paper is to propose a grey-based TODIM approach in the context of decision making.

Application potential of grey-TODIM has been demonstrated through a case empirical robot selection problem. Result obtained thereof, has also been compared to that of existing grey-based decision support systems available in literature.

Application potential of grey-based decision support systems (grey-TOPSIS, grey analysis, grey-MOORA) have been highlighted in available literature resource. However, the shortcoming of these approaches is that they do not consider decision-makers’ risk attitude while decision making. TODIM method is derived from the philosophy of Cumulative Prospect Theory (CPT) which considers risk averting attitude of the decision maker in case of gain and risk seeking attitude in case of loss, while comparing dominance between two alternatives with respect to a particular criterion. Hence, this paper contributes a mathematical foundation of TODIM coupled with grey numbers set theory for logical decision making.

Circular supply chain management (CSCM) has the potential to compensate for the drawbacks of the linear supply chain model. Therefore, the objective of this study is to identify and prioritise the significant enablers related to the effective management of the circular supply chain (CSC).

The significant enablers of CSCM are identified through a two-step process of literature review and grey-Delphi approach. Further, an integrated grey-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is applied to prioritise these enablers.

This study proposes a grey-based framework to identify and evaluate the enablers associated with CSCM. A total of 12 significant CSCM enablers are finalised per the proposed framework. The findings suggest that the top three enablers are "long-term planning", "development of CE culture" and "alignment of CE goals with strategic objectives". These enablers require the attention of the top management and supply chain partner on priority.

This study identifies 12 enablers through the literature review, so there is a possibility to overlook some significant enablers. However, the expert's input is used to finalise and analyse the enablers, which can be biased towards their working level.

This study proposes a grey-based framework to identify and analyse the CSC enablers rarely investigated in the contemporary literature. The paper is strong in methodological contributions and gives practitioners and researchers a better framework for CSCM.

This paper aims to develop an efficient measurement index evaluation system towards assessing suppliers' green performance practices. Apart from estimating overall performance index, the paper also aims to highlight application of decision‐support tools for selection of appropriate candidate supplier in green supply chain management context.

In order to tackle incompleteness and imprecision arising from assigning appropriateness rating as well as priority weights against subjective performance criteria‐attributes, use of grey numbers was proposed. An efficient grey‐based supplier appraisement platform was established. Application of Grey‐Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and COPRAS‐G method were reported to solve the supplier selection decision‐making problem. The theory of grey numbers was utilized here to facilitate such decision modeling.

Over the last two decades, growing concerns about ecosystem quality have stimulated to a renewed interest in environmentalism. Purchasing professionals should also be concerned and need to rethink purchasing strategies which have traditionally neglected environmental impacts. The “green” purchasing‐packaging in reducing and eliminating waste is a major concern in recent days. In order to help foster environmentally concerned purchasing strategies, this paper presents the findings of supplier evaluation strategy in an enterprise with enhanced degree of awareness and frequent applications of “green” purchasing. Environmental factors are identified that may reshape supplier evaluation decisions. The concept of grey numbers set has been adopted in this work. A case study reflects effectiveness of exploring grey relation theory in the context of green supplier evaluation.

The major contributions of this work have been summarized as follows: development and implementation of an efficient decision‐making tool to support green supplier evaluation; an overall green performance index evaluation platform has been introduced; concept of grey numbers has been efficiently explored to facilitate this decision‐making; the appraisement index system has been extended with the capability to search ill‐performing areas which require future progress; and the proposed appraisement system is capable of reducing the number of green attributes towards computing grey appropriateness index thereby transforming into lesser number of green capabilities, thus, facilitating applying decision‐making tools like grey‐TOPSIS and COPRAS‐grey method for appropriate supplier selection from a set of candidate suppliers.

In present research, the authors conducted the massive literature review and collected the information, in regards to material handling system (MHS) to build a multi criteria MHS hierarchical module consists of ecological cum fiscal criteria. Moreover, similar literature review assisted the authors to resolve and eventually construct the effectual and robust approach. The purpose of this paper is to facilitate the managers for benchmarking the MHS alternatives operating under similar module via robust decision support system (DSS).

In present research, the proposed module dealt with ecological (subjective) and fiscal (objective) criteria, where subjective criteria associated with incompleteness, vagueness, imprecision, as well as inconsistency, solicited the discrete information in terms of Grey set via linguistic scale from experts panel. The objective information (capital) has been assigned by expert’s panel in terms of Grey set. To robustly evaluate and select the admirable MHS, three approaches named: degree of possibility, technique for order preference similar to ideal solution as well as Grey relational analysis fruitfully applied to connect and unite discrete information.

The performance evaluation of MHSs has been carried out under concert of individual fiscal criteria excluding ecological criteria in past researches. Moreover the previous developed DSS tackled sole approach under individual fiscal criteria. The authors found the broad applications of fuzzy sets except Grey set theory in the same context for measuring the performance of MHS alternatives. Aforesaid research gaps have been transformed into research objectives by incorporating the module for both fiscal cum ecological criteria. This research embraces a robust DSS, which has been explored to select the admirable MHS alternative.

An empirical case study has been carried out in order to demonstrate the legitimacy of holistic Grey-MCDM method, implemented over multi criteria MHS hierarchical module. Proposed DSS seems to be the best for organisations, which believe to appraise and select the MHS including fiscal as well as ecological criteria excluding individual fiscal criteria. Moreover, subjective cum objective or individual subjective or objective criteria can be extended with respect to varieties of MHSs.

Additive manufacturing (AM) technologies have tremendous applications in industries owing to their unique advantages. Sustainable AM (SAM) is gaining significance because of lightweight structures, lattice geometries and customized parts for industrial applications. To facilitate design for SAM, design guidelines from AM and environment viewpoints are to be analyzed. In this context, this paper aims to present the analysis of SAM guidelines.

This work divides 26 identified SAM guidelines into four categories. Grey axiomatic design was used to calculate the weights of guidelines categories. Further, the grey technique for order preference by similarity to ideal solution was used as a solution methodology to prioritize the SAM guidelines.

The top identified guidelines are “Design for reusability” and “Optimize part orientation for build time and roughness.” Implementing proper design guidelines leads to many sustainable benefits such as minimum material consumption, energy consumption and emissions.

This study would facilitate Am product designers to deploy prioritized guidelines for enhancing the effectiveness of the additively manufactured product with sustainability benefits. The prioritized guidelines would guide the AM product designers to take maximum advantage of the AM process by deploying design for AM and design for environment guidelines. This study contributed a structured approach for design engineers and practitioners to deploy guidelines during the early stages of product design to ensure AM feasibility with minimal environmental impact.

The activities using drum resources restrict the operation of multi-project systems. However, existing monitoring methods are not suitable for the characteristics of drum activities in the multi-project system. The authors therefore propose an adaptive capacity constraint buffer monitoring model based on the attributes of drum activities, aiming to build a high-efficiency progress control framework for multiple projects.

Considering the attributes and the interrelationship of drum activities, the monitoring reference points are determined on the basis of decentralized buffers. The authors next set action thresholds according to the relationship between the drum activities' interval margin and buffer consumption, and then the corresponding monitoring measures are taken.

The empirical results show that, compared to the classic methods, the proposed approach can effectively monitor the progress of the drum plan and realize the dual optimization of multi-project duration and cost.

The buffer consumption at the follow-up monitoring time point is neglected when determining the action thresholds. Prediction methods can be introduced to present more all-sided monitoring.

This paper fulfils the dual optimization of multi-project duration and cost. It provides a reference guide for project managers.

A capacity constraint buffer monitoring method suitable for a multi-project environment is produced.