A Novel Fuzzy-Based VIKOR–CRITIC Soft Computing Method for Evaluation of Sustainable Supply Chain Risk Management

Assessing the role of green supply chain management in enhancing Pakistan manufacturers' performances

The purpose of this study is to explore the impact of success factors of Green Supply Chain Management (GSCM) dimensions (i.e., green purchasing, internal environmental management, cooperation with customers, eco-design, and investment recovery) and their impact on the Organizational Performance of Manufacturers in Pakistan on three pieces of levels of execution (i.e., Environmental Performance, Economic Performance, and Operational Performance), whereas GSCM practices in developing countries like Pakistan need to be explored. Data was collected from managers of 220 business firms in Pakistan through survey questionnaires with a purposive sampling technique. The target population consisted of managerial employees (business experts and executives) from private business firms, and partial least square-structural equation modeling was used for analysis. All GSCM dimensions were found significant in at least one of the performances, except eco-design on environmental performance and green purchasing on economic performance, either clearly or indirectly. The proposed model assembles and provides the respondents' distinctive working areas: electronic gadgets, automobile process and machinery sectors. Moreover, the appraisal of the connection between five developments of GSCM practices and three builds with the effect of the green distribution of authoritative execution in the Pakistan assembling setting offers an extraordinary theoretical commitment to exploring the green supply chain management field. First, this research is done by studying the green supply chain management dimensions of different performances of manufacturing firms in Pakistan, which has never been done before. Second, it contributes to the existing literature on critical success factors of GSCM. Manufacturing firms should implement GSCM practices to improve all three types of performance, i.e., environmental, economic, and operational.

A survey of multi-criteria decision-making techniques for green logistics and low-carbon transportation systems

With the increasing severity of environmental problems, low-carbon development has become an inevitable choice. Nowadays, low-carbon green sustainable development is influenced by a variety of factors such as social, environmental, technological, and economic development levels, making its development complex, which in turn imposes challenges on decision-makers. In this context, the application of multi-criteria decision-making (MCDM) in different areas of sustainable development engineering has become a hot topic. Although many reviews of MCDM techniques already exist, there is a lack of holistic review efforts on MCDM in the field of low-carbon transport and green logistics. Considering these shortcomings in the state of the art, this paper systematically reviews more than 190 papers from 2010 to 2022, constructs a general structure of MCDM techniques for this research topic, provides a comprehensive review and analysis of it, and clarifies the current practices. Furthermore, future directions for the development of MCDM techniques for green logistics and low-carbon transportation systems are presented as well.

Key performance indicator based dynamic decision-making framework for sustainable Industry 4.0 implementation risks evaluation: reference to the Indian manufacturing industries

Global corporate giants are keen to adopt Industry 4.0 (I4.0) owing to its continuous, impactful, and evident benefits. However, implementing I4.0 remains a significant challenge for many organizations, mainly due to the absence of a systematic and comprehensive framework. The risk assessment study is key to the flawless execution of any project is a proven fact. This paper aims to develop a KPIs-based sustainable integrated model to assess and evaluate risks associated with the I4.0 implementation. This research paper has developed the I4.0 risks evaluation model through fifteen expert interventions and an extensive systematic literature review. This research, based on sixteen KPIs evaluates six risks impacting the organization's decision to adopt I4.0. Initially, the Fuzzy Decision-Making Trial and Evaluation Laboratory method is used to map the causal relationship among the KPIs. Further, the additive ratio assessment with interval triangular fuzzy numbers method is used to rank the risks. The study revealed that information technology infrastructure and prediction capabilities are the most crucial prominence and receiver KPIs. Simultaneously, technological and social risks are found to be highly significant in the I4.0 implementation decision-making process. The developed model meticulously supports the manufacturer's, policymaker, and researchers' viewpoint toward I4.0 implementation in the present and post COVID-19 pandemic phases in manufacturing companies. The comprehensive yet simple model developed in this study contributes to the larger ambit of new knowledge and extant literature. The integrated model is exceptionally based on the most prominent risks and a wider range of KPIs that are further analyzed by aptly fitting two fuzzy MCDM techniques, which makes the study special as it perfectly takes care of the uncertainties and vagueness in the decision-making process. Hence, this study is pioneering and unique in context to I4.0 risks prioritization aiming to accelerate I4.0 adoption.