Benchmarking smart manufacturing drivers using Grey TOPSIS and COPRAS-G approaches

Smart manufacturing and sustainability: a bibliometric analysis

Purpose

During the past decade, the necessity to integrate manufacturing and sustainability has increased mainly to reduce the adverse effect on the manufacturing industry, transforming traditional manufacturing into smart manufacturing by adopting the latest manufacturing technology as part of the Industry 4.0 revolution. Smart manufacturing has piqued the interest of both academics and industry. Manufacturing is a foundation of products and services required for human health, safety, and well-being in modern society and from an organizational standpoint. This paper uses bibliometric analysis better to understand the relationship between smart manufacturing and sustainability scholarship and provide an up-to-date account of current industry practices.

Design/methodology/approach

This paper used the bibliometric analysis method to analyze and draw conclusions from 839 articles retrieved from the Scopus database from 1994 to February 2022. The methodology is divided into four steps: data collection, analysis, visualization, and interpretation. The current study aims to comprehend smart manufacturing and sustainability scholarship using the bibliometric R-package and VOSviewer software.

Findings

The study provides fascinating insights that may assist scholars, industry professionals, and top management in conceptualizing smart manufacturing and sustainability in their organizations. The results show that the number of publications has significantly increased from 2015 onwards, reaching a maximum of 317 journals in 2021 with an increasing publication annual growth rate of 21.9%. The United Kingdom, India, the United States of America, Italy, France, Brazil and China were the most productive countries in terms of the total number of publications. Technological Forecasting and Social Change, Journal of Cleaner Production, International Journal of Production Research, Production Planning and Control, Business Strategy and the Environment Technology in Society, and Benchmarking: An International Journal emerged as the top outlets.

Research limitations/implications

The research in the area of smart manufacturing and sustainability is underpinned by this study, which aims to understand the trends in this field over the last two decades in terms of prolific authors, most influential journals, key themes, and the field's intellectual and social structure. However, according to the research, this field is still in its early stages of development. As a result, a more in-depth analysis is required to aid in the development of a better understanding of this new field.

Originality/value

The paper focuses on integrating smart manufacturing and sustainability through increased interest from 2015 onwards through the literature review. Specific policies should be formulated to improve the manufacturing sector's competence. Furthermore, these findings can guide researchers who want to delve deeper into smart manufacturing and sustainability.

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.

A Two-Stage Multi-Criteria Supplier Selection Model for Sustainable Automotive Supply Chain under Uncertainty

Sustainable supplier selection (SSS) is gaining popularity as a practical method to supply chain sustainability among academics and practitioners. However, in addition to balancing economic, social, and environmental factors, the emergence of the COVID-19 pandemic has affected the selection of long-term suppliers to ensure sustainable supply chains, recover better from the pandemic and effectively respond to any future unprecedented crises. The purpose of this study is to assess and choose a possible supplier based on their capability to adapt to the COVID-19 epidemic in a sustainable manner. For this assessment, a framework based on multi-criteria decision making (MCDM) is provided that integrates spherical fuzzy Analytical Hierarchical Process (SF-AHP) and grey Complex Proportional Assessment (G-COPRAS), in which spherical fuzzy sets and grey numbers are used to express the ambiguous linguistic evaluation statements of experts. In the first stage, the evaluation criteria system is identified through a literature review and experts’ opinions. The SF-AHP is then used to determine the criteria weights. Finally, the G-COPRAS method is utilized to select sustainable suppliers. A case study in the automotive industry in Vietnam is presented to demonstrate the proposed approach’s effectiveness. From the SF-AHP findings, “quality”, “use of personal protective equipment”, “cost/price”, “safety and health practices and wellbeing of suppliers”, and “economic recovery programs” have been ranked as the five most important criteria. From G-COPRAS analysis, THACO Parts (Supplier 02) is the best supplier. A sensitivity study was also conducted to verify the robustness of the proposed model, in which the priority rankings of the best suppliers are very similar. For long-term development and increased competitiveness, industrial businesses must stress the integration of response mechanisms during SSS implementation in the COVID-19 epidemic, according to the findings. This will result in significant cost and resource savings, as well as reduced environmental consequences and a long-term supply chain, independent of the crisis.

Selection of Cold Chain Logistics Service Providers Based on a Grey AHP and Grey COPRAS Framework: A Case Study in Vietnam

Choosing the most suitable cold chain logistics service providers (CLPs) is a vital strategic decision for businesses aiming to achieve an effective and sustainable cold supply chain. A sustainable CLP is one that integrates sustainable practices across its whole operation cycle to achieve product quality, on-time deliveries, and satisfied customer requirements, while preventing products from going to waste, which is especially important in the context of a developing country. This study aims to evaluate and select the best CLP regarding their sustainability performance. For this evaluation, a multi-criteria decision making (MCDM)-based framework is proposed that integrates the grey analytic hierarchy process (G-AHP) and grey complex proportional assessment (G-COPRAS) methodologies, in which grey numbers are used to express the linguistic evaluation statements of experts. Initially, the evaluation criteria based on service level, economic, environmental, and social dimensions were determined by means of a literature review and experts’ opinions to employ the MCDM approach. The G-AHP was utilized to identify the criteria weights, and then, G-COPRAS was used to select the best CLP among the alternatives. A case illustration in Vietnam is presented to exhibit the presented approach’s applicability. From the G-AHP findings, product quality, logistics costs, innovation, and effectiveness of cold chain processes, customer experience, and CO emissions of refrigerated vehicle were ranked as the five most important criteria. From the G-COPRAS analysis, Yoshida Saigon Cold Logistic (CPL-05) is the best CLP. The robustness of the applied integrated MCDM approach was also tested by conducting a comparative analysis, in which the priority rankings of the best CLPs were very similar. The assessment in this study is directed towards enabling managers, practitioners, and stakeholders of cold chain businesses to assess the most efficient CLP in the supply chain in the market and also to devise suitable strategies toward sustainable development.

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms' productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm's predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems.