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The literature that is presently available on sustainable supply chain management (SSCM) combining optimization and industry 4.0 techniques falls short in its depictions of the recent developments, budding pertinent areas and the importance of SSCM in the growth of industrial economies around the world. This article's main objective is to analyze current trends, highlight the latest initiatives and perform a meta-analysis of the literature that is currently accessible in the SSCM area with a special focus on optimization and industry 4.0 techniques. The paper also proposes a conceptual framework that will assist in illuminating how the ideas of optimization and industry 4.0 may contribute to realizing sustainability in supply chains.

The proposed study systematically reviews 85 research publications published between 2010 and 2022 in referenced peer-reviewed journals in diverse fields, including engineering, business and management, services and healthcare. Numerous categories are considered throughout the examination of the literature, including year-wise publications, prominent journals, type of research design, concerned industry and research technique used.

The study demonstrates a deeper comprehension of the literature in the field and its evolution throughout numerous industry sectors, which is helpful for both practitioners and academics. The results from the content analysis highlight various future research opportunities in the domain.

This is one of the first research articles that have attempted to establish, analyze and highlight the current trends and initiatives in the SSCM domain from an optimization and industry 4.0 techniques viewpoint. The cluster-based future research propositions also enhance the novelty of the study.

The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.

This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.

This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.

The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.

This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.

The European Union's Emissions Trading System (EU ETS), which is already one of the EU's most impactful instruments for reducing greenhouse gases (GHGs), will soon include the maritime transport industry. Although ports are this industry's most environmental-friendly component, there are still some barriers to including ports in the system. Therefore, the purpose of the study is to identify these barriers and to reveal the barriers' interrelationships.

The study was conducted by identifying barriers from a literature review before analyzing the barriers with the Fuzzy DEMATEL method. Finally, based on the Complex Adaptive System Approach, various solutions are proposed to overcome these barriers.

The identified barriers were grouped into cause-and-effect groups. Two barriers, namely long payback period and high investment costs, were evaluated as triggers of the model while the others were more sensitive to the model.

This study only includes the perceptions of green certificated ports in Türkiye. The results revealed an expectation that elimination of financial concerns will alleviate other barriers to including ports in the system. The study's findings can guide port managers on the integration of the managers' processes into the system.

This study provides novel findings regarding the relationships between barriers hindering ports from involvement in the EU ETS.

When is it more profitable for multinational manufacturers to manufacture in high-cost environments and when in low-cost environments? While the literature offers many cues to answer this question, too little empirical research directly addresses this. In this study, we quantitatively and empirically investigate the financial effect of companies' production footprint in low-cost and high-cost environments for different types of production networks.

Using the data of 770 multinational manufacturing companies, we analyze the relationship between production footprints and profitability during four calendar semesters in 2018 and 2019 (N = 2,940), investigating the moderating role of companies' production network type.

We find that companies with networks distinguished by both high levels of product complexity and process sophistication profit the most from producing to a greater extent in high-cost countries. For these companies, shifting production to low-cost countries would be associated with negative performance implications.

Our findings suggest that the production geography of companies should be attuned to their network type, as defined by the companies' process sophistication and product complexity. Manufacturing in low-cost countries is not always the best choice, as doing so can adversely affect profits if the products are highly innovative and the production processes are complex.

We contribute to the scarce empirical literature on managing global production networks and provide a data-driven analysis that contributes to answering some of the enduring questions in this critical area.

There is a growing body of literature discussing the green logistics practices (GLPs) that companies could introduce to reduce the logistics environmental impact. Current approaches also identify several influencing factors within firms that could serve as barriers to, or enablers of, GLPs. However, less is known about the role of extra-firm stakeholders, even though these are crucial to operationalizing green logistics effectively. This study merges current theoretical understanding with empirical evidence to provide a detailed stakeholder analysis of GLPs.

Using stakeholder theory as a theoretical lens, the authors aimed at offering a mid-range contribution by conducting multiple embedded case studies examining Italian logistics service providers and shippers. GLPs and the related influencing factors were examined as sub-units of analysis within broader companies' environmental sustainability strategies.

The authors identified cascading effects among factors influencing the adoption of GLPs (e.g. key economic factors are affected by external factors which also influence organizational and collaboration factors). These effects are moderated by interdependencies between primary and secondary stakeholders, and the study highlights the prominent involvement of secondary stakeholders, such as final consumers.

This paper contributes to better understanding how and why companies adopt GLPs, emphasizing the wide set of stakeholders involved and illustrating how different stakeholders impact on GLPs adoption by affecting a set of influencing factors. By combining insights from the available literature with contemporary empirical data, the authors emphasize how Logistics Service Providers (LSPs) and shippers can no longer address the adoption of GLPs as “focal companies”, but only as part of a “focal network of interconnected stakeholders”, all of them influencing GLPs adoption.

This study has two major objectives. First, comprehensively review the literature on transparency in supply chain management. Second, based on a critical analysis of literature, identify the attributes and sub-attributes of supply chain transparency and develop a numerical measure to quantify transparency in supply chains.

A systematic literature review (SLR) was conducted using the PRISMA approach. Utilizing SCOPUS database past eighteen-year papers search resulted in 249 papers to understand major developments in the domain of supply chain transparency. Subsequently, graph theoretic approach is applied to quantify transparency in supply chain and the proposed index is evaluated for case supply chains from pharma and dairy sectors.

It can be concluded from SLR that supply chain transparency research has evolved from merely tracking and tracing of the product towards sustainable development of the whole value chain. The research identifies four major attributes and their sub-attributes that influence transparency in supply chains, which are used to develop transparency index. The proposed index for two sectors helps to understand areas that need immediate attention to improve transparency in the case supply chains.

This paper attempts to understand the development of transparency research in supply chain using the PRISMA approach for SLR. In addition, development of mathematical model to quantify supply chain transparency is a novel attempt that would help benchmark best practices in the industry. Further, transparency index would help to understand specific areas that need attention to improve transparency in supply chains.