• Energy Research

Notwithstanding the research on refining lean tools for the sake of sustainable development is slowly progressing, sustainability-oriented application of value stream mapping has received undivided attention from practitioners and researchers. Going through the literature highlights that there is a lack of research in integrating and systematising the available knowledge on this lean tool, which is regarded as a visual process-based method to make sustainable progress over the time-based and green concepts of wastes to also assess and improve the societal sustainability performance of organisations. Hence, this paper has been aimed at presenting the findings of a systematic literature review on value stream mapping from the triple bottom line point of view. It classifies and codes the main studies in the context as well as provides a research agenda with nine recommendations that may advance this under-studied field. To narrow the gap in the current literature, this article also proposes a sustainability indicator set that would considerably contribute to guiding and strengthening the state-of-the-art research on successful implementation of the application. Besides, the findings indicate that more investigations are needed on employing survey and conceptual methodologies, applying comparative and cross-industry perspectives, developing sustainability indicator sets particularly societal metrics, and considering the stakeholders’ benefits from adopting sustainability-oriented value stream mapping. The research on the convergence of this sustainability-oriented application and new paradigms such as IR 4.0 and/or Circular Economy should be also strengthened.

The manufacturing industry has undergone numerous revolutions over the years, with a unanimous acceptance of the greater benefits of being sustainable. The present industrial wave—Industry 4.0—by using its enabling technologies and principles holds great potential to develop sustainable manufacturing paradigms which require balancing out the three fundamental elements —products, processes, and systems. Yet, numerous stakeholders, including industrial policy and decision makers, remain oblivious of such potential and requirements. Thus, this bibliometric study is aimed at presenting an overview of the broad field of research on the convergence of sustainable manufacturing and Industry 4.0 under the umbrella of “Sustainable Manufacturing 4.0”, which has yet to be developed. It includes the dissemination of original findings on pathways and practices of Industry 4.0 applied to the development of sustainable manufacturing, contributing a bibliometric structure of the literature on the aforementioned convergence to reveal how Industry 4.0 could be used to shift the manufacturing sector to a more sustainable-based state. An initial research agenda for this emerging area has accordingly been presented, which may pave the way for having a futuristic view on Sustainable Manufacturing 5.0 in the next industrial wave, i.e., Industry 5.0.

Big data analytics, described as the fourth paradigm of science breaking through Industry 4.0 technological development, continues to expand globally as organizations strive to attain the utmost value and sustainable competitive edge. Yet, concerning its contribution to developing sustainable products, there is a need for innovative research due to limited knowledge and uncertainty. This research is hence aimed at addressing (a) how research on big data analytics for sustainable products has evolved in recent years, and (b) how and in what terms it can contribute to developing sustainable products. To do so, this study includes a bibliometric review performed to shed light on the phenomenon gaining prominence. Next, the fuzzy technique for order of preference by similarity to ideal solution, along with a survey, is used to analyze the matter in terms of the respective indicator set. The review’s findings revealed that there has been growing global research interest in the topic in the literature since its inception, and by advancing knowledge in the area, progress toward sustainable development goals 7, 8, 9, 12, and 17 can be made. The fuzzy-based analytical findings demonstrated that ‘product end-of-life management efficiency’ has the highest contributory coefficient of 0.787, followed by ‘product quality and durability’ and ‘functional performance’, with coefficients of 0.579 and 0.523, respectively. Such research, which is crucial for sustainable development, offers valuable insights to stakeholders seeking a deeper understanding of big data analytics and its contribution to developing sustainable products.