The recent technological inclusions in supply chains are encouraging practitioners to continuously rethink and redesign these supply chains. Organizations are trying to implement sustainable manufacturing and supply chain practices to utilize their resources to the full extent in order to gain a competitive advantage. Circular supply chain management acts as the main pathway to achieve optimal circular business models; however, research in this area is still in its infancy and there is a need to study and analyze how the benefits of technology can be leveraged in conventional models to impact circular supply chains and build smart, sustainable, circular business models. To gain better familiarity with the future research paradigms, a detailed systematic literature review was conducted on this topic to identify the dynamics of this field and domains deserving further academic attention. A holistic and unique review technique was used by the authors to capture maximal insights. A total of 96 publications from 2010 to 2021 were selected from the Web of Science core collection database through strict keyword search codes and exclusion criteria, with neat integration of systematic and bibliometric analyses. The findings of this study highlight the knowledge gaps and future research directions, which are presented at the end of this paper.

Background: As the Internet of Things (IoT) has become more prevalent in recent years, digital twins have attracted a lot of attention. A digital twin is a virtual representation that replicates a physical object or process over a period of time. These tools directly assist in reducing the manufacturing and supply chain lead time to produce a lean, flexible, and smart production and supply chain setting. Recently, reinforced machine learning has been introduced in production and logistics systems to build prescriptive decision support platforms to create a combination of lean, smart, and agile production setup. Therefore, there is a need to cumulatively arrange and systematize the past research done in this area to get a better understanding of the current trend and future research directions from the perspective of Industry 4.0. Methods: Strict keyword selection, search strategy, and exclusion criteria were applied in the Scopus database (2010 to 2021) to systematize the literature. Results: The findings are snowballed as a systematic review and later the final data set has been conducted to understand the intensity and relevance of research work done in different subsections related to the context of the research agenda proposed. Conclusion: A framework for data-driven digital twin generation and reinforced learning has been proposed at the end of the paper along with a research paradigm.

Background: Digital and smart supply chains are reforming the food chain to help eliminate waste, improve food safety, and reduce the possibility of a global food catastrophe. The globe currently faces numerous food-related issues, ranging from a lack of biodiversity to excessive waste, and from ill health caused by excessive consumption to widespread food insecurity. It is time to look back at how technology has tackled food supply-chain challenges related to quality, safety, and sustainability over the last decade. Moreover, continuous transformations of the food supply chain into a more sustainable business model with utmost resilience is the need of the hour due to COVID-19 disruptions. Method: This study aimed to systematize literature (2010–2021) in the described context and propose a future research direction, with the assistance of a systematic review and bibliometric analysis on the research agenda proposed above. Results: The findings reveal that technological Industry 4.0 (IR 4.0) tools face specific barriers due to the scope and objective of the application. Conclusion: The Internet of Things has received more attention than any other IR 4.0 tool. More integration between the specialized tools is needed to address this issue. Furthermore, the authors have proposed a food supply chain-based operational framework on technological inclusion to facilitate the roadmap for food supply chain 4.0 for more resilience and food supply chain viability.

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The aim of this study is to investigate the dynamic movement between sectoral indices in the Malaysian Stock Market and the three macroeconomic variables, namely oil price (OP), gold price (GP), and exchange rate (ER) during the period 1995-2014. Using the Augmented Dickey-Fuller and Phillip Perron unit root test, the underlying series are tested as non-stationary at the level but stationary in first difference. The use of Johansen-Juselius (1990) Multivariate Cointegration and Vector Error Correction Model indicate that there is long run linkages between macroeconomic variable and sectoral indices specifically in Technology sector. Meanwhile, the analysis based on Vector Auto regression Model technique indicates that there are short run linkages between macroeconomic variable and sectoral indices namely Financial, Industrial, Consumer Product, Industrial Product, Properties and Trade and Service.