Journal
LOGISTICS-BASEL
Volume 5, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/logistics5040084
Keywords
digital twin; data-driven technology; lean manufacturing; supply chain 4; 0; reinforced learning; simulation modelling; prescriptive analysis; systematic review
Funding
- Institute of Business Excellence (IBE), Universiti Teknologi MARA [40450]
Ask authors/readers for more resources
Digital twins, virtual representations that replicate physical objects or processes over time, help reduce manufacturing and supply chain lead time. Reinforced machine learning has been introduced in production and logistics systems to create prescriptive decision support platforms, enabling lean, smart, and agile production setups.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available