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Research Article

(DT4Smart) a digital twin-based modularized design approach for smart warehouses

ORCID Icon, , , , &
Received 24 Aug 2022, Accepted 27 Sep 2023, Published online: 05 Nov 2023
 

ABSTRACT

The rapid emergence and widespread adoption of next-generation information technologies have led to a growing recognition of the need for digitalized warehouse designs. However, creating a digital replica of a physical warehouse in a virtual environment is a complex task. This research introduces a framework for designing smart warehouses based on digital twins, consisting of four key steps: (1) defining the dimensions of the digital twin, (2) establishing a digital twin framework that encompasses the physical warehouse, digital twin, and design processes, (3) implementing modularization techniques for the digital twin, and (4) operating the smart warehouse based on the digital twin. To validate the proposed framework, we present a detailed case study involving a semiconductor manufacturing plant. The results demonstrate the effectiveness of the digital twin-based smart warehouse design and its operational processes.

Acknowledgments

The authors thank the editor and the anonymous reviewers for their helpful comments. The authors disclose receipt of the following financial support for the research, authorship, and/or publication of this article. This work was supported by Jiangsu Social Science Fund of China (Grant No.2022SJYB0183), Introduction Program of High-Level Innovation and Entrepreneurship Talents in Jiangsu Province (Grant No. JSSCB20210481), Starting Research Fund from the Nanjing University of Information Science & Technology, and Singapore A*STAR IAF-PP fund (Grant No. A1895a0033) under the project “Digital Twin for Next Generation Warehouse”.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

Additional information

Funding

The work was supported by the Introduction Program of High-Level Innovation and Entrepreneurship Talents in Jiangsu Province [JSSCB20210481]; Singapore A*STAR IAF-PP fund [A1895a0033]; Starting Research Fund from the Nanjing University of Information Science & Technology .

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