Automation of Value Stream Mapping: A Case Study on Enhancing Lean Manufacturing Tools Through Digital Twins

Published

June 16, 2025

Author(s)

Maya Reslan, Matthew Triebe, Rishabh Venketesh, Ashley Hartwell

Abstract

Lean manufacturing has gained popularity over the last few decades as a resource-efficient approach to manufacturing. The rise of smart factories and digital twins brings with it an opportunity to leverage digital technologies and computation in the next generation of lean tools. This paper demonstrates the feasibility of incorporating digital twins into lean tools. Specifically, a manufacturing process is designed for a lab-scale manufacturing cell with the use of a value stream map (VSM) to improve the process. Traditionally, production environments use manual VSMs. We integrate a digital twin with a VSM, enhancing the process with real-time data and more accurate visualization of a production line. The digital VSM is developed to collect data directly from the machines and generate an improvement to reduce non-value-added process time. More broadly, this paper aims to demonstrate how digital twins can support decision-making in a lean manufacturing context by allowing real-time process monitoring, predictive simulations, and dynamic value stream optimization, thereby enabling companies to proactively identify and reduce waste, improve resource utilization, and adapt to changing operational conditions for sustained efficiency and competitiveness.

Proceedings Title

58th CIRP Conference on Manufacturing Systems 2025

Conference Dates

April 13-16, 2025

Conference Location

Overijssel, NL

Conference Title

CIRP CMS

Pub Type

Conferences

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Local Download

Keywords

Value Stream Map, Lean Manufacturing, Digital Twins

Citation

Reslan, M. , Triebe, M. , Venketesh, R. and Hartwell, A. (2025), Automation of Value Stream Mapping: A Case Study on Enhancing Lean Manufacturing Tools Through Digital Twins, 58th CIRP Conference on Manufacturing Systems 2025, Overijssel, NL, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959005 (Accessed June 22, 2025)