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Available online at www.sciencedirect.com
Procedia CIRP 134 (2025) 455–460
2212-8271 © 2025 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
Peer-review under responsibility of the scientific committee of the International Programme committee of the 58th CIRP Conference on Manufacturing
Systems
10.1016/j.procir.2025.02.159
Keywords: Lean Manufacturing; Digital Twins; Value Stream Map
1. Introduction
Lean manufacturing (LM) is a well-regarded methodology
that addresses resource efficiency and waste reduction. Since
the introduction of lean, manufacturing has seen great innova-
tion including the growth of smart manufacturing, i.e., the in-
clusion of advanced technologies and digitalization into tradi-
tional processes and methods [1]. Smart manufacturing is at-
tractive because it expands the bounds of what can be pro-
duced and can meet higher goals of efficiency [2]. This work
explores how technologies from the smart manufacturing era
can be leveraged with proven methods such as LM for com-
bined benefit.
This paper focuses on a single LM strategy and demon-
strates the value of its digitalization. Specifically, this paper pro-
vides a methodology for digitalizing a value stream map (VSM)
through the creation of a LM digital twin (DT) and validation
involving the production and inspection of a two-part assem-
bly. The purpose of this paper is to provide a methodology for
using DTs to modernize traditional Lean tools, allowing real-
time process monitoring, easy decision-making, and anticipa-
tion of improvements/insights. These insights eliminate waste,
optimize resources, and provide continuous improvement in
manufacturing operations. VSMs are foundational tools of LM
that manage the flow of material and information required to
manufacture and deliver products to customers. However, inte-
grating process change in VSMs traditionally requires manual
improvements which can be challenging. This paper addresses
these challenges by establishing a method that provides manu-
facturers with real-time feedback from a manufacturing cell for
process planning. Furthermore, the method provides decision
support by automating a labor-intensive step, helping sustain
the benefits of lean methods–improving process efficiency, and
reducing manufacturing waste.
2. Literature Review
LM’s origins began in the 1950s with the Toyota production
system, with aims to decrease waste and non-value-added pro-
cesses, ultimately decreasing the time it takes from customer
order to delivery [3]. While many companies understand the
potential benefits of LM, research shows that a key issue with
Proceedings of the 58th CIRP Conference on Manufacturing Systems 2025
Automation of Value Stream Mapping: A Case Study on Enhancing Lean
Manufacturing Tools Through Digital Twins
Maya Reslan
*a
, Matthew J Triebe
a
, Rishabh Venketesh
a
, Ashley J Hartwell
a
a
National Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, MD 20899, USA
* Corresponding author. Tel.: +1-240-316-7776. E-mail address: maya.reslan@nist.gov
Abstract
Lean manufacturing has gained popularity over the last few decades as a resource-efficient approach to manufacturing. The rise of smart facto-
ries 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 pro-
cess 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.
© 2025 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
Peer-review under responsibility of the scientic committee of the International Programme committee of the 58th CIRP Conference on
Manufacturing Systems
