Digital Value Stream Mapping through Simulation in the Automotive Wiring Industry

Abdelaziz Mrabti

Tunisia

University of Carthage image/svg+xml

Advanced, System Laboratory (LSA), National Engineering School of Carthage

Sana Bouajaja

https://orcid.org/0000-0003-2523-3292

Tunisia

Tunis El Manar University image/svg+xml

LR-OASIS, National Engineers School of Tunis

Khaled Nouri

Tunisia

University of Carthage image/svg+xml

Advanced, System Laboratory (LSA), National Engineering School of Carthage

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Accepted: 2025-10-03

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Published: 2025-12-09

DOI: https://doi.org/10.4995/wpom.23686
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Keywords:

Digital VSM, Plant Simulation, Lean Production, Bottleneck Identification, Waste Reduction, Lead Time Optimization, Production Efficiency, Industry 4.0, Value Stream Design

Supporting agencies:

This research was not funded

Abstract:

In the context of Industry 4.0, integrating digitalization with Lean Manufacturing tools has become a critical strategy for improving operational performance in complex production environments. This paper presents a real-world case study from the automotive wiring industry, where Digital Value Stream Mapping (DVSM) is implemented using Tecnomatix Plant Simulation software to address inefficiencies in production processes. The objective of the study is to identify bottlenecks, reduce waste, and optimize resource utilization through a simulation-based approach. A selected product family is analyzed using current state mapping, followed by simulation to evaluate operational performance and propose data-driven improvements. The results show significant gains: production efficiency was increased, rework and scrap were reduced, and total lead time was also minimized. These improvements were achieved through interventions such as workstation consolidation, workforce optimization, and the implementation of a pull system supported by FIFO logic and pacemaker scheduling. The study demonstrates the potential of DVSM as a practical decision-support tool, enabling manufacturing organizations to digitally model and validate process improvements prior to implementation. These findings contribute to the advancement of lean digitalization strategies in high-variability industrial settings.

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