TY  - CONF
TI  - Towards Enabling Sustainability-aware Operations in Housing Manufacturing with AI-driven Value Stream Mapping: a Review
AU  - Mora, Miguel
AU  - Tang, Pingbo
AU  - Toledo, Mauricio
AD  - PhD student, Civil and Environmental Engineering, HMHI Lab, Carnegie Mellon University, Pittsburgh, PA, USA, mmora@cmu.edu, orcid.org/0009-0003-4285-8670
AD  - Associate Professor, Civil and Environmental Engineering, HMHI Lab, Carnegie Mellon University, Pittsburgh, PA, USA, ptang@andrew.cmu.edu, orcid.org/0000-0002-4910-1326
AD  - Assistant Professor, Head of Civil Engineering Department, Universidad Andres Bello, Santiago, Chile, mauricio.toledo@unab.cl, orcid.org/0000-0002-3903-7260
ED  - Seppänen, Olli
ED  - Koskela, Lauri
ED  - Murata, Koichi
PY  - 2025
DA  - 2025/06/02
T2  - Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33)
C3  - Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33)
CY  - Osaka and Kyoto, Japan
SP  - 976
EP  - 986
DO  - 10.24928/2025/0266
SN  - 2789-0015 (ISSN)
AB  - The construction industry is a major contributor to global CO2 emissions, with housing construction playing a significant role. Lean and technological approaches offer promising solutions for reducing emissions in housing, since Value Stream Mapping (VSM) enhances process analysis, and Artificial Intelligence (AI) can optimize complex systems. This paper aims to identify the state-of-the-art and drivers of current approaches that combine AI and VSM to integrate sustainability analysis in manufacturing. The main objective is to gather insights from the manufacturing industry to develop an AI-driven VSM for sustainability-aware operations in housing manufacturing.  Based on a literature review based on the PRISMA methodology, the authors identified that Internet of Things (IoT) approaches enable AI-driven VSM by integrating real-time data collection. Specifically, IoT enables real-time data collection, and AI enables dynamic process analysis for monitoring, optimizing, and controlling. Additionally, defining sustainability goals and assessing information quality is critical before integrating sustainability variables in AI-driven VSM approaches. This paper presents the research background, findings, recommendations, and future research guidelines to deliver an AI-driven VSM approach for reducing CO2 emissions in housing manufacturing.
KW  - Value Stream Mapping
KW  - Artificial Intelligence
KW  - IoT
KW  - Housing Manufacturing
KW  - Sustainability
L1  - https://www.iglc.net/papers/details/2413/pdf
UR  - https://www.iglc.net/papers/details/2413
DB  - IGLC.net
LA  - English
N1  - Export date: 04 October 2026
ER  -