Advanced planning with supplier selection for modular construction supply chain performance improvement

Purpose This study aims to address project delays and cost overruns in modular construction (MC) caused by poor supplier performance and lack of stakeholder collaboration. This study introduced a decision-support model to help designers and project managers evaluate various modularity and supplier scenarios, enabling the selection of the most cost-effective and time-efficient solutions for MC projects. Design/methodology/approach A generalized mixed-integer linear programming model was developed to optimize supplier selection and activity planning during the project planning phase. The model evaluates multiple modularity and supplier scenarios to identify optimal solutions and is validated through a numerical example to assess its practical applicability and effectiveness. Findings The findings demonstrate that the proposed model significantly enhances the supply chain performance in MC projects. The model mitigates delays and cost overruns by optimizing supplier selection and project activity planning. Experimentation confirms that strategic decisions informed by the model lead to improved efficiency, cost savings and project outcomes. Moreover, the model facilitates enhanced collaboration and decision-making among project managers and designers by enabling scenario evaluations. Originality/value This study presents a novel optimization framework that integrates a transition matrix approach to improve supply chain performance during the transition to MC. By aligning strategic planning with supply chain decisions, the model provides a comprehensive tool for evaluating modularity and supplier options, fostering smoother integration and better project outcomes in MC.

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Publication Details

Journal
Journal of Modelling in Management
Published
2025-06-09
DOI
https://doi.org/10.1108/jm2-05-2024-0156
Primary Topic
BIM and Construction Integration
Type
article
Field-Weighted Citation Impact
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article

Advanced planning with supplier selection for modular construction supply chain performance improvement

Muhammad Atiq Ur Rehman, Amin Chaabane, Sharfuddin Ahmed Khan
Journal of Modelling in Management
BIM and Construction Integration
article

Advanced planning with supplier selection for modular construction supply chain performance improvement

Muhammad Atiq Ur Rehman, Amin Chaabane, Sharfuddin Ahmed Khan
article en

Abstract

Purpose This study aims to address project delays and cost overruns in modular construction (MC) caused by poor supplier performance and lack of stakeholder collaboration. This study introduced a decision-support model to help designers and project managers evaluate various modularity and supplier scenarios, enabling the selection of the most cost-effective and time-efficient solutions for MC projects. Design/methodology/approach A generalized mixed-integer linear programming model was developed to optimize supplier selection and activity planning during the project planning phase. The model evaluates multiple modularity and supplier scenarios to identify optimal solutions and is validated through a numerical example to assess its practical applicability and effectiveness. Findings The findings demonstrate that the proposed model significantly enhances the supply chain performance in MC projects. The model mitigates delays and cost overruns by optimizing supplier selection and project activity planning. Experimentation confirms that strategic decisions informed by the model lead to improved efficiency, cost savings and project outcomes. Moreover, the model facilitates enhanced collaboration and decision-making among project managers and designers by enabling scenario evaluations. Originality/value This study presents a novel optimization framework that integrates a transition matrix approach to improve supply chain performance during the transition to MC. By aligning strategic planning with supply chain decisions, the model provides a comprehensive tool for evaluating modularity and supplier options, fostering smoother integration and better project outcomes in MC.

Journal of Modelling in ManagementVol. 21(1)
University of Regina (CA), École de Technologie Supérieure (CA)
Openalex Percentile: Top 87%
BIM and Construction Integration
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