Equipment Logistics Performance Measurement Using Data-Driven Social Network Analysis
Publication: Journal of Construction Engineering and Management
Volume 145, Issue 5
Abstract
The construction industry relies heavily on the use of equipment. Equipment management for a single project is, in itself, challenging, and large contractors who want to achieve long-term success must also manage equipment at an intraorganizational level. While vast amounts of data are collected and updated dynamically to track equipment status within an organization, current practices do not consider these data during the decision-making process. Rather, companies often rely on a single metric, equipment utilization, for evaluating management performance. Inspired by the ability of social network analysis (SNA) to examine the interactions and relationships between objects, a SNA-based method for investigating equipment movement between project sites and equipment shops is proposed. This study proposes a novel performance metric, the direct dispatch index (DDI), which adds a distance weight to the clustering coefficient of SNA, to measure equipment dispatching performance from equipment logistics data. Historical equipment logistics data from the equipment and project management systems of a company in Alberta, Canada, were used to demonstrate the functionality and feasibility of the proposed approach. The methodology was found capable of evaluating the logistical effort associated with equipment dispatch and planning, thereby enhancing equipment management through improved decision-making.
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Data Availability Statement
Data analyzed during the study were provided by a third party. Requests for data should be directed to the provider indicated in the acknowledgments. Information about the Journal’s data-sharing policy can be found at http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
This research was funded by a Natural Sciences and Engineering Research Council of Canada Collaborative Research and Development Grant (CRDPJ 492657). The authors would like to acknowledge Jeffrey Gossain and Roman Gundyak for sharing their knowledge and expertise on the topic of equipment management, Yihan Zhao for sharing her knowledge of data visualization, and Graham Industrial Services LP for providing data.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 145 • Issue 5 • May 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jun 6, 2018
Accepted: Nov 16, 2018
Published online: Mar 14, 2019
Published in print: May 1, 2019
Discussion open until: Aug 14, 2019
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