Integrating operators and enabling technologies in construction equipment to enhance lifecycle value and future-proof business models

This research investigates the application of enabling technologies within the construction equipment industry. The primary challenge addressed is enhancing customer value across the entire equipment lifecycle. Through iterative exploration, creation, and validation phases, the research developed and tested various prototypes to optimize sales processes, maintenance procedures, and equipment management. The findings highlight the potential of advanced data collection from operators for predictive maintenance, enhancing machine learning models by integrating human and machine data for more accurate predictions, reducing downtime, and boosting operational efficiency to align construction equipment maintenance with the demands of future business models.

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Publication Details

Journal
Procedia CIRP
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procir.2025.08.083
Primary Topic
Design Education and Practice
Type
article
Field-Weighted Citation Impact
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article

Integrating operators and enabling technologies in construction equipment to enhance lifecycle value and future-proof business models

Ryan Ruvald, Christian Johansson Askling, Evelina Persson, Sowmya Sista Lakshmi et al.
Procedia CIRP
Design Education and Practice
article

Integrating operators and enabling technologies in construction equipment to enhance lifecycle value and future-proof business models

Ryan Ruvald, Christian Johansson Askling, Evelina Persson, Sowmya Sista Lakshmi, André Leme Fleury, Simon Bååth, Rossana Kallas, Samer Albardan, Artur Paparounis, Gabriel Paraizo
article en

Abstract

This research investigates the application of enabling technologies within the construction equipment industry. The primary challenge addressed is enhancing customer value across the entire equipment lifecycle. Through iterative exploration, creation, and validation phases, the research developed and tested various prototypes to optimize sales processes, maintenance procedures, and equipment management. The findings highlight the potential of advanced data collection from operators for predictive maintenance, enhancing machine learning models by integrating human and machine data for more accurate predictions, reducing downtime, and boosting operational efficiency to align construction equipment maintenance with the demands of future business models.

Procedia CIRPVol. 136
Universidade de São Paulo (BR), Blekinge Institute of Technology (SE)
Industry, innovation and infrastructure
Openalex Percentile: Top 77%
Design Education and Practice
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Integrating operators and enabling technologies in construction equipment to enhance lifecycle value and future-proof business models — Ryan Ruvald, Christian Johansson Askling, et al. · Procedia CIRP (2025) | TGRS Research Map | TGRS