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.
Keywords
Iterative methods, Learning systems, Life cycle, Machine learning, Predictive maintenance, Product design, Business models, Customer values, Design thinking, Enabling technologies, Equipment industries, Exploration phasis, Future proofs, Global networks, Machine-learning, Product-service systems, Construction equipment
Reference
Bååth, S., Lakshmi, S., Kallas, R., Albardan, S., Persson, E., Paparounis, A., Paraizo, G., Ruvald, R., Johansson Askling, C., & Fleury, A. L. (2025). Integrating operators and enabling technologies in construction equipment to enhance lifecycle value and future-proof business models. In Procedia CIRP (Vol. 136, pp. 480–485). Elsevier. Presented at 35th CIRP Design Conference, CIRP Design 2025, Patras, April 2-4, 2025. https://doi.org/10.1016/j.procir.2025.08.083
