Industrial maintenance has traditionally been reactive, labor-intensive, and wasteful, leading to high energy use, carbon emissions, and unnecessary parts replacement. At MD&M West, Javid Vahid of Edlore Inc. presented a case study showing how a suite of digital technologies, such as digital twins, artificial intelligence, augmented reality, and predictive analytics, can transform these processes into sustainable, circular workflows. The collaboration between Edlore and Siemens demonstrated that digital transformation could lower environmental impact while improving operational efficiency, tells Design News.
A digital twin is a dynamic virtual model of a physical asset or system that continuously syncs with real-world data. Paired with AI, these models go beyond static simulation to predict how equipment will perform, detect emerging faults, and recommend actions. In the maintenance context, this means identifying potential failures before they occur and reducing unnecessary part replacements by up to 40% annually, according to data from the session. Digital twins can reduce troubleshooting time by 30–50% across operations, translating into less downtime, lower energy use, and fewer emissions from idle machinery.
Augmented reality further enhances this model by overlaying actionable insights on the physical equipment at the point of maintenance, helping technicians diagnose issues faster and with fewer errors. Predictive AI analyzes sensor streams and historical performance data to forecast wear and remaining useful life for components, allowing maintenance crews to service machines only when needed rather than on fixed schedules. This shift from reactive to predictive models aligns sustainability with profitability by cutting waste and extending equipment life.
Together, these technologies support a maintenance paradigm that is not only more efficient but also more environmentally responsible. By anticipating problems, optimizing intervention timing, and minimizing unnecessary replacement parts, companies can lower carbon footprints and resource use while maintaining high operational reliability. These approaches point toward a future in which smart, data-driven maintenance becomes both an economic and environmental advantage for industrial enterprises.