According to the International Society of Automation, factories typically miss out on 5% to 20% of their manufacturing potential due to downtime. The old-school approach of preventive maintenance fixes machines based on time or use. But this can still lead to unexpected breakdowns, leaving workers with nothing to do, causing more waste, losing money, and annoying customers. Plus, it sometimes replaces parts that still have some life left, which isn't exactly friendly to the wallet or the clock.

AI-driven predictive maintenance taps into data from IoT sensors in machines, manufacturing processes, and environmental conditions to figure out which parts need replacing before they quit on you. AI models detect patterns hinting at potential failures or forecast a part's lifespan considering its environment. When signs of trouble or age appear, parts get swapped out during planned maintenance breaks. According to McKinsey and Company, this approach can slash annual maintenance costs by 10%, cut downtime by up to 25%, and reduce inspection costs by a quarter.

At Bewize, our goal is to make AI accessible to everyone, enabling more industries to tackle business and social challenges with AI's might. Top brands like Stanley Black and Decker have teamed up with Bewize to bring forward-thinking manufacturing solutions to life using Bewzie Driverless AI. This award-winning platform speeds up the creation of precise predictive models, helping data science teams scale their efforts rapidly. It comes packed with features manufacturers love, like understanding machine learning models, providing reasons for individual predictions, and handling time series automatically.