In the world of manufacturing, tribal knowledge refers to the shared but unwritten techniques, procedures and practices that a handful of employees hold. This might sound like a secret sauce that gives your manufacturing company an edge, however, it’s also a ticking time-bomb, set to detonate once these key employees decide to move on.

Then you’re left to scramble, fill in the knowledge gap, all while trying to maintain efficiency and productivity. Not exactly an ideal situation, right?

Imagine a factory floor humming with robotic activity, guided by an invisible hand – AI. Sensors collect real-time data on equipment health, wear patterns and environmental conditions. Algorithms analyze the collected data, predicting maintenance needs and optimizing operations with unparalleled accuracy. Robots collaborate seamlessly with your workforce, performing intricate tasks with pinpoint precision, guided by AI-assisted decision-making. This isn’t sci-fi; it’s the future taking shape.

Predictive Maintenance: Traditional maintenance schedules often lead to unnecessary downtime or catastrophic failures. AI-powered systems can forecast component wear and tear with remarkable precision, enabling targeted preventive maintenance. Ascentek and Redlist provide dynamic AI-powered lubrication and asset management to help prevent downtime and premature failure of critical equipment inside of the manufacturing environment.

John Keller, President and Co-founder, of Redlist says,

“A healthy asset is the lifeblood of your business. Unplanned downtimes due to a preventable reliability issue—can be costly and even catastrophic to your business and the people that run it.”

Using AI to predict when your machines may need maintenance, new lubricants or both, reduces downtime, extends equipment life and optimizes lubricant usage.

Change can be hard and embracing AI requires strategic preparation. Here are some key considerations for manufacturers:

• Educating the Workforce: As AI automates certain tasks, workers will need new skills. Focus on data analysis, programming and collaboration with AI systems to ensure a smooth transition.
• Data-Driven Culture: Data fuels AI algorithms. Invest in collecting and storing high-quality data across operations to facilitate effective AI implementation.
• Cybersecurity and Ethics: Prioritize robust data security measures and adhere to ethical principles in AI development and deployment.
  Partnerships: Collaborate with technology providers, research institutions, and other manufacturers to accelerate learning and share best practices for successful AI integration.

Keller says that the ease of embracing AI in your facility is a matter of effort:

“It really depends on how organized and consistent a manufacturing facility’s data is. If their data is consistent, clean, and organized, it can take a matter of hours to migrate their information and only a few more hours to train their staff. If the data is incomplete or inconsistent it can take several weeks with our team of experts and going through the reps to get your data in the system. This data can and should include pictures of the assets and their respective components and lube points and a basic mapping of where to find these assets within your facilities. It should also include the basic lube instructions of how much of which lubricant goes where and how often.”

If you follow best practices and the advice of experts in the field, then you’ll be able to integrate your facility with AI and foster the knowledge of your key employees that keep your business running. Doing it right will lead to your business increasing productivity with minimal impact on your workers and customers.