Submitted by Luis Morinigo, VP Business Development & Strategic Partnerships at TwinThread
Continuous improvement, as a concept, is nothing new. Maybe that’s why it’s time to brush away the cobwebs and reimagine what continuous improvement can be. AI and machine learning technologies are bringing new life and innovation to continuous improvement and are truly empowering operations teams to go from being reactive to proactive and predictive. This isn’t the old continuous improvement process you have been using for years. This is the next frontier in automation.
It’s easy to get to this next frontier. All you need to do is identify operational issues worth solving, connect your data to uncover insights into performance, and apply a proactive approach to problem-solving using pre-built applications that are part of our Predictive Operations Platform.
Start by prioritizing the metrics you want to improve. Do you need to improve first pass quality? Maybe you need to improve asset reliability? Production rates are always a concern – maybe there’s an opportunity to improve performance there? Is sustainability and energy cost a priority for your organization? Or, maybe you need to extend the useful life of your equipment? Whatever the metric is, focus on your top priority first.
See the Future
Now that you’ve picked the metric you want to optimize, the next step is to connect to your data and apply the out-of-the-box application that is designed to optimize your priority metric. Our Predictive Operations Platform allows you to stay focused on the outcomes and saves you valuable time in development and implementation. In 30 days or less you can combine your existing data with TwinThread’s predictive applications to see what being predictive could mean for your organization. When you can see the future, you can take control of the future.
The Value of Prediction
What would it mean to your organization if you could anticipate problems and had a highly automated process to solve them? Imagine being able to correct quality deviations before they happen. Picture being able to solve downtime causes before you go down. Instead of optimizing your maintenance cycles by time, what if you could optimize by predicted condition?
The answer can be just 30 days away, get started today.