What is Predictive Analytics?
Predictive analytics is an advanced type of analytics that uses artificial intelligence (AI), machine learning (ML) modeling, data mining, and/or deep learning to analyze data. AI and ML-powered predictive analytical engines can spot patterns and trends in datasets that are too big or too complex for human analysis, and use those patterns to make informed, reliable predictions about the future.
Predictive analytics is quite a broad term. It includes many things, like predictive monitoring and predictive maintenance. In a process plant, predictive analytics is used to track and monitor all your processes, workflows, pieces of equipment, and minor parts in order to spot anomalies early.
Process plants are enormous and complex, making it almost impossible for any one person or program to keep track of all the moving parts and processes. But AI and ML-based predictive analytics can.
How can predictive analytics help process manufacturers?
Predictive analytics can help process plants in two main ways:
- Predicting defined, repeating problems so they can be prevented or resolved early. These are significant, but are not the biggest pain point in a process plant.
- Spotting undefined, non-repeating, non-historical problems in their very earliest stages. This delivers much more value, because it’s rare for problems to repeat themselves in a process plant.
- Forecasting demand for product, so plant managers can allocate resources more accurately to the items that are driving the most sales.
- Understanding customer behavior and preferences, so companies can refine their marketing campaigns and brand messaging for better results
- Gain insights into the entire supply and distribution chain to identify weak points, reduce transportation costs, and improve fulfilment times to raise customer satisfaction.
By generating very early alerts about issues that are still developing, predictive analytics notifies you before your equipment begins to fail or limits arise on production. Predictive analytics helps your business to:
- Resolve issues faster while they are still minor
- Speed up investigations into plant incidents
- Optimize production
- Prevent unexpected shutdowns and shorten planned shutdowns
- Gain a better understanding of plant operations
- Stop chasing fires and start optimizing plant performance
How can process plants use predictive analytics to its fullest extent?
Map critical needs
Every plant has different needs from the same predictive analytics solution, like reducing part failures, struggling with process and optimization, decreasing energy consumption, or unique plant-specific issues. Discover your priority pain points to implement the new solution in the most effective way.
Gather plant data
Predictive analytics need the right kind of data, and most plants already generate it, but you might not be recording it or storing it for a long enough period of time. Make sure you’ve got enough deep digital data, utilization data, and data historian tags for the new solution to work on.
Assess plant personnel
The ebay solution in the world will fail if you don’t have someone with the time, skills, and interest to implement it and learn how to use it properly. Employees who already have a full workload won’t be able to take on another task.
Prepare to lead a culture shift
Predictive analytics is an entirely new approach to plant management. You’re going to ask a lot of key employees to start catching and addressing issues early, before they become serious or evident to the trained eye, instead of ignoring them as insignificant. You might need some education sessions before your workforce is ready for this new solution.
Get the management on board
Bringing in a predictive analytics solution is part of your digital transformation. It’s more than just adding a new tool to your toolbox. You need the management to be enthusiastic about the potential of predictive analytics if you want to see adoption and support across the organization.
How does predictive analytics benefit process plants?
Embracing the new possibilities of predictive analytics can help you cut maintenance and repair costs, prevent shutdowns, increase efficiency, and achieve operational excellence. By taking steps to implement predictive analytics, you can optimize production, improve customer satisfaction, gain an edge over the competition, and increase your bottom line.