How to Implement Predictive Analytics For a Manufacturing Environment: Top Tips

Predictive analytics is a term that refers to using machine learning to analyze business data and make predictions about future events. This is a powerful technique that is currently on the rise, specifically in the manufacturing industry. Companies are using predictive analytics to reduce costs, repair expensive machines before they break, and even to optimize schedules, resource consumption, and product quality.

One important piece in implementing predictive analytics for manufacturing is the necessary data science effort of collecting, researching, testing, and processing data. The other main effort necessary is to incorporate the results that come out of the analytics stack into business systems that automate otherwise manual steps.

Analytics can be helpful in gaining profitable insights. However, they can best be leveraged and scaled by integrating them into software that runs the day to day operations of the business. With that in mind, before embarking on a major project, make sure you have the following:

  • Historical results for the AI systems to learn from
  • Sensors, PLC, and process data that actually correlates with these results
  • Existing software that runs the businesses processes, or departmental motivation/ability to create custom software.

To deliver the best results, a prediction system project should be well-designed with the following tips in mind:

When to Predict

The goal of the project is to build predictions into the software. Rather than wait until the end of a user’s task, give them a chance to use the information as early as possible. The software should be able to proactively predict, so users can reactively adjust. Simply put, you want to provide important information to users whenever the software knows they should take a different action than the one they appear to be taking.

  • If the user has made a mistake, provide an alert to let them know (ideally with appropriate corrective actions).
  • If a machine is about to break, identify what is at risk so repairs can occur.
  • If an item is projected to be delivered late, perhaps it can still be saved if the problem is caught early enough.

When to Fall Back

Machine learning has limitations, especially when trying to predict rare occurrences for which there is insufficient data. In such cases, an AI algorithm’s success percentage is going to be much lower than a typical case. Still, not all is lost – you can draw on your experience in your industry. Use that to plan when to switch from machine learning in your software to custom business logic that can make a best guess based on predefined criteria.

Be Agile (Even with Data)

To prove that your project can be successful, try to deliver the smallest piece with the most valuable outcome first. In other words, start with something simple, and add to it over time. This can be difficult to do when it comes to your data. It can be tempting to gather all the available data up front and feed it to your ML algorithm. This approach will likely create problems as the more unprepared data you use, the more noise it contains. Instead, fund small experiments first. Try to be strategic in using a subset of data that is more likely to represent causation. You can add more as you iterate, which will improve the success of your results.

Incorporate Security

Information security is especially important with your predictive software project because of the increased level of automation and the proprietary advantage it represents. Your software should be secured differently depending on if it is public-facing or only for internal use. If public, make sure not to allow external access to the secret sauce that helps propel your business. If internal, you have an extra layer of protection that should keep intruders out of your network. In either case, every consideration should be taken to ensure your system cannot be tampered with. Imagine a worst-case scenario of hackers stopping production, stealing your valuable insights, etc. and make a plan to mitigate these scenarios.

Predictive analytics for manufacturing projects can be incredibly valuable. However, despite all the positive buzz about them, they are not simply plug-and-play. You can’t buy an off-the-shelf software package and immediately gain all the possible benefit. By incorporating predictions into an end-to-end, custom solution, the results can be multiplied for the long term.

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