Machine learning algorithms have a growing number of uses and, in the age of the Internet of Things, automation and artificial intelligence (AI), their uses are sure to expand much further in the not too distant future.
One of the areas being transformed by the use of machine learning algorithms is that of predictive analytics systems for use within modern manufacturing and factory environments, especially those looking to further or enhance their transition into a smart factory.
These predictive analytics systems are enabling manufacturers and factory operators to gain a much deeper insight into various aspects of the operational performance of their technological devices and machinery.
In this article, we’ll be looking at how to choose the right predictive analytics solution for your factory and breaking that concept down into four main elements to consider when making your decision. The elements we’ll consider are quality, application, integration, and finally cost.
Before we do this, however, let’s take a look at a few of the benefits of predictive analytics for use in factories and manufacturing and see why you may want to invest in a predictive analytics solution.
Benefits of Predictive Analysis
There are a multitude of benefits to the implementation of predictive analytics in manufacturing, from preventative maintenance to demand forecast, the use of machine learning to interpret raw datasets obtained from connected devices, machinery, vehicles, and other tools and equipment located on site to improve and enhance operations is invaluable to manufacturers and factory operators.
Preventative analytics takes data readings from the numerous connected devices, machinery, vehicles, and other tools and equipment and uses them to determine whether repairs and maintenance are required and, if so, sends out an alert to notify operators and engineers of this requirement.
This then enables any repairs to be scheduled before a breakdown or malfunction occurs and can thus save time in reducing unexpected downtime and save money in repairs and help to extend the lifespan of equipment, vehicles and machinery.
predictive analytics can also be used to forecast future demands such as the types of products people will want, how much they will want, and when they’ll want it.
Using predictive analytics, manufacturers and factory operators are able to detect patterns and anomalous data that seems to re-occur during certain periods, enabling them to evaluate and prepare for such demands.
These are but a few of the reasons why manufacturers and factory owners are turning to predictive analytics systems and solutions in order to enhance and improve their performance and output. There are, of course, many other reasons as to why these systems are on the rise, such as the transition into smart factories.
Four Considerations for Choosing Your Predictive Analytics Solution
Now that we’ve gone over why manufacturers and factories are looking to integrate predictive analytics into their operations, let’s take a look at four of the biggest considerations for anyone looking to invest in a predictive analytics solution for their factory. Let’s start with quality.
1- Quality
When it comes to predictive analytics, you can’t always be 100% accurate 100% of the time. In some cases, accurate approximations are the best option and when this is the case, the quality of your predictive analytics solution will be the difference between accurate and disparate predictions.
When it comes to building an accurate learning model, it really is worth looking for providers that have put the time and effort in to ensure their machine learning algorithms are sustainable and have a proven track record with customers in similar industries to the one you find yourself in.
2- Application
Understanding the application you have in mind for your predictive maintenance system will give you a better idea of which service or solution providers will best suit your needs.
If you want a predictive analytics solution to power a preventative maintenance system, enquire with any potential providers about their experience and expertise in this area as well as any examples of how they’ve previously helped clients with preventative maintenance.
If your solution requires a large percentage of your existing systems to be reworked, you’ll need to consider whether the work is worth the result.
3- Integration
Leading on from solutions that require re-working existing systems, integration is a huge factor in choosing the right predictive analytics solution for your factory or manufacturing facility. As a starting point, why not check out your existing technology providers for potential solutions you could inquire with them about.
Another reason integration is so important is due to aspects such as future technological additions and smoother upgrade paths.
Any future technological additions will likely integrate better if the last addition is prepared for future upgrades and updates. With technological advancement occurring quicker than at any previous point in history, being prepared for future technologies is essential.
4- Cost
The cost of any predictive maintenance systems will always be a big consideration when choosing the right solution for you. The price of the solutions itself as well as any other additional costs, subscriptions or insurance will all need to be determined and evaluated against the budget with which you’re working.
Comparing the solutions and services you would receive from different solutions providers will give you an idea of what to expect for your money and to adjust your expectations accordingly. As with most things in life, you get what you pay for, so don’t totally reject the idea of spending a larger amount of money for a more comprehensive solution.
