As many other industries struggle to define what it means to them, manufacturers already have a strong idea of what artificial intelligence can do within their operations. The industry’s dependance on automation already makes AI in manufacturing a great fit, thanks to its potential for managing processes as efficiently as possible while also providing adaptability. One of the key areas in which the manufacturing sector can harness AI to its fullest is for scheduling maintenance activities.
There are multiple forms of AI for maintenance scheduling that can be integrated into your procedures. Machine learning enables your system to pay attention to past activities and make decisions based on that information. Predictive analytics examines trends and makes predictions about what is most likely to occur in the near future. Finally, Internet of Things (IoT) integration allows equipment to communicate with each other and create a real-time, holistic view of your entire facility. All these elements come together in an effective maintenance scheduling AI program.
How AI works in maintenance scheduling
The integration of AI into manufacturing equipment maintenance typically consists of three main elements:
- Data collection — An array of sensors connected to equipment monitors conditions including temperature, vibration and other factors that can impact the health of machinery. At the same time, IoT-enabled machines keep up a constant stream of communication between themselves, sharing information about their performance and status. AI platforms scrape these data sources and compile records that give the software a comprehensive picture of what the equipment is doing and what might be affecting its condition.
- Data analysis — The meat of AI for maintenance scheduling is here, where advanced algorithms analyze patterns in the data collected by the software. Based on the historical information, the program spots trends and learns when equipment will be in most need for specific maintenance tasks. For example, the platform can determine when a particular piece of equipment will need lubrication based on its level of use, environmental conditions and past maintenance history.
- Scheduling optimization — After analyzing all the available information, AI can generate an optimized schedule for maintenance based on its predictive insights. Technicians can use this schedule to ensure each piece of machinery receives the appropriate level of care at the most optimal time.
Steps to implement AI for maintenance scheduling
Manufacturers who want to harness the power of AI to streamline their maintenance programs must be aware of the all the steps involved. It isn’t enough to simply invest in a platform and hope for the best. A successful implementation of artificial intelligence requires a good deal of planning and forethought. Here are the steps that go into a proper integration of AI into a manufacturing maintenance plan:
1. Assess your current maintenance processes — This means taking a close look at how you schedule and perform maintenance tasks in your current operations. Pay close attention to any areas that appear to be lacking in effectiveness or take more time and resources than they should.
2. Choose the right AI tools — There are numerous platforms in the marketplace today that offer AI functionality. Choosing the right one for your needs will come down to factors such as your budget, your needs and your existing suite of software.
3. Data integration — When you’ve selected the AI platform that fits your situation, it’s time to work on integrating it with the systems and databases you already have in place.
4. Training and development — Your maintenance staff needs to be brought onboard with the new system as early as possible, so they have time to acclimate to it and make it a part of their everyday procedures. Otherwise, it’s unlikely you’ll ever experience the full benefits of AI.
5. Continuous monitoring and development — Despite what some might think, AI isn’t a “set it and forget it” type of solution. You should be keeping a watchful eye on how your system performs and track its effectiveness. Adjustments may need to be made depending on the results you experience.
Benefits of using AI for maintenance scheduling
If you implement it correctly, an AI-enabled maintenance program should be able to drive improvements in your operations that include:
- Increased efficiency — Your technicians won’t be wasting time and resources performing tasks that aren’t as necessary at that exact moment.
- Predictive maintenance — A platform using predictive maintenance AI can help you anticipate equipment failures before they occur and take steps to prevent them.
- Cost reductions — With less downtime and optimized scheduling, you can save money across the board.
- Data-driven decisions — Real-time, data-driven manufacturing enabled by your system gives you the power to make better-informed choices about your equipment.
Although other industries may be having a difficult time fitting AI into their strategies, manufacturers have a ready-made use case that can boost their productivity to a significant degree.
