3 Ways AI Makes Business More Predictable
The future of AI ranges from self-driving cars to robotic overlords, on a varying scale of useful versus questionable possibilities. With the rise of digitization, we’re gathering more and more data that, if used to its full potential, will help businesses counter uncertainty and make business outcomes more predictable.
Nowadays, companies face countless challenges — inflation, supply chain delays, natural disasters, and global pandemics. The most valuable part of AI is its ability to take in huge amounts of data and calculate every possible outcome, then make recommendations based on a variety of parameters. It can also offer solutions to lessen these problems without the need for human interference. Combined with a fully integrated end-to-end ERP system, AI can be a critical factor in streamlining business processes.
Here are just a few of the ways organizations are currently using AI to navigate all the challenges being thrown their way.
1. Predictive Asset Maintenance
In an in-person industry like field service, scheduling is key. Knowing who should be where and when and deploying them at the right time (with the right equipment) is critical to achieving the optimized outcome. To ensure all these pieces are in place, AI takes in a set of conditions and objectives to predict where everyone should be and what spare parts are required, using a combination of historical data and information — such as the typical lifespan of a piece of equipment and when it was last replaced — with IoT data about things like the current state and usage of the equipment to calculate the precise state of each cog in the system.
For instance, with the help of AI, field-service organizations can perform necessary maintenance on a wind turbine part before it fails to ensure power generation isn’t interrupted. By using historical trends and weather patterns combined with information from sensors on the equipment and forecasted supply chain delivery times, the maintenance team can preemptively place orders for spare parts and perform the required maintenance work in advance.
On the flip side, AI can also ingest an alarm signal from a piece of equipment after it’s gone down, then analyze the previous work for that type of equipment combined with that particular fault code. Based on that history, AI determines the parts needed for the repair and can submit the work order immediately. This allows the crew to be proactive in avoiding outages, as well as eliminates the need for a diagnostic examination before they can repair the equipment after an outage, drastically reducing costs and downtime for businesses.
2. Combining with IoT Data
As more systems adopt IoT sensors, they’re able to collect swaths of data that haven’t been used to their fullest before. One of the most prevalent uses of this data is in end-to-end solutions, where it becomes a pipeline of technical info that can provide a holistic overview of the business, and when it’s integrated with the right tools, it can even be used to manage the business. Putting AI at the core can permeate all other services to streamline operations.
Currently, most manufacturing AI is in its infancy. There are a lot of IoT sensors on intelligent machines, but the data piles in factories without being connected to the business outcome. Most IoT platforms are fairly good at filtering data by nanoseconds and producing trending info; a complementary AI/ERP tool can use the trending info, attach machine learning algorithms, apply filters, and analyze the resulting data.
When trends start to emerge and the AI can make accurate predictions, integrating it with an ERP tool can then allow for automated workflows without the need for any human intervention. If a business has invested time and money into installing these sensors and collecting the data, it only follows that they should use it to drive outcomes, otherwise it becomes limited in the value it can deliver to the overall business.
3. Automated Manufacturing
Some people might fear a future in which robots have taken over our jobs, but automated robots are already being used in many industries. Robots are extremely valuable in manufacturing, for example. In one interesting use case for a combination of AI and IoT data we saw during the pandemic, a manufacturer was lacking sufficient manpower on the factory floor due to the quarantine requirements. To fill the gaps, the team turned to automated guided vehicles to drive around the factory floor.
With the help of an AI-enabled ERP system feeding it information, the automated vehicles knew where to drive and what parts to retrieve and quickly became part of the manufacturing process. However, just having an automated vehicle didn’t solve the problem until it was fully integrated into the operation. COVID has driven the need for automated manufacturing due to the quarantine conditions, but AI and AI-enabled robots present promising alternatives for high-risk jobs where people often face unsafe conditions in addition to solving labor shortages.
As the world churns out more and more data — whether it be from the thousands of IoT sensors, from shipping data that shows delivery times of raw materials and parts, or weather conditions from meteorological stations — AI is maturing at a perfect time to help humans make sense of all the information, sorting out the signal in a sea of noise to make actionable decisions. With proper AI configurations, businesses with an AI-enabled, ERP-integrated operation can finally have full visibility of their business and streamline their operations.
