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Beware of La Niña: Using AI and Analytics To Prepare for Disruptions

The National Weather Service forecasts increased hurricane activity, emphasizing the importance of predictive risk management strategies to mitigate potential disruptions to business operations.

ITPro Today

April 4, 2024

4 Min Read
view of tropical hurricane eye from space

From wildfires and hurricanes to atmospheric rivers and winter storms, recent years have brought an abundance of extreme weather events and put climate at the forefront of the American consciousness. Between the “Great Texas Freeze” and the Canadian wildfires that turned the northeastern US sky an apocalyptic orange, people across the country struggle every year to prepare for the next unexpected weather-related crisis.

In February, the National Weather Service announced a La Niña watch, indicating that this fall could bring more hurricane activity than normal as El Niño weakens and ocean temperatures cool. With the current El Niño driving unusually high temperatures in the Atlantic Ocean, the climate cycle could lead to a very active hurricane season.

A potential La Niña season could wreak havoc on Hurricane Alley, the area across the Atlantic stretching from the Gulf Coast to northern Africa where many hurricanes form, with cascading weather effects rippling across the world. Although the watch issued by the National Weather Services serves as more of a potential warning, weather patterns are affected by a variety of factors, and La Niña is just one player. It’s never a bad thing to prepare for the worst and hope for the best.

Natural disasters can be detrimental to business – fortunately, technology can help.

Related:Why AI and BCDR Are a Natural Fit

With the threat of potential disruptions in operations and supply chains, enterprises should adopt predictive risk management strategies to get ahead. AI in particular is a helpful tool for analyzing a company’s assets, predicting when spare parts or maintenance will be needed, preemptively purchasing supplies, and scheduling fixes as the odds of severe disruptions increase.

Optimizing the Supply Chain

Supply chains are centered around getting the right materials to the right place at the right time. For the past few years, as supply issues abounded and it felt like something new happened to disrupt the supply chain every month, companies put a greater emphasis on increasing their storage to ensure they had enough supply to meet demand. More recently, companies have returned to just-in-time inventory, trusting that the supply chain will be able to meet their demand in a quicker time frame. Not too much, not too little.

The good news is that algorithms for demand planning in supply chain management are getting more sophisticated, with the ability to take into account weather forecasts and historical demand data for more accurate planning. For example, seasonal patterns are a prime factor for supply planning. Say you’re selling propane tanks, you already know that holidays like Memorial Day and the Fourth of July are big days for grilling and backyard barbeques. If your planning algorithm has weather data, you can also tell where it’s going to be sunny on important days, where it might storm and reduce demand, and what supply lines will be affected to plan your operations more efficiently.

The addition of this kind of criteria can – and should – impact the movement of supply from one step to another. Predictive risk management is becoming more essential to business operations, as AI and analytics have enabled more accurate forecasting and can be a big difference-maker for companies looking to minimize any potential disruptions brought on by extreme weather events.

Optimizing Enterprise Asset Management

Another area where AI is making waves in enterprise technology is predictive maintenance, which helps lower maintenance costs and keep unplanned downtime at a minimum. The same weather data used for supply chain planning can also be used to pre-empt potential disruptions in field equipment or on the manufacturing floor based on a mix of historical maintenance information and IoT sensor data.

Past maintenance requests reveal a lot about an issue, including what supplies are needed to address a specific problem and which technician is best at fixing the issue. AI can be used to monitor real-time data on the state of the equipment and initiate a maintenance request if there are any anomalies. It can also tell if, for example, there’s a storm on the way and a piece of equipment is at risk of failure, or if there’s a low supply of a crucial tool needed for business operations.

We don’t often get warnings about potential disruptions, which span boats getting stuck in narrow canals and blocking international trade lines to flash floods, so this season is a rare opportunity to make sure enterprises are prepared for an uncertain future. Hurricane season, as with any other natural disaster, isn’t to be taken lightly. Now is the best time to evaluate your enterprise asset management and supply chain planning strategies and get ahead of those rainy days.

Kevin Miller is the chief technology officer of IFS in North America.

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