Using AI-Enabled Predictive Maintenance in the Supply Chain

ODSC - Open Data Science
4 min readAug 17, 2023

Whether you’re a delivery driver, help package products, or are one of the first links in the supply chain of worldwide goods, something is always bound to break — often when you need it most. Traditionally, there were just two maintenance methods to ensure everything ran smoothly. Now, using AI in predictive maintenance offers a new way to conduct repairs, helping everyone in the logistics industry work with fewer incidents.

Explaining Different Types of Maintenance

There are three main strategies for performing maintenance along any part of the supply chain.

  • Reactive

Reactive maintenance means waiting for a device or system to fail before performing repairs. For example, if you’re a fleet manager, you might wait until a truck breaks down to take it to a mechanic.

This form of maintenance is often costly, leads to more downtime, and can even cause accidents. But equipment breakdowns can happen unexpectedly, so it’s still necessary to conduct reactive repairs occasionally.

  • Preventive

Preventive maintenance involves giving devices a tuneup long before they reach a breaking point. Maybe you inspect your factory twice a year to prevent accidents or device failures from occurring.

Although it’s better than just reacting to problems in real-time, this scheduled maintenance is somewhat arbitrary and may not be the most efficient strategy. You might inspect the factory too often, which can lead to unnecessary expenses, or not enough, which can cause failures.

  • Predictive

Predictive maintenance is a cost-effective way to — as the name suggests — predict when you need to conduct maintenance. Using AI in predictive maintenance helps minimize downtime by measuring actual operating conditions, usage, and equipment feedback to decide when to perform a tuneup. AI software looks at current and past performance to alert you to potential problems.

Combining Methods

Eighty-eight percent of manufacturing industries used preventive maintenance in 2021. Many used a combination of techniques, with 40% using analytical tools and 52% using run-to-failure management.

A program that integrates all three methods isn’t always the most cost-efficient. Still, it is vital for specific industries — like medical device manufacturing or nuclear power — that can’t afford failures. It can improve worker safety and ensure operations run smoothly.

The Role of AI in Predictive Maintenance

Predictive maintenance uses sensors that detect qualities like temperature, vibration, and sound, even at ultrasonic levels. Changes in these metrics often indicate stress, friction, misalignment, or wear that could cause an equipment breakdown. Algorithms can then estimate how soon a device may malfunction.

AI software can also analyze large amounts of information to find patterns, optimize inventory levels, and predict product demand. It enables businesses to save as much as 15% on costs associated with stocking too much or too little inventory.

One example of using AI in predictive maintenance is vibration analysis. Suppose your factory’s AI software monitors a centrifugal pump motor’s normal vibrations. It issues an alert when the vibrations deviate from the norm since unusual patterns could indicate an upcoming failure.

Consequently, you schedule an inspection and find a loose ball bearing in the pump. Replacing the entire pump would mean scheduling significant downtime while fixing the ball bearing takes just a few minutes. It allows the factory to keep operating and means you don’t have to send workers home for the day.

Effects on the Supply Chain

Using AI in predictive maintenance improves every link in a logistics network. Truck drivers can service their vehicles before encountering mechanical problems. Factories can replace aging conveyor belts to keep products moving smoothly down the line. Warehouse operators will know exactly when to order more materials, ensuring there are no snags in the supply chain.

It’s no wonder experts think the predictive analytics market will be worth $34.52 billion by 2030. AI-based predictive maintenance is the future of supply chain management.

The Rise of Predictive Maintenance

Reactive and preventive maintenance have their place. However, using AI in predictive maintenance helps nail the timing when performing repairs or placing orders. It saves operators time and money by keeping equipment up to date, preventing failures, maintaining worker safety, and complying with regulations.

AI-based predictive maintenance is still a new technology relegated chiefly to large companies. But as it matures, it will become more accessible and will likely be a mainstay of supply chain operations. This is just the beginning.

Originally posted on

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