This startup is using predictive modeling to save manufacturers millions

Business / Tech
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AUTIT is a strong example of how manufacturing plant managers could save money and avoid the hassles that come with unnecessary spare parts.

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Many fantastic business ideas like predictive modeling come about when their creators see problems or needs in the marketplace and feel ready to fill them.

Such was the case with AUTIT, a company founded by Paul J. Noble. However, the current vision for the business did not arise immediately.

A Powerful Pivot

During his time overseeing global supply chain management for Sherwin-Williams, Noble noticed a lack of visibility among the various parties involved with parts inventory — the manufacturers, suppliers and distributors.

Due to the inadequate transparency, increasing costs resulted, and none of the parties could maximize their value. Noble believed he had a solution.

Initially, AUTIT was a platform that facilitated sales. It was aimed at helping suppliers and distributors have smoother communications with manufacturers through analytics and automated proposals.

However, after talking to potential customers and getting their feedback, Noble realized many members of his target audience weren’t accustomed to using high-tech solutions within their workflows.

Noble recognized that his first business model had merit. However, the situation was frustrating in its polarity. Although potential customers heard about the idea and were enthusiastic about it, they also weren’t ready to adopt AUTIT into their operations due to the technical nature of the platform.

Last year, Noble and his team pivoted. Instead of focusing on suppliers and distributors, they set their sights on equipping large manufacturers to keep tabs on their parts inventory and run leaner facilities.

Expensive Downtime Makes Manufacturers Keep Parts on Hand

Manufacturers know if they don’t have enough parts to meet demand, their normally smooth operations could be compromised, leading to costly slowdowns. Noble’s experiences indicate the average Fortune 500 company has up to $60 million in working capital tied up in spare parts.

Some companies, such as Volvo, participate in additive manufacturing to create spare parts with 3D printers. Volvo takes that approach with some construction equipment, especially that which is so old, it’s no longer in production.

How Does AUTIT Assist Manufacturers With Reducing Parts Excesses?

AUTIT helps manufacturers in a way that’s different, but no less high-tech. The company’s platform utilizes artificial intelligence (AI) to help users analyze their parts inventories. It looks at historical data and checks for accuracy regarding currents parts levels.

Moreover, AUTIT eliminates the data silos that can result after mergers or acquisitions. The decentralization of data at many manufacturing facilities makes it difficult to view the sources speedily and simultaneously. AUTIT brings that information together for easy viewing.

Even more importantly, AUTIT connects parts inventories to return on investment. Then, business leaders can see how valuable it is to cut down on stores of unnecessary parts and only keep the most necessary ones on-site.

Made With Smart Technology

Best practices for inventory management include carrying out quality control checks and regular audits. AUTIT is an example of a technology that could reduce manual verification of the need for spare parts.

According to the company’s website, AUTIT uses AI neural networks to predict inventory levels across a business with 99.73 percent accuracy, potentially reducing working capital by millions of dollars every year.

The platform also uses data-cleansing methods that are superior to other techniques concerning speed and accuracy. Therefore, predictions get better with time.

During an episode of “The Take Over,” a business podcast, Noble discussed how he wanted to digitize the information that was traditionally in a static format, such as Excel spreadsheets. He saw how, due to the lack of good data or not having data at all, most organizations are “working off of a false sense of reliability.”

AI consumes a massive amount of data, then makes sense of it for the manufacturers using AUTIT. Traditional methods of working with data at manufacturing facilities are often segmented, but AUTIT attempts to bring more cohesiveness across data sources.

Another Way to Use AI to Prevent Shutdowns

Indeed, AUTIT seems like an innovative way for manufacturers to stop keeping so many parts on hand without ever knowing if they’ll be necessary.

There are other possibilities for using AI concerning components. For example, Hitachi recently introduced a predictive maintenance solution for petrochemical plants.

It also uses AI, gathering operating data about equipment and then checking those statistics for abnormalities that could indicate an impending breakdown. In tests, the system reportedly found signs of equipment failure that human operators missed.

If representatives from manufacturing plants want to be especially detailed in the measures taken to prevent catastrophes, using several kinds of AI technologies may be appropriate.

AI Could Reduce Costs and Headaches

AUTIT is a strong example of how manufacturing plant managers could save money and avoid the hassles that come with unnecessary spare parts.

It gives insights business leaders can act on with confidence and authority.

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