Producing change with leading indicators, AI and IoT

By focusing on leading rather than lagging indicators, manufacturing businesses can use technology to dramatically change the way in which their factories work

Saar Yoskovitz
By Saar Yoskovitz on 11 October 2021
Producing change with leading indicators, AI and IoT

Artificial intelligence. The internet of things (IoT). Data lakes. The manufacturing industry is fixated on vague technological terms. While new technology is crucial, it’s the behavioural and organisational change that follows which truly brings about digital transformation.

IoT and artificial intelligence (AI) will fundamentally change the factory floor by providing manufacturers with real-time, leading indicators. Today, most manufacturing processes rely on lagging indicators. If the scrap level on a production line goes up, indicating that products aren’t passing quality control, the team must check individual parameters and sensors to find out what went wrong. With a leading indicator, they get early warning that quality will degrade and can address the cause immediately, before scrap levels rise.

Other industries changed radically when technology gave them access to leading indicators. In sales and marketing, the conversation used to revolve around the number of leads and top line revenues. Now we measure the number of times a prospect viewed a particular web page, the number of questions she asked during a sales call and the time it takes her to progress through each sales stage, in order to fine tune the process at every level.

Sales and marketing teams were often at odds. Leading indicators have aligned them around common KPIs and goals, and enabled cross-­functional collaboration unimaginable a mere decade ago. New roles like sales development and customer success emerged, along with an entirely new industry to support them – revenue operations – which is now worth billions of dollars.

We are about to see the same transformation in maintenance, reliability and operations. Today, a conflict may arise between a maintenance technician who wants to perform scheduled preventive maintenance tasks and an operator who is under pressure to meet a strict production schedule.

What would happen if they had a common set of machine health and performance insights? A small change in vibration or temperature on a motor can indicate a future machine failure. Maintenance and operations can now decide together whether to address the problem today or delay mitigation in order to meet production targets, depending on the risk profile.

When teams align, new roles are created that require a wider understanding of the business. A maintenance technician will need to understand how a degraded bearing can affect production planning. His role changes from complying with a predefined maintenance schedule to managing operations risk using AI-based probabilities.

Nothing is more exciting to me than seeing this transformation take place before my eyes. Leveraging our Machine Health solution, leading manufacturers have started to schedule daily stand-ups for maintenance and operations teams and define new roles like Machine Health specialists.

At Augury, we see our role as more than selling a solution. We want to be a partner to manufacturers as they navigate this organisational change.

Saar Yoskovitz is co-founder and CEO of Augury 

This article was originally published in the Autumn 2021 issue of Technology Record. To get future issues delivered directly to your inbox, sign up for a free subscription.

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