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Investor blog

Time to capture the silent knowledge of machine whisperers

by Ergin Tuganay, October 23, 2017

One day, Paula came to work only to see that the production line was temporarily shut down. Something had gone wrong with the machine, but no one had seen it coming. Well, one person might have.

One day, Paula came to work only to see that the production line was temporarily shut down. Something had gone wrong with the machine, but no one had seen it coming. Well, one person might have.

A week before the factory faced a forced shutdown that cost tens of thousands of euros, Martin, the factory’s maintenance chief, had taken one last look at the machinery and then walked out the door. After working for decades in the factory, he finally retired. And when he left, the company lost something they didn’t even realize they had: the silent knowledge of a machine whisperer. You see, Martin knew everything there was to know about interpreting the equipment’s data without ever seeing the actual data collected by factory’s many sensors.

Factory workers around the world are growing older; we’ll soon see simultaneous retirement of a large group of experienced workers. At the same time, we live in a data-driven century, in the middle of a digital industrial revolution. If you think these things won’t affect your business, think again.

People in production, quality and maintenance departments are the ones whose productivity grows the longer they do their jobs. When this group retires, industries around the world will face a problematic skills shortage while being in the midst of adapting to the new reality of Industry 4.0. This wave of retirement is about to begin.

However, people retiring is not your only problem. There is another big issue. Earlier this year, The Economist crowned data the most valuable resource in the world — the new oil. This means industries that are not able to translate machine whisperers’ knowledge into actual, visible data, will most likely go bust.

The good news is, this silent knowledge of the senior workers can be captured and translated into a data language and visualized on a dashboard. Nevertheless, to get to the level of predictive analytics that guys like Martin seem to conduct just by placing their hand on a machine and listening to the sounds, you need to take full control of the data your factory produces, collects and stores.

You need a solid, holistic data strategy and a digital innovation that facilitates connectivity, communication and collaboration as well as the creation of a digital twin of your factory. This will result in the ability for the entire production process to adjust in real time, automatically, to unexpected circumstances, guaranteeing “zero unplanned downtime” at the production system level.

Let me assure you, . But if you manage to digitalize that knowledge, your factory will start working in a more efficient way and you can significantly reduce the time needed to onboard younger employees.


Don’t miss out on your opportunity to be ahead of other industries.

I’ll be talking about how to capture the silent knowledge of machine whisperers at an international conference Manufuture 2017 – Moving up the Value Chain on October 24 in Tallinn, Estonia. Nortal will also have a stand at the conference. If you see that this is an issue for your industry, drop by our stand and let’s see what we can do to help your factory take the leap into Industry 4.0.

Ergin Tuganay

Ergin Tuganay

Partner, Head of Industry 4.0

Ergin Tuganay, Partner and Head of Industry 4.0 at Nortal, has 15+ years of experience in a wide range of areas in industrial automation and data-driven technology. By combining a business development and leadership background, he has helped several globa-scale industrial clients digitalize their manufacturing operations. Ergin’s secret weapons are in-depth, hands-on competency in traditional OT (PLC, DCS, SCADA, OPC, MES/MOM) and his knowledge of the latest IT architectures such as Edge, Cloud and Big Data. Among others, Ergin has a certification of competencies in MES/MOM Methodologies and Microsoft Azure.

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