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The real obstacles on the road to smarter factories

23 June, 2020

Many manufacturers are not enjoying the full benefits of digitalisation, partly because of the difficulty obtaining real-time data from their plants and machines. In this guest column, Jan Hemper, technical director of Gambica* member, InControl Systems, argues that such barriers can be overcome by closer cooperation between SIs and suppliers.

My biggest challenge is connectivity,” said a manufacturing solutions architect who works for a household name in the food and beverage sector. This sentiment is echoed by many manufacturers, who are desperate to enjoy the benefits of the latest software tools for improving productivity, maintenance and their processes. In short, the managers want the attractions that these tools offer, but connected to the real-time sensor data from their machines and processes.

For example, there are plenty of companies that sell maintenance planning software that runs on smart tablets, but still relies on manual data input, rather than obtaining real plant data directly from the sensors on the machines.

The 2017 UK Made Smarter Review listed 31 obstacles to adopting industrial digitalisation. At positions 3 and 6 were “lack of certainty to the solution working” and “legacy infrastructure”. At number 2 was “lack of skills to design new systems and implement”.

My experience from manufacturing sites supports these views. While number 2 is solved by finding the right partners to work with (barrier number 4), the challenge of connecting to legacy controls and machinery needs to be overcome to provide confidence that the solution will work.

In my view, “Made Smarter” is a more relevant term than “Industry 4.0”, which often frightens business leaders away from adopting digital systems that converge OT (operational technology) with IT (information technology). We prefer to start with the business need, rather than the technology available. If the business need is to reduce waste, then you can improve the process before waste is created. Or to understand when and why a bearing is about to fail, so that it can be replaced and a root cause can be understood. Then we can use technology to collect that data in real-time and present it to the people who need it.

Too often, business managers take siloed approaches to their needs: planned maintenance management is one project; OEE (overall equipment effectiveness) is another; statistical process control is yet another; as is shop floor visual management.
Yet to make these capable and AI-enriched systems perform at their best, they all need access to the same equipment. So why treat them separately?

How many factories have you seen with challenges such as this? Managers and business leaders need more data at their fingertips to know where best to allocate resources to drive down waste and energy, to increase uptime, and to boost productivity and profits.

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