OptiPeople and Omron: five kinds of data that make production more competitive

At a glance

In an interview made with Omron, Stefan Jensen, CEO of OptiPeople, goes through the data that helps manufacturers become more sustainable, both for the climate and for the bottom line. Five areas come up: energy, efficiency, quality, maintenance and cost.

The interview is in English and runs about 35 minutes.

Energy: more than kilowatt hours

Energy is not only kilowatt hours and CO2. Temperature, humidity, pressure and flow are energy and process data too, and they tell you whether production is running as it should.

Two things drive the interest. Energy prices spiked a few years ago, and now legislation is catching up. The demands start with the large companies and trickle down to their suppliers, who are asked how much CO2 or how many kilowatt hours went into a part.

A main meter often covers a whole factory. Submeters let you go down to a cell, a line or even a product, and they have become much cheaper and easier to install. The data is then used in four ways:

• Operations: spotting machines that use energy in an idle state when nothing is being produced.
• Maintenance: an alarm when a machine is outside its tolerance.
• Investments: a business case for new equipment that uses, say, 20% less energy for the same job.
• ESG reporting: facts per department or per product instead of one figure for the whole site.

Efficiency: how much of the shift the machines actually run

The data points depend on the industry, but a good start is simple uptime and downtime on machines, robots, lines or even manual workstations. From there you go a level deeper to the root cause of each stop, collected automatically or by hand.

Next is speed: if you planned 100 per hour and produce 75, there are 25% left to work on, as long as quality holds when you go faster.

Quality: the product and the process

Stefan splits quality in two:

• Product quality: cameras, vision and machine learning can check holes, colour, lids and tolerances automatically.
• Compliance quality: ISO certifications and environmental product declarations have to be backed up, for example by proving the temperature in production stayed within limits. Sensors collect it once, so nobody has to walk around reading values, type them into Excel and move them to another system.

Instead of measuring by hand, the operator gets a notification when something is outside tolerance, and spends the time on work that adds value.

Maintenance: from calendar to condition

Most maintenance is still planned by date. The front runners plan by running hours, vibration or power draw instead. A machine that has only run a few hours this period can wait, and one that draws 20% more power than normal moves up the list.

On continuous lines, such as food and pharma, process data matters most, because one breakdown stops the whole line. In order-based production with many separate machines, the data gives the overview needed to decide where to start.

Cost: what an order really costs

Finance and planning are now digging into production data, because it can show the real cost of a part, an order or a day. Cost has often been based on history or plans. With real start and end times per order you can see whether it took five hours or five and a half, how much of it was stops, and how many kilowatt hours went into it.

That is the basis for strategic decisions: which products to keep making, where to invest, and which order to produce things in.

Now is the time to act

The large companies already have this in place. Medium-sized and smaller companies are starting now, and those that act now will soon play in another league. The tools and the technology are there. What remains is to use them.