The electronics manufacturing industry faces tumultuous times with increasing customisation, shorter lead times, frequently changing environments, and shrinking order sizes - all while managing a sophisticated supply network.
Thinner margins and increased competition threaten consistent quality, risk greater downtime and reduce desired flexibility. Investments in new equipment and automation systems are increasing the amount of data available from the shop floor, but most is not used to its full potential.
Now this is where cognitive manufacturing can make the cut - by helping organisations fully harness the data generated by the machines on the shop floor.
A tête-à-tête with Bruce Anderson, global MD, Electronics Industry, IBM highlights the current challenges faced by the manufacturing space, and how cognitive steps in to make the difference.
Why cognitive manufacturing matters in electronics?
What we pay for electronics has shrunken over the last few years. There's a reverse pressure on the manufacturing cycle.
Some companies actually exited the business because they figured they couldn't make money out of it. In the semiconductor manufacturing space, as designs got more and more complex, you had to get better and better at the manufacturing side.
Anderson believes that to strike a balance between manufacturing costs and production overheads, automation and data helps equally. The companies that survived did so solely because of the way they handled data.
"But what they're doing now, is that they're taking dark data - data that's stuck in the machines. So, the idea is to get that data out, put it into an analytical environment, derive insights, and then act on it fast enough to make a change in the manufacturing economics," adds Anderson.
What's changed is the amount of data which is now available, and the speed with which you can make sense out of it.
Sensors installed in machines collect data, and the data is then stored in either the datacenter or the cloud, and no one ever gets any insight out of it. It's just a complete waste, opines Anderson.
What can cognitive manufacturing achieve that conventional methods of manufacturing cannot?
Let's take conventional approach - you take data set, take that back to your office and work on it. And then you come up with ideas on what can be changed in the manufacturing process. Let's be generous, and give this process a cycle time of 24 hours. So, for 24 hours, that problem still existed in the manufacturing cycle. During this phase, you've lost throughput, yield, and quality.
Also, the data that they were looking at, was by our estimates, only half the available data. It actually all boils down to the dark data - what did you not know, that could help you come up with a better answer.
Sign up for CIO Asia eNewsletters.