How to Lead in Cognitive Manufacturing (or how IBM won 4 Manufacturing Leadership Awards)
On June 13, Frost and Sullivan handed out the Manufacturing Leadership Awards.
IBM was selected as Manufacturer of the Year in the Large Enterprise category. The much-deserved award recognized IBM for establishing itself as a paragon of Manufacturing 4.0 transformation by innovating the use of advanced technologies such as machine learning but also by embracing the notion that leadership and a reskilled workforce will be necessary to drive digital transformation.
IBM Corp. in the Data and Advanced Analytics Leadership Category for its Watson Approach to Optical Quality Project.
IBM Corp. in the Talent Management Leadership Category for its Competency & Talent Acceleration for Advanced Manufacturing Initiative.
IBM Corp. in the Engineering and Production Technology Leadership (Singapore Advanced Manufacturing (Industrie 4.0) Initiative)
When IBM talks about Cognitive Manufacturing for electronics, there’s a credibility to it: because IBM doesn’t just do the algorithm side of things — we actively use these technologies. The reality is that very few if any other companies in the AI game are actually manufacturing electronics goods. We have the data, the processes and the people to understand these patterns. We practice what we preach and we do it in our own facilities.
When examining the traits of leaders for the 2017 Cognitive Manufacturing study for the IBM Institute for Business Value, we validated that the path IBM took to success was similar in our leader group.
- they had multiple advanced analytics and AI initiatives underway — they had already started to gain experience
- they had a definitive strategy for AI/cognitive manufacturing deployment and investment, they had specific desired outcomes in mind.
We also found that the leaders were overall more technologically savvy based on their investments but most especially those investments in IoT — which for them showed consistently high ROI. Taken together, these enable leaders to increase productivity or competitive advantage.
With visual inspection in particular, it’s important that deep subject matter experts do the algorithm training — for instance on a flat panel — to find a minute defect that the eye can’t detect but would cause problems for a consumer. It’s not just about the defect but the ability to inform the supply chain, the manufacturing plan and other facilities. Sensors and cameras in and of themselves don’t do that work. The connective processes that surround solid manufacturing efforts can, if the work is well understood.
Industry 4.0 applications need experience and expertise. That’s why the adoption curve can be challenging for those who are looking for an easy way forward. The technology isn’t really useful with the domain knowledge
Next, the leaders in our study also know that connecting equipment through IoT, enabling collaboration and optimizing processes allows them to be ready for the future: to be ready to manage multiple concurrent processes, to focus on systems that can configure themselves on their own and continuously learn.
We validated these findings again in the C-Suite study on IoT across a broader sample and more industries — they hold up tremendously well as the path to value.
Sure, algorithms are a huge part of the battle. However, understanding the work, the industry and challenges deeply contributes greatly to success in cognitive manufacturing.
For additional information, please see:
“Why cognitive manufacturing matters in electronics”, IBM Institute for Business Value
“Intelligent Connections”, IBM Institute for Business Value
The IBM Intellectual Property Management Solutions team is applying Watson technologies to IP use cases such as Evidence of Use, Prior Art, Landscapes and others. Contact me for further information.
Connect with the author, Cristene Gonzalez-Wertz
