UMBRAGROUP, a long-time IBM client, approached the company for help, and it put the manufacturer in touch with IBM Business Partner Nodes.
Working together, UMBRAGROUP, Nodes and IBM produced an initial proof of concept (POC) that used Watson™ technology to derive new insight and understanding from the company’s production data. The Nodes team, in particular, evaluated UMBRAGROUP’s various back office systems, identifying its ERP and manufacturing execution system (MES) as particular focuses.
“The first step,” explains Guillaume Ammassari, Sales Director at Nodes, “was to make sure that we had the right data. If you want the algorithm to work correctly, you need good data. Otherwise you’re going to have a problem.”
And with the appropriate systems identified, the Nodes team then cleaned up the data, placing it in a usable format for the analytics and prediction engine.
After a successful POC, UMBRAGROUP worked with Nodes and IBM to create a more robust, long-term version of the platform. Watson Studio serves as the heart of the solution, analyzing and drawing conclusions from the existing data as well supporting the build of new models and prediction processes.
“From analyzing the data, we came to some very unexpected conclusions regarding our processes that we never considered,” notes Bernareggi. “For example, we discovered that after we set up specific tooling, the first lot produced reported a relatively high percentage of non-conforming parts. Consequently, we changed operating processes and that drove dramatic improvement.”
He continues: “We also noticed that a lot more non-conforming parts are probable if we stopped certain machines (where extremely tiny tolerances are allowed) and restarted them a half hour later. Now we try to avoid stopping these machines during the day for whatever reason. So rather than shutting down for lunch, we stagger shifts for continuous work.”
Beyond this initial analysis, UMBRAGROUP continues to use the solution to monitor current production efforts and to mitigate future waste. “In the toolkit, we have a way to analyze constantly,” adds Bernareggi. “Every week we check our data to see if there is something else that might pop up that we did not consider. Or if we plan to make a change, we can model how much scrap that change might cause.”