Danaher Augments New Talent To Influence OEE in 3 Steps
Danaher is Driving Change Using Raven.ai
For more than 100 year Danaher‘s dental division has developed innovative and high-quality dental products. With a focus on team cohesion and efficiency, its Morrisburg, Ontario plant produces dental burrs almost 24/7.
The Challenge: Increasing output to meet demand
Danaher relies on a symbiotic relationship between operators and machines. To keep up with demand, machines need to run almost every minute of every day. Maintaining high levels of uptime and Overall Equipment Efficiency (OEE) is critical to make sure orders are delivered on time.
“We needed data to have more informed discussions internally about downtime,” says Dave Martin at Kavo Kerr. “We wanted to look at the data together, as a team, to find out where the losses are and how we could improve.”
Dave’s plant was already performing at a high level, recognized corporately as a top performer. Their solid performance was driven by a talented and experienced shop floor team, committed leadership as well as Danaher’s commitment to investing in their continuous improvement culture.
To leverage their strong continuous improvement culture to meet customer demand, Kavo Kerr decided to implement Raven’s Augmented Management Platform to uncover insights into reasons for downtime and learn more about losses.
The Opportunity: Uncover new opportunities for improvement from the data
Before beginning their project there were many theories about why some lines and operators were able to perform at a higher level than others. There were theories that some operators always got the easy jobs, some had better machines, and some operators “never went on break”. Part of the goal for implementing Raven was to understand what enables top performance.
The foundation of any data driven system is to establish performance truth which is obtained by combining data from machines, operators and the ERP. Not only does data need to be combined, it also needs to be cleaned and validated — many digital initiatives quickly lose the confidence of plant teams because of poor data quality.
Raven installed interactive tablet computers on 150 machines to capture output from the machine as well as downtime and job context from the operators. Large interactive tablets were installed at the end of each line giving the operators a tool to see real-time and historical performance during their daily stand-ups. Operators and shop floor leadership were involved in the configuration of the tablets to minimize operator burden, validate that the data collected was trustworthy and to create a high level of engagement.
As the teams began to fully integrate real-time insights from their data into their daily work trends began to emerge highlighting real opportunities to improve. The two biggest opportunities uncovered were
- There was a significant drop in performance in the 30 minutes around shift change
- There was significant variability between operator setup performance.
High levels of operator engagement are critical for any improvement initiative. Operator representatives were to monitor each machine and give real-time insights. Touchscreens allow operators to identify issues immediately and escalate when needed. Custom analysis from a dedicated team of data scientists identified opportunities for further improvement.
“We find tremendous value in the insights from Raven’s data scientists. They help by going deeper into the data and providing recommendations,” says Martin.
The Impact: Achieving new levels of performance by combining talent with technology
Within 12 months of integrating Raven, Danaher increased uptime from 78% to 92%. More importantly, however, augmented management given the Kavo Kerr team the insights to have rich discussions about best practices. “It shows us what’s really happening,” says Martin. “We have better conversations to solve problems together.”
“With Raven our operation increased uptime to 92% from 78% – saving hundreds of thousands of dollars annually.” says Shannin Hudson, Plant manager.
For this busy manufacturing company, the impact goes beyond data science; it’s about supporting the individuals on the team. “As soon as we make the reasons for downtime visible, we can provide help,” says Martin. “The machine’s performance is a known entity, so it’s about supporting our people, collaborating, and learning from each other.”