Using state-of-the-art CNC machines, Nobel Biocare produces precision machined components that are designed to last a lifetime. By improving response time to downtime, they increased output by $200K per month.
In response to strong demand, Nobel needed to seize the opportunity to increase output using their existing machines and people. From the data they had, they knew OEE was around 75%, but the specifics of “why” were elusive. They needed clarity on what was causing production downtime and a precise way to initiate improvements.
The company chose Raven to measure and improve their line production. The first step was to instrument 40 CNC machines with Raven tablets to capture a production timeline. Data from both machines and operators supported workflow changes, team training, and real-time alerts to systematically improve performance and reduce unplanned downtime.
Raven tablets were installed on 40 of the company’s CNC machines with a simple signal indicating when machines were making product versus when they were down. Initial tags on the tablets gathered operator context to differentiate between production, planned downtime, and unplanned downtime. The first three months data became the baseline. Raven Flight reports presented a timeline of production across all machines with a summary of time lost by category and cause of downtime. In the spirit of lean manufacturing, the focus was on continuous improvement. The company worked with Raven data scientists every month to study patterns that identified areas most ripe for improvement. The impact of changes to the workflow was measured and new baselines were established.
Nine months after Raven units were installed, and six months after improvements began, operating efficiency for the 40 machines had increased from 75% to 91%.
Increased Production Output
Reduced downtime meant a direct increase in product output. Each machine was estimated to produce $100 of value per hour. Output improved during the six months after baseline, to $204K of incremental monthly production. That projects to an incremental $2.45 million for the value of additional production over the next 12 months.
Reduced Unplanned Downtime
Unplanned downtime across the line reduced from over 1,500 hours per month during the baseline period to under 100 hours in the sixth month of improvements. The largest portion of observed time lost was in waiting: for maintenance, for programming, for materials shortages, or for operators to restart production after downtime. Refined tagging identified the sources of waiting, and process improvements were made for each. For example, waiting for maintenance and programming was reduced from 466 hours to just 33 hours.
Reduced Planned Downtime
The process improvements, which were applied to deploy resources more efficiently for unplanned downtime, resulted in improvements in planned downtime as well. Planned downtime for activities such as setup, tool change, and production switchovers was reduced from 793 to 577 hours per month.
Hawthorne Effect Improvements
From the time Raven was installed, the tablet’s red screens alerted operators to downtime, immediately improving response times. Likewise, the big-screen team displays showing the status of all 40 machines prompted supervisors to respond to material shortages or maintenance needs faster. This suggests the baseline measurement was an improvement from productivity before Raven was installed due to the Hawthorne effect (the awareness of measuring having an effect on measurement).
Improved Best Practices
The monthly reports identified which shifts and operators were consistent out performers. The company interviewed those team members to establish best practices and develop training to help all operators improve.
Based on the value of increased production, the company's return on investment in the first year was 24:1 and is projected at 50:1 for subsequent years.