As modern technologies redefine manufacturing, regional experts stand to gain in big ways—particularly when it comes to data visibility. New technologies are instigating a shift towards greater data accessibility. These regional experts whose job is to fix problems and improve efficiencies will have more context and more information to work with. Rather than gathering data on a project-by-project basis, regional experts can now operate on the right data, always readily available, moving their organizations a giant step forward toward realizing an Industry 4.0 environment.
What is data visibility?
Facility-wide access to raw machine data is the core of data visibility. When analyzed and presented within the context of factory operations, it represents a deep pool of knowledge.
Factories with poor data visibility force their teams to preplan when working with data. This means that they are limited to asking a small number of questions while looking at limited information.
This also adds an additional trial test for validation on the next production run—adding to downtime and possible defects. Factories with better data visibility can examine detailed historic data and distinguish valuable insights.
Manufacturers that implement new technologies and invest in good data visibility free up the experts tasked with increasing efficiency across factories. They can use good data visibility to ask new and novel questions and find unique insights into problems. These freedoms translate to quicker solutions and less downtime.
Supercharging the skills of regional experts
These new abilities naturally pair with the focus of regional experts on cross-sections of factory performance and best practices.
Metrics that can be improved with better data visibility include:
- Utilization
- Part attrition
- Waste
- Postproduction (test)
- Part inspections
- Quality
More data and context translate to better performance. These are powerful for a single factory, but when the activities and data of multiple factories can be compared, and solutions replicated across all sites, it becomes even more significant.
Changing perspectives on problems
Raw machine data, fed into a central system through new technologies, also frees experts to tackle even the most detailed problems. Typically, the time and money spent solving these challenges would be a waste. Even the most experienced line managers and production oversight teams would be hard-pressed to find their root causes.
Data visibility tackles this head-on by dramatically lowering the barriers that obscure the root causes. Industry 4.0 tools put the expert on the production floor, providing remote access to both the real-time raw data and complete detail of production history.
When experts can ask the right questions, manufacturers see rapid gains in their productivity by solving small problems efficiently.
Consistency opening new doors to productivity
Another boon for regional experts provided by data visibility is consistency. As raw machine data is collected automatically, it does not go through processes that remove or obscure some parts of it.
This removes potential issues that can cause data to become flawed, or misleading over time. While line operators are good at their job, they are still humans. There is a limited amount of data one person can collect, and without automated monitoring, they can miss things. By adding the right alerting mechanism based on expert experience, anomalies can be detected within large amounts of data and operators can be notified when issues occur.
Incomplete or erroneous data collection may be a small factor on one machine, but over multiple machines and factories, errors in the data collected become glaring inaccuracies. Inconsistent data is also a major barrier to finding problems and identifying their causes.
Replacing the email thread and the spreadsheet
For instance, say that a regional expert is trying to compare productivity in two factories making the same product. They wish to know why Factory A outperforms Factory B on weekly production numbers. Using traditional methods, this project could take several months due to the time it takes to collect data and analyze it, usually with threads of emails and shared spreadsheets. They would need to account for each line in both factories and then figure out the differences in procedures to make an accurate comparison. Small errors in data would make comparisons difficult because it would force experts to accept margins of error that may obscure the differences. That’s all before finding the reasons why Factory A is more productive in the first place.
Collecting all of the raw machine data means that it is usable in real-time and is always consistent—removing guesswork and time-consuming projects. Regional experts can dig into current data, comparing machines within factories and quickly seeing what they need. It allows those looking at the data to rewind the clock. They can also ask questions of the data pool that would have been impossible before. They could compare efficiencies during shift changes, the software the machines use in each factory, or any other number of metrics. Because this information lives in the cloud, they also don’t need to travel or call meetings with stakeholders. A project that once took several months can be completed in only a few days.
The next level
It takes people with both domain and data expertise and proper insight to oversee production and improve efficiency. With good data visibility, these skills and insights can be taken to the next level—finding solutions to problems and efficiencies on the factory floor that are otherwise invisible costs and inefficiencies. Manufacturers who invest in new technologies that support good data visualization are also equipping their regional experts with the tools they need to reach the next level.