Manufacturing generates more data than any other industry.
With the increasing affordability of industrial sectors, cloud, and other connectivity-focused technologies, manufacturers have more opportunities than ever to turn their operational data into a competitive advantage. But on many shop floors, reality doesn’t align with potential.
Many manufacturers still record data on whiteboards and clipboards. They still process their data in Microsoft Office.
We’ve written about the historical and industry-specific barriers that contribute to slow technological adoption. But the question remains: why do manufacturers still use Excel?
Here, we’ll show that there’s a better way.
Tracing the Data Chain
The fact is, many manufacturers still use Excel because it is readily available. It’s the universal language of data analysis, so to speak.
Excel is already installed on the vast majority of business computers. It is an interface most people understand.
But when it comes to processing manufacturing data, Excel is far from the ideal.
For one, Excel can’t provide real time insights.
Consider how information about a manufacturing process makes it into Excel. First, an engineer needs to record the data. Often, this is done using a mixture of manual methods. Just as often, clipboards or whiteboards are involved. If you’re trying to compare the performance of different departments or machines, then you’ll likely need to gather information from different silos.
Then the information needs to get entered into Excel. If things are really organized, you can upload a .CSV, do a little cleaning, and be ready to go. But more often, this means manual data entry. Entering data by hand is far from the best use of valuable time, and it’s prone to error.
Once the data is entered, you might be ready to produce insights. Might.
Chances are, however, that manufacturing processes are too variable and the data too incomplete to produce insights that speak to root causes and performance truths. How do you account for processes that require customization? Or for products and processes that have multiple variants?
Ultimately, data collection needs to be fast, error proof, and flexible enough to handle variability in manufacturing processes.
What Real Time Data Looks Like
With the right tools, manufacturers can cut the data chain turn the execution of processes into useful information.
Tulip gives manufacturers real-time data at the source.
With Tulip’s manufacturing application platform, you can turn routine processes into data. As operators move through processes, the platform automatically captures performance information. With edge computing and machine monitoring, the data machines generate is naturally and automatically converted into valuable information.
And because the platform automatically collects performance data, it’s flexible enough to account for variability and customization.
So instead of collecting and entering data, engineers can spend time making improvements that deliver real value.
Ready to replace your spreadsheets with automatic, real-time manufacturing analytics? Get in touch for a free demo today.