4,000+ Hours Saved Per Year
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Case Study Summary
On the manufacturing lines for a particular component of the F-35 Joint Strike Fighter, tracking production status required a significant amount of resources each week.
In essence, it would take several hours for engineers to assess the location and status of 100's or 1,000's of components being manufactured.
By the time the data was collected and analyzed, everything had already changed. The data would quickly become "stale." This created a significant challenge for decision makers.
To solve this problem, our team developed a fully automated Excel tool that determines a real-time status of all components in production within seconds.
The automated Excel tool connects straight into the manufacturing database backend, extracts raw data, processes it intelligently, and then presents critical information on a dashboard.
In essence, the tool does hours of work in seconds, allowing decision makers to run the report whenever they like. In all situations, better data leads to better decisions.
In the defense industry, manufacturing is high-tech and high-speed. There's a lot of moving parts, and the manufacturing processes are very complex.
Consequently, there's a lot of manufacturing data and metrics the need to be tracked and analyzed. It takes an army of highly skilled engineers to keep things running smoothly.
As manufacturing scales up, analyzing larger sets of data becomes even more challenging.
Real-time data is absolutely critical. But how do you process thousands, if not millions of data points to extract actionable information? It's a significant challenge.
On the manufacturing lines for a particular component of the F-35 Joint Strike Fighter, significant resources were required each week to determine the production status of all components.
It would take several hours to collect this data and analyze it. However, in this high-pace manufacturing environment, the data would become stale quickly. By the time a report was created, it was no longer accurate.
There had to be a faster way to get real-time status, so that decision makers had the best information at their finger tips.
I had the opportunity to lead the team that solved this problem.
Our team developed a fully automated tool that determines a real-time status of all components in production within seconds.
To accomplish this, we used Excel and VBA automation. Python was also a top consideration. However, Excel and VBA offered a more viable solution for the end users of the tool.
So how does it work?
The automated tool connects directly to the manufacturing backend system, which is an Oracle based server. It then extracts raw data using targeted SQL statements.
Next, it uses data science techniques to intelligently process the extracted data. Finally, it organizes the processed data on a dashboard. All of these steps are fully automated. It requires just a click of a button.
The final presentation of the data is a visual map of all components in production flow. This allows users to rapidly assess what's happening in real-time. It also illuminates any issues that need attention.
Overall, this tool reduced a 6-8 hour process down to seconds. It also saves over 4,000 man-hours a year. And finally, it eliminated nearly all human error.
In addition, the automated tool allowed decision makers to get a real-time status whenever they liked. They no longer have to wait a day or two to know what is happening on the production line.
A critical lesson for me across several industries has been: "better data = better decisions."
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