Cutting down on waste means cutting costs, no matter what size the business. But first a manufacturer must figure out what it's scrapping and why. As part of a major effort to optimize its manufacturing process, an international automotive parts supplier decided to undertake that challenge. They enlisted BlueGranite’s help. We helped them delve into the Internet of Things (IoT) – the data embedded in the company’s manufacturing equipment – for solutions.
While identifying waste is daunting for any business – tackling it in a global arena presents a formidable challenge. This supplier employs more than 100,000 workers at dozens of manufacturing and office sites across the world. Its engineering process involves a vacuum device that picks up each manufactured part and moves it along the assembly line, ultimately holding it in front of a camera for a quality assurance inspection.
That review determines whether the piece makes it into the supply chain. This is where bad pieces meet their end, but good pieces can end up in the scrap pile, too. Sometimes the part isn’t where it’s supposed to be, or the vacuum may be holding the piece at an odd angle, resulting in an accidental scrap. This company wanted to get a real picture of the good, bad and potentially bad parts discarded. But each site had different scrap calculation methods. And getting those numbers was a chore. Someone had to reach out to more than 100 managers to collect the data. Then it had to be compiled.
After tapping into the IoT, we used a data lake to capture actual scrap numbers as they are being created. Processing this big data – massive, complex information sets that were once almost impossible to investigate with traditional data processing methods – can create a picture of business trends and statistics over time. By mining the IoT, we’ve given the parts supplier accurate, timely numbers to analyze.
Once the data lake was in place, this company also used it to look at creation process cycle time. A computer monitors the operating line, notifying the person overseeing it if the cycle time increases to an unacceptable level. From there, that person can take action in resolving any issues. Prior to the data lake, there wasn’t much more the company could do with this information. Now that the data lake is collecting and storing these variances and their causes, leaders here can study how variances in cycle time affect the manufacturing process overall and, ultimately, the bottom line.
The manufacturing view afforded to corporate site managers and executives is no longer limited to a single plant or machine – the new data architecture brings everything together in one place so they can see as a whole how the company is doing. From there. They can make accurate, informed decisions.
Curious how uncovering data can help your organization thrive? We’d love to help. Contact us today.