Steve is a manager at an industrial beverage plant that produces bottled soft drinks. Accessing, analyzing, and sharing data about the daily performance is an integral part of his job and one that can often be tedious and time-consuming.
Resources like energy, chemicals, and water all play a role in the quality of the end product the plant produces as well as the profit margins Steve and his team can achieve. Manual and legacy data management processes can eat up a serious portion of an operator’s day.
The following illustrates the challenges that workers like Steve experience throughout the day in an attempt to manage and make sense of their data.
Monday: 9 a.m.
Steve gets to his desk and opens an email from his colleague about the performance of a new piece of equipment the plant installed last week. He doesn’t quite remember what the data in the spreadsheet is measuring, but it doesn’t look good. He searches back through last week’s emails to jog his memory.
He clicks download on the Excel spreadsheet attachment in his colleague’s email, only to get a pop-up window that says he needs to update his Microsoft Office Suite in order to open the document. He asks himself, “Where is that activation code, again?”
He opens the spreadsheet and has to correct some of the data formulas that didn’t import the right way, he starts reading through the 13 tabs in the document. The numbers don’t look right for some reason. He swears it was performing perfectly when he read the initial read-outs from his technician last Friday. Steve rifles through the thick portfolio on his desk for the printout the technician gave him last week. He can’t find it. “I’ll have to give that tech a call,” he says.
Steve then gets a voicemail message saying that particular technician is out sick today. The report will have to wait.
After back-to-back meetings, Steve gets called down to the factory floor to inspect a piece of equipment that has automatically shut off due to a malfunction. Production is at a standstill as he and his team try to figure out what went wrong with the machine.
After sifting through dozens of printouts and warning screens on the equipment itself, he and the team discover the machine was overheating. Things get frantic as the plant sits idle, so Steve makes an executive decision to adjust the cooling system on the equipment to a temperature his gut tells him will work (he has over 25 years of experience, so his intuition is spot on, right?)
Steve gets back to his desk and opens the spreadsheet from the morning. He realizes the report from the email was showing the coolant malfunction in the machine he just had to deal with on the factory floor. He has access to all this data, but it’s spread out across so many different sources that he can’t make the appropriate decisions that will lead to meaningful actions. He combs through the spreadsheet to see if the gut-based temperature adjustment he made earlier was the right one.
He’s way off…
Like thousands of other industrial operators, Steve can’t make real-time adjustments to his plant’s processes when his data is locked in legacy and manual systems. He would benefit from a centralized platform that can offer him real-time updates on his plant’s processes and assets, as well as automated recommendations and solutions on how to fix problems when they arise.
After that nightmare of a day, Steve has to spend the next morning looking for ways to deal with equipment downtime and the issues that spreadsheets and other legacy methods have been causing him.
Increasing their industrial intelligence By installing advanced automated sensors powered by an AI system, Steve and his team can monitor critical assets and conditions around the clock in a clear and simple readout that is always up to date. And when emergencies arise, the right AI system can automatically make adjustments and recommendations before a time-wasting issue halts production.
Does any of Steve’s day sound familiar to you or your team? Unplanned downtime can cost manufacturers an estimated $50 billion annually. It may be time to reevaluate your relationship with your data.