Data Data Everywhere, But Not a Byte to Eat

Data Data Everywhere, But Not a Byte to Eat

By Joe Cichon, Vice President Manufacturing Technology, INX International Ink Co.

Joe Cichon, Vice President Manufacturing Technology, INX International Ink Co.

INX International manufactures Printing Inks for the graphic arts industry, and is unique in that we use a myriad of machines to produce batches from 10 pounds to 30,000 pounds, with an SKU count of over 500,000 unique items.

We have many process machines that are controlled by PLCs and software. Some PLC systems and control software generate tons of data but much of that data is not stored, and subsequently it becomes lost process data information.

We decided to start collecting and using our data on our processes many years ago, and for the most part it was hand collected data that was recorded on paper or process orders, and finally someone would enter it into a spread sheet or download it from our ERP. These records are helpful when we have to troubleshoot a problem but for the most part, most of the data was not used.

In the last few years, we decided to use the data that passed through our machine control circuits and PLCs to help optimize our equipment. When we investigated options, most vendors who approached us estimated costs of about $40,000 to $80,000 to give us a full assessment and in two cases, the assessments came in between $190,000 and $400,000 at just one of our 7 major plants

Our business-based ERP data was helpful in managing and planning our business, but we needed detailed process data. We discovered early on that using SAP (our ERP) to process machine data was extremely expensive and the cost to configure the software was estimated in the mid 6 figure range.

We had machines in our plants that were controlling equipment and logging data, but the problem was that no one in operations knew how to access the machine data and for the most part it was just consuming bits on a hard drive somewhere or just evaporating into space. The data that we collected was very helpful to engineering when troubleshooting an issue, but our plants are not run by Engineers. We needed information that could be used by managers and operators, and technicians. It needs to be accessible, and easy to view and use. We do not have time for an engineer to spend hours sorting through tables of data tags to find an answer every time we have an issue. Even more important we needed data that would help find and maintain optimum running conditions for every product we make on any machine.

"We discovered early on that using SAP (our ERP) to process machine data was extremely expensive and the cost to configure the software was estimated in the mid 6 figure range"

We decided to start harvesting our machine data to help to find ways to optimize our machine processing systems. We spoke to a few integrators, and software firms who indicated that they could use artificial intelligence or cloud data to help to optimize machine production efficiency. What we discovered (circa 2013) was that all of them were using machine sensors and indicators and gauges to determine when a machine was approaching failure. Looking at vibration sensors, temperature sensors, power requirements etc., they are able to determine anomalies that indicated something might be wrong. This is useful information and it is indeed worth its weight in gold. The ability to predict a machine failure before it happens, can prevent many catastrophic failures that can shut down a line, or a plant or cost you some very important customers. But what we really need is data for managers, and operators to help assure that our machines are running at optimum production, and quality rates whenever we need them to run.

We are still on our journey, and there is much to share, but for now, I would like to share some of our lessons learned. If you are aware of the possible pitfalls when you start, you can save your team a lot of money and a lot of time.

Key points that you might want to capture based on our experience.

• Start simple focus on individual line OEE (Overall Equipment Efficiency) data.
• A solid infrastructure is critical to success (check to make sure your manufacturing connected system is reliable, with compatible PLCs, HMI, and data transfer protocols and networking).
• Newer machines have IIOT options to get your machines individually reporting up into the cloud and available on internet or mobile devices, and tablets.
• Make sure your team includes Managers and operators working with your installers to assure that they get what they want.
• Operator training is very important and should be documented, and delivered by an expert, and confirmed as well as Policed over time.
• Rely on machine data as much as much as you can instead of using operator scans or truth tables. (if there is sufficient power to the machine it is running)
• Data Cleansing is often overlooked and is also a critical step. Don’t use bad data, especially if operators (Humans) must enter information.
• If Operators enter important information be sure your system validates the entry so they cannot enter bad data.
• Make sure the data is easy to see and access. Don’t rely on spreadsheets or query reports.
• Security is important. Be sure to use an IDMZ (industrial De militarized Zone). A big risk for any company is ransomware (even if you do not feel you have secret data to protect, Hackers can disable or damage your equipment and encrypt your data.)
• Artificial intelligence can be used to harvest performance data to help optimize production settings on equipment.
• The Data Scientist is an emerging field resource that we all will need to either have on staff or be able to tap into as needed.

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