The quest to reduce variations in products made on different shifts has existed as long as the chemical industry has. Yet, success has continued to elude us. However, with the arrival of more connected devices (the so-called Internet of Things or IoT), we are starting to have the opportunity to address product variability and other process problems — and the industry is responding (see: "Chemical Makers Embrace Digitalization.”).
Most process companies long have had the capability to gather operational data. However, with the lack of general connectivity, gathering these data was time consuming and expensive. Moreover, only a select few people looked at and analyzed the data. In some cases, data analysts were in different departments and arrived at different conclusions due to different data sources, methods of calculation and timeliness.
With the technology available today, we can address some of these product variability issues. Sensors are becoming cheaper, new equipment arrives with sensors and connection capabilities already installed, and operational data increasingly are viewed as valuable and worth the effort of analyzing.
Are You Collecting The “Right” Data?
Ask most process companies and they will tell you they are collecting operational data. Unfortunately, most companies stop there. According to a study by IDC, less than 1% of data collected are analyzed. This begs the question: What are we collecting the data for? The answer isn’t always obvious. Back in the day, before we even thought of connecting sensors to everything, I was doing some analysis and followed the data trail around a company. In this case, the data trail was a paper document. The document, which was created daily in production and operations, listed the products and quantities made along with the associated work in progress. Copies were filed in the production office and in accounting. Nothing was done with the data. This had been going on for years, using an expensive preprinted form. Apparently, many years ago, a study that required the data was initiated; when the study was completed, no one halted the data collection.
This underscores that some data are needed only for a limited time; so, it’s good practice to periodically review if the data are still useful. I believe the problem (gathering non-useful data) will grow as it becomes easier and easier to automatically collect data and automate the analysis. When the analysis stops being useful, do you remove it from your reports and dashboards or just ignore it?
Another issue in collecting data to address your problems is determining what are the “right” data. Most companies seem to have many definitions of each data element, usually unique to each department. These differences lead to many discussions about whose number is right. Can you agree across your organization on what the actual calculations for your key performance indicators (KPIs) are? Even if you agree on the definitions, do you agree on the data source — and also on the interpretation of data?
With all this decided, you must consider one more thing: Can you access the data? Depending upon the data source, company policy may prohibit you from accessing the data directly, and could require you to go through a demilitarized zone and firewall to get the information. This usually occurs when you’re trying to get to data that can impact the operation and safety of the facility (e.g., safety instrumented systems, safety instrumented functions and other control systems). Most companies are very leery about opening these systems to any possibility of Internet access out of concern about an intrusion or virus affecting them. Because most management systems are open to the Internet in some form (e.g., online sales portal, purchasing networks, mobile device access, etc.), companies severely restrict access to control systems from other networks and, in some cases, air gap (don’t physically connect) them.
These cases mandate setting up additional steps. The data from control and safety systems must be pushed into a safe area where the analytical and transaction systems can retrieve (pull) the data. This creates an additional layer of complexity, requiring coordination and timing among the various systems.
Are You Asking The Correct Questions?
Let me start by quoting from “Knots” by R. D. Laing:
If I don’t know I don’t know
I think I know
If I don’t know I know
I think I don’t know
KPIs imply you already know the question you want to ask, e.g., what are my inventory turns, my percent budget completion, etc. By knowing the question you want to ask, you already know the data elements needed for the answer. You might have trouble getting to a data element or might have to deal with multiple sources of the data element but essentially you understand what’s desired.