What are the five most frequently asked questions I get about digitalization?

When I was putting together some ideas for digitalization topics that I wanted to blog about, I bounced a few of them off my wife. As a blogger herself who works in the field of social media and marketing, I see her as a great inspirational resource. She does not work in the industrial space, so she brings with her a very different perspective that I appreciate and value, especially for some out-of-the-box thinking. After going back and forth for a bit, she asked me what questions I typically get about digitalization –

and that really got me thinking. Which inquiries keep popping up in conversations and how do I typically respond to them? Answers to questions about the digital world don’t always apply to every industry, company culture and the like, but there are still similarities that can be applied across the board. Here are the five most frequently asked questions I get about digitalization, and my perspective on each one.

What is digitalization?

As I continually emphasize in conversations, workshops and blog posts, digitalization is all about capturing data, analyzing it, and creating meaningful insights into a process, plant, or operation. It really is as simple as that.

Now, implementing digitalization requires more work, of course – including a well-thought-out strategy, executive support, and people with 100% of their time allocated to the topic – but the basic premise remains the same. Today’s technology has advanced so much that it has become a lot more affordable and easier to use than ever before. Gateways that allow you to collect data from equipment, even equipment built back when data analysis was in its infancy, are now readily available. Be aware, however, that you shouldn’t collect data just for the sake of collecting it. Make sure there is a tangible business value behind doing it as I emphasized in my recent post.

Is there a cookie cutter approach to digitalization that can make implementation easier?

Well, let’s just cut right to the chase on this one. Unfortunately, there is no such thing as “cookie cutter” when it comes to digitalization. Transformational journeys tend to vary depending on the company that’s implementing it, the employees’ skill sets, financial strength, executive involvement, and so forth. With that said, it is possible to identify commonalities in how, when, and where to tackle digitalization. Since digitalization has been around for a few years, tried and tested approaches/methodologies have been developed that can be applied to pretty much any size company in any industry. So, don’t despair; there is hope. Just make sure to work with the right partner or set of partners to support you in your individual implementation.

What are some typical digitalization examples?

Although this depends on the company, their level of digital maturity, and the industry they’re operating in, there are three areas where digitalization typically has the highest impact.

1. Mobile operations, which basically makes information available when and where it is needed. A great example is the use of tablets that enable remote access by a service technician. The technician can use the video feed to see what’s going on and overlay instructions or graphics on the tablet screen for the person on-site to see. This can make service calls cheaper and more efficient and also has the potential to decrease equipment downtime considerably. Read here for more details.

2. Predictive and prescriptive maintenance. This has come a long way in the last couple of years, powered primarily by machine learning and artificial intelligence. By means of data analytics, we are now able to better predict when a failure is going to happen and, in some cases, prescribe a “cure” to the problem. Not all equipment is ready or even suitable for this, so make sure you have identified the assets that give you the biggest headache before making any investments. It all goes back to only collecting the data you need and defining the value behind collecting it.

3. Equipment performance optimization, which is somewhat related to predictive maintenance but focuses more on the simulation aspect. The idea is that by using a digital twin, you can simulate behavior in the design phase and implement any changes before the physical equipment or asset is built. This has the potential to save valuable project time and considerable amounts of money by reducing the number of changes required to rectify design flaws. And this brings me to the fourth question about digitalization that I get most often

What makes simulation so useful?

Simulation is a mature technology that has experienced somewhat of a renaissance in the digitalization age. As mentioned above, simulation can be used as part of the digital twin in the design phase, but there are many more examples of how it can lend value to your project. A big one is its suitability for training of operators and maintenance crews. Imagine having a plant operator train on a system that is an exact duplication of the real plant. He can open and close valves, practice emergency scenarios, and more. That is a very powerful tool in today’s age of baby boomers retiring faster than younger generations can fill those voids in the job market. Another example is to simulate the behavior of a liquid, substance, or gas in a vessel. This is extremely powerful when testing various vessel design options, especially when proper mixing of the materials is key to the final product quality. In talking through this, I may have just discovered my next blog topic – there’s just too much to cover in this one.

Am I ready for artificial intelligence, machine learning, and block chain?

This may not be exactly how the question is worded, but it’s a topic whose importance becomes apparent through conversations about digitalization. Many people believe that digitalization is all about artificial intelligence and machine learning, and although that is true to some extent, there is much more to digitalization. The key takeaway here is that you cannot expect to employ artificial intelligence if, say, your data or communication protocols are not digital and easily readable by a computer. This is where digital maturity comes in. Artificial intelligence is typically easier to implement in a digitally mature company that has gone through several iterations of digitizing data and sharing data across departments, and that has solid experience using a cloud platform. I’m not saying that you cannot use artificial intelligence until you have gone through those exact steps, but there is a certain degree of evolution to digitalization that needs to be considered. Knowing where a company is on that maturity curve is crucial before embarking on any transformation journey.

So, what do you think about these five questions – and answers, for that matter? Can you relate them or are there others I should have included? The bigger question is whether you agree that I should ask my wife for advice on future topics to write about?