If you work in a manufacturing related industry, it’s difficult to escape the ideas and concepts of Industry 4.0. A brainchild of the German government, Industry 4.0 is a framework that is intended to revolutionize the manufacturing world. Similar to what the steam engine did for us earlier in the last century, smart usage of modern technology will allow manufacturers to significantly increase effectiveness.
While there is a general framework that describes what Industry 4.0 should be, I have noticed that most companies have developed their own definitions. As a matter of fact, most of my clients lump the terms Industry 4.0, Digitalization and IoT together. Also, the desired objectives have a wide range and include items such as:
Improve product quality
Reduce cycle time
Industry 4.0 initiatives
With a wide definition of Industry 4.0/ Digitalization comes an equally wide interpretation of what type of tactics and initiatives should be undertaken to achieve the desired outcomes. Based on my own experience, I see companies look at a variety of activities that include:
When you think about it, each one of these programs requires a ton of data. How else would you go about it? Consider the easiest example: energy management. Reducing the amount of money spent on energy throughout a large plant by gut-feel or experience is almost impossible. It is the smart use of data that allows you to identify energy usage patterns, and hot spots of consumption. Data must therefore be the foundation of every Industry 4.0 undertaking.
Big Data & Industry 4.0
What type of data does Industry 4.0 require? It depends. Typical scenarios could include relational data about industrial equipment (such as maintenance intervals, critical component descriptions etc.), geospatial (e.g. Equipment location, routes, etc.) and most importantly sensor data (e.g. Temperatures, pressure, flow-rates, vibration etc.).
Sensors and automation systems are the heart of your Industry 4.0 program: they pump a vast amount of highly critical time series data through your various initiatives. Just like the vital signals from a human being allow a doctor to diagnose a disease, industrial time series data allows us to learn more about our operations and to diagnose problems with our assets & processes early on.
The value of industrial time series data
Assets such as turbines, reactors, tablet presses, pumps or trains are complex things. Each one of them has thousands of valves, screws, pipes etc.. Instead of relying on intuition, hard-earned experience and luck, we can collect data about their status through sensors. It’s not unusual for specific assets to produce upwards of 1000-5000 signals. Combine a number of assets for a specific production process and you end up with some really BIG DATA. This data, however, allows engineers and data scientists to monitor operations in real-time, to detect specific patterns, to learn new insights and to ultimately increase the effectiveness of their operations.
Industry 4.0/ Digitalization is an exciting opportunity for most companies. While many organizations have already done a bunch of stuff in the past, the hype around Industry 4.0 allows project teams to secure funds for value-add initiatives. It surely is an exciting time for that reason.
But is dealing with industrial time series data easy? Collecting, archiving and managing this type of data can be a huge problem if not done properly. In the next blog post, I will speak about the common challenges and ideas for making this easier.
2012 is almost over and I just realized that I have not yet posted a single entry about big data. Clearly a big mistake – right? Let’s see: Software vendors, media and industry analysts are all over the topic. If you listen to some of the messages, it seems that big data will create billions of jobs, solve all problems and will make us happier individuals. Really? Not really – at least in my humble opinion. It rather seems to me that big data fills a number of functions for a select group of people:
It provides analysts with a fresh and fancy-sounding topic
Media have something big to write about
BI companies obtain a ‘fresh’ marketing message
Professionals can have ‘smart’ discussions
Consultants can sell new assessment projects
Big data – really?
I do apologize for sounding so negative. But I have a hard time finding big value in this big data discussion. Please don’t get me wrong – I would be the last person to deny that there is a tremendous amount of value in big data. But it does not deserve the hype. On the contrary, I personally find that the current discussions ignore the fact that most of us do not have the skills to do big data. We need to get the foundation right and make sure that we can tame the ‘small data lion’ before we tackle the big data Gozzilla. Don’t believe me? Consider the following:
Spreadsheets are still the number one data analysis tool in most organizations.
Managers still argue about whose revenue and unit numbers are correct.
Knowledge workers have yet to learn how to make sense of even simple corporate data sets.
3D pie charts are floating around boardrooms.
Companies spend over 6 months collecting and aggregating budgets only to find that a stupid formula mistake messed up the final report
Hardly any professional has ever read a book or attended a course about proper data analysis
Here is the thing: Dealing with big data is a big challenge. It will require a lot more skills than most of us currently have (try finding meaning in gazillion TBs of data using a 3D pie chart!).
A big data problem
Earlier this year, I acquired a 36 megapixel camera. You can take some amazingly gorgeous photos with it. But it comes at a cost. Each photo consumes 65-75MB on my sad hard drive. Vacations now create a big data challenge for me. But guess what: this camera is anything but easy to handle. You have to really slow down and put 100% effort into each and every photo. 36MP have the ability to reveal every single flaw: The slightest camera shake is recorded & exposed. Minimal focus deviations that a small camera would not register, kill an otherwise solid photo. In other words: this big data camera requires big skills. And here is something else: The damn camera won’t help you create awesome photos. No, you still need to learn the basics such as composition, proper lighting etc.. That’s the hard stuff. But let me tell you this: If you know the basics, this big data camera certainly does some magic for you.
Big data – what’s next
Ok. That was my big data rant. I love data and analytics. No doubt – there is a tremendous amount of value we can gain from those new data sources. But let’s not forget that we need to learn the basics first. A Formula 1 driver learned his skills on the cart track. At the same time, there is a lot of information hidden in our ‘small data’ sources such as ERP, CRMs and historians. Let’s take a step back and put things into perspective. Big data is important but not THAT important.
With that: Thank your for following this blog. Happy holidays and see you next year!