This is the first in a series of blog posts on Data Platforms for Manufacturing. For reasons that I will explore throughout this series, many companies seem to be strategically lost among the various digitization initiatives and with many difficulties in achieving results from the investments made, particularly in the trendiest and related to Industry 4.0 and with the world of Data Analytics and IoT.
In general, it seems to me that the main reason for this is that, surprisingly or not, these technologies associated with the fourth industrial revolution did not originate in the … manufacturing industry.
Throughout this series, I will try to explain my own learning process and how we are applying these technologies in an integrated offering at Critical Manufacturing.
A Data Revolution is Before Us
Clive Humby, UK Mathematician and architect of Tesco’s Clubcard, is widely credited as the first to coin the phrase in 2006: “Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”
There is no doubt that we are witnessing an authentic technical revolution. Call it Industry 4.0, Internet of Things, Big Data, or Artificial Intelligence; the proliferation of solutions facilitating Industry 4.0 is accelerating.
Whatever the perspective is with which we look at this fast-paced evolution, there is an asset that is at the center of everything: data.
However, contrary to what happened in the previous industrial revolutions where manufacturing was at the focus of the revolution, manufacturing has lagged in implementing the base technologies underlying this data transformation. It has been conservative and extremely slow to realize that the application of these technologies is invaluable, perhaps much more than in any other segment. They are refining, but at a much slower pace.
Case in point, the term “The Internet of Things” was coined by Kevin Ashton in a presentation to Proctor & Gamble in 1999; and more than 20 years later, manufacturing is still learning the meaning of IoT and trying to devise strategies to take advantage of it.
But, Manufacturing seems to be trying to make up for lost time, and at least with regard to future intentions, it is the most relevant segment. It remains to be seen whether it does it correctly and with results.
But let’s step back for a moment and understand what were the fundamental changes that allowed this data revolution:
- The miniaturization and accessibility of electronics. Moore’s Law in 1965 stated that the number of transistors packed in a given unit of space would double every two years. although the periods of doubling shortened and today it seems we’re reaching saturation, this postulate has remained true for many decades.
- The significant improvement of hardware and software capabilities with attractive price points to process and store data, including cloud technologies. Although this seems new, CompuServe offered small amounts of disk space for file storage back in 1983.
- The evolution of artificial intelligence, specifically machine learning. These are not ‘new’ algorithms. Deep Learning started in 1943 when Walter Pitts and Warren McCulloch created a computer model based on the neural networks of the brain.
In addition to the ones above, it is also worth mentioning that a significant part of the evolution of tools that are used today in the area of big data and analytics originated from the need to process data massively generated mainly by social networks. In fact, the primary data technology evolution had its origin in social media. Huge arrays of unstructured data were being generated, with no way of processing or capturing the massive data ranges with existing data storage and analytics capabilities. Both the technical processing needs and the requirement for real-time analysis led to a shift from batch processing to streaming, and ever since the big data technologies have never been the same.
The Birth of Data Platforms – Does One Solution Fit All?
The new requirements and challenges posed by the frequency and volume of data being produced and the need to seize this data by a new breed of scientists, determined to use advanced analytics solutions ranging from graphical visualization to artificial intelligence, gave birth to a new type of application called IoT data platforms.
I don’t like the name IoT data platform, because it gives rise to great confusion about what a data platform truly is and what relationship it has with the IoT. This topic will also be explored later, but keep a key idea for now: a data platform can have an IoT source, but also any other type of software applications. So I’m just going to call it Manufacturing Data Platform.
While many of the aspects of data handling and processing are common to any type of data platform, there’s the need to rethink these in the light of the specific requirements of manufacturing in discrete segments with pre-installed solutions that fulfill the required tracking and control needs.
Although in general these platforms can be implemented in parallel to the tracking and control software solutions, the overall value these create is nothing but a fraction of what they could achieve if the solutions were conceived in combination to leverage each other’s capabilities.
In the next post of this series, we’ll explore the broader topic of data analytics, including data platforms: why they exist, and most importantly, how they can be used in the manufacturing context to leverage existing advanced solutions, such as modern MES systems. Stay tuned!