Not long ago, in a comment on anarticle I wrote on LinkedIn about Industry 5.0 that I consider to be a deviation from the necessary focus on smart manufacturing technologies and business models–someone brilliantly called the stage we live in: Industry PI or Industry 3.14159265.

It’s funny because it’s easy to memorize, but also because it has a huge dose of truth: anyone who has field experience in industry and in the application of technology can easily understand that, although with a lot of variability, we are closer to the 3rd industrial revolution than the 4th.

From Industry 1.0 to Industry 4.0

This is certainly evident in automation, control and traceability system components, but it is seen very clearly in industry’s attitude towards data. It is therefore relatively easy to assess the maturity of a company from the way it collects, processes, and uses the data produced—and in their use of manufacturing data analytics. 

Many companies still live in pre-Industry 3.0 stage, where the factory has little or no data, and much of it collected manually, or through relatively rudimentary applications. 

In stage Pi, probably where most companies are, there is a set of software solutions in place, such as ERP, MES, QMS, along with some automation solutions. It is common at this stage to have data collected from machines, but these are often stored in silos–in isolated files or databases–which eventually someone will try to analyze. 

Tell me what you do with your data, and I”ll tell in which stage you are

Trying to evolve from the Pi stage (perhaps in a Pi++ stage), some companies seek to interconnect these solutions and begin to try to solve the problems of disparity in information sources. Furthermore, they already understand that one of the greatest assets they have is data and therefore seek to centralize information in data warehouses. 

In a maturity stage already on the way to Industry 4.0, there are companies that have already realized that they need to have centralized data collection and storage solutions from various applications. This is when we see companies looking to create centralized data lakes that they imagine will not only finally become the single source of truth, but that promise to open the door to the artificial intelligence “El Dorado”. 

And here a new phase begins, often called pilot purgatory. Many companies are embarking on machine learning and data science initiatives with quite disappointing results. They may have been lured by much-hyped initiatives promising great insights and predictive models allowing the company to boost its performance. Unfortunately, these solutions often end up being inefficient, costly, and difficult to scale. 

Most of the time it is not due to lack of data – many manufacturers gather loads of data and send it to central data lakes. But then data scientists spend most of their time creating data sets and cleaning data, not running advanced algorithms to uncover valuable insights. 

With AI everywhere, why is it so difficult to use it in Manufacturing?

The title of this section says it all. We’re surrounded by AI in everything we do, lately with LLM’s becoming omnipresent. But if this is case, why isn’t manufacturing leveraging it yet? 

The challenge is that unlike what happened in the past, to truly extract value from data, organizations must employ a combination of three roles: data engineers, data scientists, and domain specialists. This multidisciplinary approach is crucial for overcoming “pilot purgatory”

The Data Triangle: why you need Data Engineers, Data Scientists, and Domain Specialists in Manufacturing?

Data engineers are responsible for building and maintaining the infrastructure and architecture that allow for efficient data collection, storage, and access. This includes setting up databases, data warehouses, and data pipelines. They ensure that data is available, reliable, and correctly formatted for analysis. Without robust data engineering, organizations can struggle with data silos, poor data quality, and inefficiencies that impede the scaling of data initiatives. 

Data scientists analyze and interpret complex data to help make informed business decisions. They use a variety of techniques from statistics, machine learning, and predictive modeling to uncover insights and patterns within data. Their expertise is vital in turning raw data into actionable insights. However, without proper data infrastructure and domain knowledge, their ability to deliver meaningful results can be limited. 

Finally, domain specialists (aka subject matter experts), have deep knowledge and understanding of the specific area or industry in which the organization operates. They provide context to the data and help in formulating relevant questions and interpreting the results in a meaningful way. Without their input, data analysis can lack practical applicability or miss crucial industry-specific nuances. 

There are many references to the need for these roles and the need for them to work together. As an example, you can find below a schematic view of these roles, taken from an HP blog post, “The New Data Science Team: Who’s on First?” in which the author funny enough says that “AI is too important to leave to the data scientists alone”. 


To get rich from your data, you first need data enrichment

Once again, this title says it all. The key to moving beyond pilot projects is the collaboration among the three roles described above. And one of the main topics they need to collectively resolve is data enrichment and contextualization. 

The idea is not new and not specific to manufacturing data analysis. Yet, it seems most people simply forget to analyze how other industries resolve their problems and apply similar solutions. In fact, data enrichment is a common practice across company functions in a variety of industries. 

In Marketing and CRM, companies start with basic customer data. Such as names, email addresses, and purchase histories and enrich them with additional information like demographic details, social media activities, or browsing behaviors to gain a deeper understanding of their customers; in financial services, customer data is enriched with investment history and transaction patters to help understanding the customer’s financial behavior; E-commerce platforms enrich their customer data with browsing patterns, product preferences, and feedback or reviews;

And in manufacturing it should be no different. If for instance the target is to perform predictive maintenance, then operational data from machinery needs to be combined machine information and specs, maintenance history, current material being processed, program being used, etc. In terms of quality, sensor data or other quality parameters collected from the equipment can be enriched with specific production batch records, product specifications, machine settings, etc., to predict quality or pinpoint the root cause for the quality issues. What we’re adding is data context. And data context are all the meaningful relationships between data sources that support the use cases.

Discover here how we transform data into actionable insights here at Critical Manufacturing.

Almost every company of a given size that I deal with has a data lake project of some sort. And in the vast majority of those cases, they are storing the diversified data in separate areas of the same data lake, with no contextualization. And this is where MES comes into play. I don’t know of any better contextualization source of manufacturing information capable of enriching otherwise uncontextualized and feature poor equipment or sensor level data, than Manufacturing Execution Systems. And this is where the value from data analytics is earned.

Clean up your data lake: overcoming common manufacturing data challenges

The idea of using MES as not only a contextual layer, but also as the main structure of a Common Data Model is tremendously important and I am convinced it will become a game changer in our industry. I will deal with it in another article, but for now, I would like to simply pass on some of the reasons why data lake projects are not yet successful in manufacturing. Out of luck, I found a great article from CogniteClean up your data lakes, data contextualization in the manufacturing industry”.