Industrial manufacturing is going through one of the most challenging and exciting phases of its history. The sector is growing globally, but its growth rates remain low. Manufacturing supply chains are struggling to keep up with demand, making productivity gains a more critical element of competition in this vertical.
Manufacturers are responding to slow growth rates by launching what’s known as ‘the fourth industrial revolution,’ or Industry 4.0. Industry 4.0 is taking over the manufacturing industry thanks to widespread deployment of cheap sensors and connectivity tools.
These tools, combined with advanced analytics, enable the collection and analysis of huge amounts of industrial data. This, in turn, provides manufacturers with an incredible source of knowledge concerning the whole supply chain. Business intelligence (BI) and business analytics (BA) can then be used to draw insights about potential improvements at any stage of a product’s lifecycle.
The right mix of descriptive, predictive and prescriptive analytics eventually leads to great competitive advantages. These can include cost reduction, supply chain optimization and productivity gains, or the use of new disruptive approaches like forecasting manufacturing, proactive maintenance, and ‘predict and fix’ models.
To understand the dimensions of this phenomenon, General Electric found that one of its factories could generate 5,000 data samples every 33 milliseconds. Just one product line could produce as much as 4 trillion data points per year. And that was in 2012! As technology keeps getting better, manufacturers can access even more data (and value).
Dealing with such a large amount of information can be grueling and problematic. However, those who won’t understand or embrace industrial data strategies will soon be out of the game. It’s critical for manufacturers to be aware of the challenges they might face when collecting, analyzing, and managing industrial data, so that they can be better prepared to overcome them.
The 5 challenges with industrial data
The main challenges stem from the increasingly distributed nature of industrial data sets. When data is produced by many different sources, it usually ends up being structured and presented in totally inconsistent ways. As a consequence, it creates challenges in industrial data access, integration, and sharing. As a result, many organizations are able to capture data, but can’t properly extract and analyze it.
The big data generated by industrial IoT is usually characterized by heterogeneity, variety, unstructured features, noise, and high redundancy. These features can thwart manufacturers trying to properly use their data. Indeed, many in the manufacturing industry end up using only a small percentage of their industrial data in a way that adds any value to the business.
1. Industrial data integration: when multiple sources become a challenge
One of the most critical industrial data challenges concerns the ability of manufacturers to bring together data from the factory floor, offices, and any other location-based sources of industrial data.
The main issue in integrating multiple industrial data sources is that traditional “transaction oriented” ERP systems were designed as independent applications that weren’t meant to exchange information. Software developers did create several ways to export data from one application to another, but this wasn’t provided in an intuitive or streamlined way.
Traditional data management techniques were also developed to deal with one specific data source. These techniques fail to provide the real-time, instant answers that many industrial data analytics applications require.
Manufacturers need a unified data model that facilitates industrial data sharing, rather than simply data exchange. Integrated PLM tools facilitate this process by making any change visible and accessible to everyone in the company. The goal is to create a “single source of truth” containing all the information that’s meaningful and relevant for decision-making processes.
Once industrial data has been integrated and shared to every business unit, everyone in the company will be operating based on the same data model and available information. This will help minimizing wasted materials and activities.
2. The challenges of integrating new technologies within current frameworks
Industry 4.0 is a process of constant evolution. It requires careful step-by-step processes to efficiently integrate numerous disruptive technologies into the existing technological framework of the company.
Hence, another serious industrial data challenge relates to the integration of new technologies into a company’s legacy systems. Legacy systems include, for instance, ERPs, machine-level control systems, manufacturing execution systems, and production planning systems.
Integration challenges usually appear when legacy systems lack a well-defined interface and proper documentation, or when they’ve been programmed in different or obsolete coding languages.
3. Industrial data storage challenges
The last few years have seen a rapid explosion in the volumes of industrial data manufacturers generate and collect. Storage management systems have failed to keep pace so far.
Industrial data storage has thus transformed into one of the most pressing issues of Industry 4.0. When attempting to solve this issue, remember that timeliness influences the value of your data. Make sure you only store relevant, insightful data that can be quickly acted on.
4. Challenges in industrial data visualization and user interaction
The big data industry is still working to provide better visualization and customer interaction tools. Presenting information in an intuitive and user-friendly style is important, as it helps users produce better insights and devise new solutions.
With the help of data visualization and data exploration, you have a chance to understand the meaning of your data. Without it, your data is just a series of numbers. Data exploration tools can analyze your dataset to help find patterns and meaning in the information. These tools could find a correlation that the human eye could have missed that has major effects on your business.
The more the volume and complexity of data grows, the more the visualization issue becomes a priority. While manufacturers may not be in charge of solving this particular industrial data challenge, they should nonetheless be aware of its influence.
5. Industrial data security challenges
Industry 4.0 relies heavily on the use of Internet of Things (IoT) technology, an essential piece of digitization in the manufacturing industry. However, connected tools also pose new challenges in terms of data confidentiality and protection.
The capacity of current industrial control systems to share information depends on gateways that connect IoT devices to the internet. These systems have limited computing power, making them unfit to enforce the kind of authentication policies and rules we use today for emails and other internet-based tools. Hence, the same gateways that allow us to share industrial data also expose manufacturers to the risk of unauthorized access, leaks, and even sabotage.
Overcoming industrial data security challenges requires an increase in the computing power of these gateways so they can properly handle security tasks. This will require manufacturers invest more than they initially might have budgeted. However, given the risks associated with losing control of confidential information in the manufacturing industry, the return on such investment can be huge.
In the coming years, the manufacturing industry will have to overcome several critical industrial data challenges. Some will depend on manufacturers themselves, while others require manufacturers combine their efforts with other players, especially big data service providers.
Solving these issues will require large investments from companies, though not necessarily in strictly financial terms. Those who succeed will become the manufacturers of the future. The others will simply disappear, as usually happens in industrial revolutions.