IIoT, Industrie 4.0

Three phases of industrial digital transformation

Manufacturing, packaging, and logistics companies are unlocking new potential through digital transformation with the power of the Internet of Things (IoT) and advanced analytics.
By Daniel Repp November 26, 2018
Courtesy: Lenze Americas

The digital transformation of the manufacturing industry is well underway but what that means for plant engineers, specifically, is still a little unknown. For many companies it is unclear how to best implement the new digital technologies and how to determine which implementation partners will be the most reliable and experienced. However, two things are clear: digitalization is critical if companies want to stay competitive in the future, and automation plays a key role.

The goal is no longer about just automating the movement of physical product, but fully automating the data as well. This requires moving from manual, human-generated information workflows to a more real-time process. Digital transformation is about digitalization, networking, process analysis, and deep information automation.

Transformation is the right term. While it won’t be easy, digital transformation has the potential to unlock formerly unattainable benefits for all aspects of manufacturing from a machine builder to factory floor to end customers. There are three areas of potential to unlock full digital transformation, many of which are already well within reach.

1. Visualizing: factory connectivity and data integration

Today, the information flow at many plants is still manual or semi-manual. Machine operators or engineers collect data on paper or mobile devices—about things such as how long it takes to prepare a machine for production, to change from status A to status B, or to transfer part X to part Z. That information is then loaded into a computer program that can assess the data.

This process is light years ahead of where we were just two decades ago, but still takes significant time and effort, and there is the undeniable risk of human error in the process—both accidental and intentional. How do we get from the analog, linear model of the past and reach the digital, platform-based model of the future? The Internet of Things (IoT) and full-factory connectivity are tools that will help enable the digital transformation.

Using intelligent, networked parts means data can come directly out of the machine, eliminating human error entirely and enabling a much faster flow of information and business intelligence (see Figure 1). It also relieves operators and engineers from having to collect data manually and enter it into the existing process software themselves. Instead, they can focus on their core work.

Figure 1: Using intelligent, networked parts allows data to come directly out of the machine, eliminating human error and enabling a much faster flow of information and business intelligence. Courtesy: Lenze Americas

Figure 1: Using intelligent, networked parts allows data to come directly out of the machine, eliminating human error and enabling a much faster flow of information and business intelligence. Courtesy: Lenze Americas

With a more connected and integrated data stream, information can be pulled together from different process steps or from different parts such as a motor or gearbox or from entire production lines, which may contain components and machines from various manufacturers. The data can then be used to create a complete representation of the existing production process—spanning machines, plants, and even production sites.

2. Forecasting: predictive production modeling and responsive machine design

Capturing and understanding the existing behavior of a machine, machine line, or a site is only a small sliver of what is possible. However, it is the foundation on which more advanced analytics are built that provide visibility into part, machine, and system trends. These trends include predictive models to reduce or eliminate unexpected downtime or unforeseen problems (see Figure 2).

Figure 2: Capturing and understanding the existing behavior of a machine, machine line, or a site is the foundation on which more advanced analytics are built. This provides visibility into part, machine, and system trends. Courtesy: Lenze Americas

Figure 2: Capturing and understanding the existing behavior of a machine, machine line, or a site is the foundation on which more advanced analytics are built. This provides visibility into part, machine, and system trends. Courtesy: Lenze Americas

Instead of simply capturing linear values to assist in visualizing what is happening at any moment, the system captures complex, nonlinear developments or trends that forecast problems. Warning systems can then be set up to enable intervention in the production process before critical values such as impermissible quality factor of overloading of a machine occurs.

Advanced analytics often combine factory-level data with other data streams such as business administration information or weather data to account for external factors that can impact production. Factors such as ambient temperature, humidity, differences in raw materials, or shift management can easily be added into the digital process.

The full potential of the IIoT only can be realized if there is a new level of coordination within a company, including changes to the work environment and the creation of a collaborative workforce. It also requires OEMs, suppliers, data scientists, and engineers to work collaboratively, a development which has resulted in additional benefits.

3. Self-regulating: data-driven manufacturing and ongoing transformation

One step beyond predictive lies total automation, in which systems adjust themselves based on forecasting models without manual interventions. Such systems rely on innovative and ultra-efficient methodologies such as statistical process control to optimize production on the fly by tweaking various aspects that, to date, have been onerous, manual adjustments. For example, changing the setpoint values or even the whole process sequence of a machine could be done without any human involvement.

Additionally, the ability to gain an abundance of critical information quickly through Cloud computing will change how everyone within the industry functions, from suppliers to end users. The more machines and systems that are analyzed, the more collective data that can be used to identify which changes to a system or a machine, or even a particular industry, might have the most impact.

In the foreseeable future, we could see computer-generated trends and predictions flowing directly to the OEM so machines can be improved in real time, resulting in a stable, high-speed production process based on the optimal use of the machine.

Data-driven manufacturing is the future. Companies can only reach full digital transformation and realize all the benefits if they’re willing to automate their information data flow just as well as they automate their production, printing, or packaging lines.

Daniel Repp is a business development manager for automation solutions at Lenze Americas. In 2000 he began working in the controls technology department for the company’s German headquarters, and in 2011 relocated to China as a business developer for automation solutions. Repp moved to the U.S. in 2015 to begin working for Lenze Americas.

This article appears in the Applied Automation supplement for Control Engineering and Plant Engineering.

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Daniel Repp
Author Bio: Business development manager for automation solutions, Lenze Americas.