Digital for Non-digital People

This in and of itself is a complex question, and the best answer really lies in the level of “digital maturity” of you, the reader, and of your organization in general. Many operators are taking a wait and see approach, waiting for competitors who use new digital technologies to “work out the bugs.” This would be indicative of low digital maturity. 

So, let’s begin by identifying what digitalization is, how it relates to digital transformation, and how it facilitates the use of AI. Then we’ll discuss how you might use AI to improve your manufacturing process. If you feel confident in your understanding of this term, then feel free to skip reading the next section and go directly to the AI section to save some time. 

Digitalization – what does it mean? 

Digitalization is the process of converting information into a digital format that can be easily stored, processed, and accessed using computers and other digital technologies. Having your organization’s data in a digital format makes it easier to store, share, manage, manipulate and analyze it using computers and other digital devices and allows you to take advantage of the many benefits of digital technology, such as improved efficiency, greater accuracy, and more accessible information. 

So, what does this mean for our customers? A continuous improvement in production efficiency has always been considered one of the key prerequisites to ensure the competitiveness of globally operating companies, and therefore customers are either starting or have started the road of digital transformation. 

Digital transformation is a broader concept than digitalization and involves the use of digital technologies to fundamentally change the way that businesses and organizations operate. In the context of manufacturing, digital transformation can help to improve processes in several ways, including the following. 

• Increased efficiency: Digital technologies can be used to help automate many tasks and processes in manufacturing, thereby reducing the need for manual labor and reducing the time and resources required to complete these tasks. Automation can help to improve overall efficiency by reducing costs and increasing productivity. 
• Improved quality: Digital technologies can also be used to collect and analyze data from manufacturing processes in real time, allowing manufacturers to identify and address potential quality issues before they become major problems. Responding in real time can help improve the quality of products by reducing defects and increasing customer satisfaction. 
• Enhanced agility: Digital technologies can be used to help manufacturers be more responsive to changing market conditions and customer demands. By using digital tools to track and analyze market trends and customer preferences, manufacturers can quickly adjust their production processes to meet changing demand, which helps them to remain competitive in a rapidly changing market. 
• Increased innovation: Digital transformation can also help manufacturers to innovate and develop new products and services. By using digital tools to analyze data and identify new opportunities, manufacturers can develop new products and services that are tailored to the needs of their customers, thereby helping them to stay ahead of the competition. 

Key technologies that are commonly used in digitalizing manufacturing processes include: 

• Computers: Digitalization often involves the use of computers to store, process, and manage data. This can include everything from traditional desktop and laptop computers to more specialized devices such as servers, cloud computing systems, and other types of computer hardware. 
• Software: Digitalization also typically involves the use of various types of software, including applications, operating systems, and other specialized software tools. These tools are used to manage and manipulate data, as well as to analyze and visualize it in various ways. 
• Networking and communication technologies: Digitalization often involves the use of networking and communication technologies to connect different devices and systems, allowing them to share and exchange data. This can include everything from local area networks (LANs) and wide area networks (WANs) to more specialized technologies such as Bluetooth, Near Field Communication (NFC), and other wireless communication protocols. 
• Sensors and IoT devices: In many cases, digitalization involves the use of sensors and other Internet of Things (IoT) devices to collect data from physical objects and processes. These devices can be attached to machinery, vehicles, or other objects, and can be used to monitor temperature, pressure, or other physical properties to provide valuable data that can be used to improve manufacturing processes. 

Now that you know what digitalization is and how it fits within the broader topic of digital transformation, let’s discuss how you can use it in your organization. 

AI – how can it be used to improve your industrial manufacturing processes? 

Digitalization, and the technologies and tools that work with it, can change the way you conduct business and make certain decisions because the actionable information AI provides facilitates decision making and improves intended outcomes. AI already has proven useful to manufacturers in several areas, including various types of processes and analytics. Consider the following areas. 

1. Predictive maintenance: AI algorithms can analyze sensor data from equipment to predict when maintenance will be needed, reducing downtime and increasing efficiency. 
2. Quality control: AI-powered imaging systems can inspect products for defects, reducing human error and increasing accuracy. 
3. Process optimization: AI algorithms can analyze production data and recommend ways to improve efficiency and reduce waste. 
4. Autonomous robots: AI can be used to control robots and other automated equipment, allowing for more precise movement and the ability to adapt to changing conditions in real time. 
5. Intelligent scheduling: AI algorithms can optimize production schedules to reduce costs, improve on-time delivery and increase capacity utilization. 
6. Intelligent inventory management: AI can be used to predict the demand for products and thus to optimize inventory accordingly, thereby potentially reducing lead times and decreasing inventory holding costs. 
7. Intelligent procurement: AI can be used to predict the demand for raw materials and to optimize the procurement process, reducing lead times and costs. 
8. Workforce management: AI can be used to optimize staffing levels and schedule employees to improve productivity, reducing labor costs and improving employee satisfaction. 
9. Predictive modeling: AI can be used to predict the performance of production systems and identify potential issues before they occur. 

To see practical examples with real world experience, consider these three areas, process optimization, autonomous robots and predictive modeling, where Solenis applies sophisticated digital solutions, including AI, to help our customers improve their manufacturing processes. However, even AI has limits. In fact, this blog proves that point. 

Much of this blog was generated by an open-source AI chat engine called Chat GPT. It was trained by a massive data set that is a couple of years old. And one example of AI that was missed in their list of nine uses was the ability of AI to predict in real time a measurement that is normally only done by humans, for example, a sample taken from a continuous manufacturing process to conduct a quality test. This is in fact what we do at Solenis for our pulp and paper customers using our OPTIX™ advanced analytics platform. We use AI developed with our partner, ProcessMiner, to predict, for example, the strength of paper as it is being produced in near real time so that the mill operators don’t have to wait hours for test results—only, perhaps, to find out that quality went down during the run—when the issue could have been addressed (process optimization) in a timely manner had they known of it in real time. 

It is this real time prediction of quality parameters that allows us to deliver on autonomous robots. Autonomous control of robots or equipment can make real-time adjustments to maintain quality standards continuously. In our case, we typically adjust the dosage of various paper chemistries which we supply in order to course correct the manufacturing process so that the customer does not end up with a batch of off specification product at the end of the run. We are adjusting the speed of chemical dosing pumps, autonomously. This leads to many other benefits, also, as noted previously. 

So, while it is still very early for the use of AI in industrial applications, there are quite a few operators actively using digital solutions like OPTIX. And it is the early adopters who will ultimately grow their competitive advantages in manufacturing over those who choose to wait and see. 

To learn more about our portfolio of digital technologies, visit our Digital Solutions landing page or contact your Solenis sales representative.