What is needed to achieve smart manufacturing?

Globally, production in the industrial sector is under pressure – due to several interrelated factors:

Context and current situation

Ever-changing customer expectations, the need to accelerate time to market, skyrocketing inflation and supply chain disruptions. The problem is not simple: “How do manufacturers face these challenges?” “. In such a competitive environment, where new manufacturers appear, armed with the latest equipment and technology, the question arises “how do manufacturers develop?”.

To increase their competitive advantage, many industry players are looking to leverage “smart manufacturing” – the concept of integrating technology, data, processes and human interactions to improve manufacturing outcomes.

However, some are still hesitant to adopt new technologies due to concerns about system compatibility and scalability, scaling, as well as the need for significant investment.

To overcome this hesitation, we need to rethink the way business leaders think about managing and integrating “data” and “process”. It’s not just about adding new technologies: for smart manufacturing solutions to be successful, organizations must achieve true convergence between their factory (or operational technologies, or OT) and their business (or information technology, or IT). They need to manage factories in a computerized way and consider operations, workflows and human interactions more holistically within the wider business context.

The architecture behind smart manufacturing

Manufacturers need a foundation that allows them to design, scale, and execute discrete software-defined manufacturing functions on a single, cloud-based platform.

This underlying architecture includes hardware and software assemblies that integrate various functions, including process control, visualization, and data acquisition. It also means having real silicon, optimized for demanding industrial applications that can converge different applications that would otherwise require multiple CPUs, GPUs and accelerators. This saves space, energy and costs while providing exceptional data processing and process control performance. As an example, hardware and software components can be industrial computers that integrate multiple functions, such as visualization and process control, to simplify the installation and maintenance of production systems. Indeed, advanced silicon for demanding industrial applications can be used in sensors that measure parameters such as temperature or pressure to monitor manufacturing processes in real time. Additionally, consolidating multiple applications onto a single hardware platform can reduce space requirements and power consumption in factories, which can lower operating costs. And finally, data processing and process control performance can be used to optimize production in real time, enabling manufacturers to detect quality or performance problems in manufacturing processes and quickly correct them.

Program-defined production

Traditional manufacturing has long been defined by hardware, where individual pieces of equipment are designed for a repetitive task. Factory changes or upgrades may require significant investment.

In contrast, in software-defined manufacturing, factories operate like a computer system. This means that the software can configure, monitor and manage machines and their processes throughout production. It allows manufacturers to do more with existing hardware and allows hardware to have more functions or be turned to other purposes.

A traditional factory operates independently and must be supervised and controlled by staff. However, it can also function as a computerized system through software-defined manufacturing, where machines are equipped with sensors that send real-time information to a centralized control system. This system can monitor and manage machines and their processes throughout the production facility, detect malfunctions or malfunctions, and send alerts to the technician. The software can also allow a single machine to have multiple functions or to be rotated for other purposes, reducing the cost of investing in new machines. By using software-defined manufacturing, a factory can become more efficient, flexible and profitable, while producing high-quality products.

This brings greater flexibility and faster programming both for individual machines and for the entire production process through a single interface. Manufacturers can also virtualize physical machines to create digital twins in an on-premise or cloud environment to simulate the impact of an upgrade on a production line. With AI and machine learning at the edge of production, data can be analyzed closer to where it was collected, and adjustments can be made in near real-time to optimize operations.

Digital twins can also be used to train employees on new production equipment and processes before they are implemented in an actual production environment. Employees can practice using equipment and following production procedures in a virtual environment, reducing the risk of errors and costly downtime in a real production environment.

In short, digital twins play a key role in the automation of production in factories, enabling greater flexibility, faster programming and continuous optimization of production processes.

The next step in an intelligent, software-defined manufacturer journey is to enable frequent updates, upgrades and upgradability.

A true convergence between OT and IT

Traditionally, operational technologies and information technologies operate separately. In this context, factory machines and equipment that are part of operational technologies are very often neither networked nor interconnected. These are often proprietary, verticalized systems that operate in silos. Due to the lack of compatibility or common standards between machines, human operators must monitor and manage the programming and physical operations of each piece of equipment.

Emerging technologies, in the form of the Internet of Things (IoT), are bringing machine-to-machine communication and data analysis to the fore, enabling convergence between the worlds of OT and IT. From a business perspective, IT breaks down OT’s information silos by sharing and processing data exchanges across manufacturing plants. The result is intelligent automation that helps streamline workflow and improve productivity.

New technologies and machine compatibility are only the first step on the way to smart production. The next steps are the differentiator that will connect the factories to the business as a cohesive whole and create a real competitive advantage.

in summary

With manufacturers engaged in a smarter, software-defined journey, the rest of the industry ecosystem must also follow suit. Smart manufacturing can only succeed if the entire ecosystem, including original equipment manufacturers (OEMs), factories, system integrators and others, can integrate technologies, data, human processes and interactions together.

In this future of smart manufacturing, every player in the ecosystem has adopted an open, fully programmable, unified standards-based system, so manufacturers have choice, flexibility and interoperability to optimize operations and drive innovation, regardless of the vendor or suppliers they use.

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