Industry as we know it is just over two and a half centuries old, yet it has created an unprecedented wave of change in the history of technology and mankind. Since the time of the first industrial revolution, in fact, technological progress has accelerated exponentially. Conventionally, digital transformation corresponds to the Fourth Industrial Revolution, the one linked to the concept of Smart Manufacturing, that is, of the total integration of information technology, data and physical systems. Or, using a neologism, the emergence and adoption of cyber-physical systems.

Smart Manufacturing and Industry 4.0: similarities and differences

The main difference between Smart Manufacturing and Industry 4.0 is geographical and economic. In fact, the project that goes by the name Industry 4.0 is European in origin, has Germany among the ringleaders, and has a particular incidence in Europe.

The definition of Smart Manufacturing, on the other hand, has greater traction in the United States, thanks to an initiative called the Smart Manufacturing Leadership Coalition (SMLC), a nonprofit organization made up of representatives from across the manufacturing supply chain, universities and research centers.

The two projects have many assumptions in common: interoperability between systems within the entire supply chain, the creation of standards and the use of increasingly advanced IT solutions to make processes more efficient, cost-effective, flexible and scalable.

The centrality of data in the smart industry

The idea behind Industry 4.0 and Smart Manufacturing is that every element of the production chain, including products, is connected through the input of sensors, measurement and monitoring tools, and RFID chips. In this context, for example, logistics management is done in an automated and efficient way, based on real-time monitored production pace, delivery availability, carrier load, and so on. For everything to work, every element of the supply chain must exist simultaneously in both the physical and virtual worlds.

Another concept behind Smart Manufacturing is that of decentralization of control. At each step in the production chain, smart control systems make it possible to optimize the specific process step and, at the same time, to dialogue with the rest of the chain to modify and streamline the process in real time. All this while it is possible, again thanks to data interchange, to monitor processes through analysis platforms advanced.

Smart Manufacturing: advantages

The main stumbling block to the adoption of Smart Manufacturing is undoubtedly the apparent complexity introduced by the integration of control and monitoring systems. But the proven and demonstrable benefits far outweigh the possible initial difficulties. Here are some examples:

  • Demonstrable Productivity Improvement: access to new data sets and continuous monitoring of the production chain allows on the one hand to improve the efficiency of production processes, even on an instant-by-instant basis, and on the other hand to have access to analytics that allow improvements to be demonstrated and monitored.
  • Increased flexibility: the ability of processes to adapt and optimize rapidly allows a smart factory to adapt to changes, internal and external, and also to changing market conditions.
  • Openness to continuous innovation: time and cost optimization leaves more room for research and development, but not only that. Thanks to complex analytical models, and the support of solutions such as machine learning and artificial intelligence, it is also possible to revolutionize the concept of modeling and prototyping, moving them from the physical to the digital world and consequently reducing effort and costs.
  • Increased production quality: the analysis of data, the ability to make improvements quickly and responsively, and improved productivity enable more resources to be invested in product quality and also in market and customer needs, which can be accommodated more quickly and efficiently.
  • Energy efficiency: a particularly topical issue in Europe, the green conversion of manufacturing operations is much easier within controlled supply chains. Efficiency in processes also means energy efficiency and reduction of production waste. Which, ultimately, can also translate into a reduction in the final cost of the product and the resulting increased competitiveness.

Some examples of industry 4.0

Digital simulation, of warehouse movements: in the context of Industry 4.0, data-driven simulation is key to devising and testing warehouse solutions before their actual implementation. This solution makes it possible to predict the performance of a warehouse, enabling the creation of operational cycles in a virtual environment before their application in the real context.

Intelligent and autonomous warehouse vehicles: the introduction of intelligent vehicles that operate autonomously is a breakthrough in Industry 4.0 for the internal transportation of products. These vehicles are capable of moving within facilities, eliminating the need for a human operator to drive, even reaching inaccessible or dangerous areas without risk to people.

Projects and analysis based on big data: big data processing and analysis are radically changing the management of production cycles under Industry 4.0. Through in-depth examination of this data, companies can identify points of improvement, optimizing efficiency and minimizing costs.

Digital Twin and Cobot: A digital replica, or Digital Twin, is a 3-D representation of an item, either existing or being designed, enriched by data from enterprise big data. Through VR or AR devices, these replicas offer realistic immersion. On the other hand, cobots, or robots designed to interact directly with humans, operate without the need for physical separations or safety barriers.

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