Data exchange across multiple devices has made it possible for industry to evolve towards the smart factory concept
TREELOGIC TEAM| 22/02/2019
The term "smart factory" refers to a focus on industry responsiveness to prevailing market and socio-economic trends by making production more flexible and integrated. In this new context, automation takes on special importance as a key component in state-of-the-art smart manufacturing methods based on the constant exchange of data across all interconnected operations.
This novel manufacturing model is often referred to as "Industry 4.0” and is based on the use of energy-efficient and integrated manufacturing methods to meet the needs of an Internet-based market, resulting in more demand-responsive output. It is precisely this deep level of data exchange that makes integration across the entire value chain possible, both vertically and horizontally. Using this approach, all the data required to optimize productive processes and be more efficient is constantly exchanged.
Although manufacturing has historically been the first sector of the economy in which technological innovations have been deployed, the latest industrial revolution, often referred to as digital transformation, has represented a major challenge for industry. This novel approach to manufacturing takes advantage of all facets of the current technological landscape throughout every stage of production, from planning to the analysis of results.
In turn, smart factories form part of a much wider network of services and products. This new approach to manufacturing is transforming industry and other economic sectors across the board, such as logistics, transportation or distribution, among others, which come together to act as a seamless whole: A comprehensive and intelligent system that aids, as never before, in making life easier for companies serving both consumer and business product-markets.
Data exchange across multiple devices has made it possible for industry to modernize and evolve towards realization of the smart factory concept. Thanks to the innovative deployment of the Internet of Things (IoT), factory equipment and production lines are connected to computer systems, which in turn are linked to software applications hosted in the cloud and controlled by their users. As one can see, everything is connected to everything else to exchange as much data as possible, because information is power, and the more Big Data that can be farmed and analyzed, the greater the chances are of uncovering new competitive advantages.
Some of these advantages have already been mentioned, such as flexibility, efficiency and cost savings, in a scenario in which the most routine tasks are assigned to automated robots, freeing up human operators to engage in new activities that require greater knowledge and training.
The Internet of Things makes it possible for a factory to be largely autonomous. The emergence of the smart factory would not have been possible without the development of the computerized and automated processes essential for everything to work seamlessly, the design and manufacture of factory robots, or the constant supervision of specialists employing data analytics platforms. That is, human engineers, architects, data analysts, programmers and a broad array of multidisciplinary professionals continue to be the foundation of Industry 4.0.
Without a doubt, automation plays a foundational role in the creation of smart factories. It represents nothing less than the heart and soul of state-of-the-art manufacturing, but to grasp the essence of Industry 4.0, we need to go further.
A smart factory is not one in which there are endless production lines manned by automatons that individually perform their sole function: tightening a nut, welding this or that joint, or transporting any given product from one place to another. The smart factory concept is closely tied to decentralized decision-making; that is, the system itself adapts, optimizes, evolves and makes changes independently and automatically. This is where the use of Artificial Intelligence comes in, which enables entire cyber-physical systems to make decisions entirely without human intervention. The operant term that perfectly sums up Industry 4.0 is "learning". New smart factories are capable of evolving and optimizing autonomously; they are constantly learning and analyzing every piece of data that sensors capture to make optimal choices. This capacity to learn allows for the factory itself to respond to shifting requirements, up until now an unthinkable proposition, providing a flexibility that improves processes and helps to reduce costs.
Absent today's data analytics tools, the necessary conditions would not be in place to make smart factories possible. Data exchange is equally essential, but what truly adds value is the analysis of the resulting data. It profits you not to have all the information in the world if you cannot make sense of it to improve performance, which is why Big Data architecture and the people responsible for interpreting the resulting data are the most critical components of Industry 4.0.
A continuous flow of data from sensors spread across the entire production process makes it possible to access all relevant data in real time. In line with the ability to analyze data and act accordingly, and thanks to the advantages offered by Deep Learning, issues relating to supply management, such as of water or power, can be effectively managed. By applying the cutting-edge methods of deep learning, energy use can be tracked in real time and allow pattern-recognition algorithms to identify and predict peak demand for each productive asset. This acts to reduce costs and optimize the production process.
The adoption of digital transformation by manufacturing and related services is essential to industry. Companies that fail to adopt the latest technology in their productive processes will be surpassed in the market by those who do. Knowing how to adapt to constantly evolving competitive and technological trends is today as critical as being responsive to customer and market demand.
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