August 16, 2021
Digital transformation in manufacturing: top trends of Industry 4.0
Some etymologies simply don't age so well. One of the most striking cases is that of manufacturing. Coming from the Latin words "manus" (hand) and "factura" (workmanship), this term sounds like a reminiscence of a distant past in which the making of goods was mainly performed by hand.
As we all know, times have changed. Following every major technological upheaval, we have witnessed a reduction of human involvement in production processes, at least in purely physical terms. But the potential "coup de grace" may come from the massive digitization that has been going on for several years in the manufacturing sector.
What are the main trends and practical implications of the digital transformation in manufacturing and what challenges does it pose to workers, management, and shareholders? Let's try to give an honest and realistic answer to these questions.
In recent years, the manufacturing industry already exhausted by the growing market competition has also ended up dealing with the operational and logistical disruption caused by the COVID-19 pandemic, encompassing supplying, production, and distribution, to a degree never reached before.
How to adequately address this historical turmoil and ensure the continuity of the production flow while avoiding labor shortages and supply-chain fragmentation? Well, the need to make manufacturing systems more flexible and resilient in highly unstable or variable scenarios such as the recent outbreak, alongside the drive to increase productivity and competitiveness, have acted as catalysts for the shift towards the so-called Industry 4.0.
During this transition, many companies have turned to cutting-edge digital technologies designed to promote automation and connectivity via smart devices, integrating them into virtually any area of their business. For example, we may count IoT, cloud computing, big data analytics, and artificial intelligence plus its machine learning sub-branch and cognitive technologies.
In this regard, Fictiv's 2021 State of Manufacturing Report highlighted how 91% of respondents said they had increased their investments in digital transformation following the pandemic, while 77% said that this growth was significant or dramatic.
IBM's 2021 Digital Transformation Assessment also confirmed this positive trend, reporting that 67% of the manufacturing decision-makers surveyed accelerated their technology adoption plans after the COVID-19 crisis. Specifically, their plans focus on:
Digital transformation in manufacturing not only encompasses a constant evolution in the technology stack deployed by organizations to achieve their goals but also marks a profound change in their business model. Transforming a traditional manufacturing corporation takes digital enterprise consulting to properly leverage these new technologies and harmonize all the different business aspects impacted by them.
Such a radical evolution may raise more than one question among employees worried about their professional future and stakeholders reluctant to invest a relevant portion of the corporate budget in digital transformation. Regarding the first issue, the way to go is proper retraining to make the staff capable of interfacing with new technological tools and to reallocate them in new job positions if the previous ones have been taken over by machines.
As for the second problem, a good solution is to set up a gradual implementation plan that ensures small achievements in the short term. Such results should help convince investors and management of the potential advantages of digitalization. After all, showing concrete accomplishments is the first step in changing the corporate culture, making it ready for innovation.
As you may have understood, when we talk about digital transformation in manufacturing we’re dealing with a holistic change in the way of doing business that requires proper investments and efforts. According to PwC's 2020 Digital Factories report, the size of such investments in the next five years will vary greatly by industry, ranging from 8% of total revenues in the consumer goods sector to a "mere" 4% in the mining industry.
Fortunately, the benefits that can be achieved at the end of this journey match expectations. Some of the most relevant advantages reported by companies that have embraced the "smart factory" model are:
The achievements described so far wouldn't be possible without unleashing a full array of innovative technologies in each phase of industrial production. According to IBM's aforementioned study, the list of the most common technologies adopted in Industry 4.0 includes advanced data analytics, cloud, automation, IoT, AI, 3D printing, and cybersecurity, with the latter leading the way and implemented by 88% of the corporations surveyed.
Let's look at some of the tech trends of digital transformation in manufacturing, focusing on those with a direct and distinct impact on how smart factory processes are performed and starting with the way we harness the power of data.
As coal, oil, and gas have been the fuel of industrialization, we may say that data is the true fuel of digitalization. In recent years, indeed, the harvest and processing of big data have proven to be the ultimate tricks up the sleeves of many major market players, unlocking limitless possibilities in virtually every industry.
The potential of data analytics techniques, especially in terms of predictive capabilities and data-driven decision-making, has been further enhanced by integrating those with machine learning. This branch of AI focuses on creating algorithms that can investigate immense datasets and detect recurring patterns or relationships among them to obtain precious insights on various scenarios and phenomena. Another essential feature of these algorithms is their ability to improve with experience, becoming more and more skilled as they process new data.
In the manufacturing sector, data analytics and machine learning's predictive powers can be leveraged by probing logistic and market data (including customer sentiment on social networks) to forecast future trends and meet demand with proper restocking. It can also be used to process information regarding the overall factory workflow, evaluate KPIs, and spot bottlenecks or processes worth improving and harmonizing.
Finally, machine learning-powered data analytics is an important aspect of asset performance management (APM). Specifically, it’s commonly adopted to scan machinery performance data collected via sensors and detect any inconsistencies which may be signs of malfunction. This approach to anomaly detection with machine learning and analytics ensures greater asset reliability, longevity, and productivity by improving maintenance operations, while also resulting in improved worker safety.
Turning to an industry case, Bosch Rexroth has implemented an online diagnostics network named OdiN, which is infused with predictive analytics capabilities to improve its machine monitoring cycles, resulting in up to 50% faster maintenance operations.
What we've just described would not be possible without a sprawling network of interconnected smart devices communicating with each other known as the internet of things (IoT). Such devices are typically equipped with a wide range of sensors and powered by advanced software, making them capable of collecting, processing, and exchanging data with other digital tools via the internet.
In the manufacturing field, this implies the possibility to monitor and manage industrial equipment non-stop at a fraction of the usual cost, thanks to a constant flow of information regarding the system status. Networked sensors can also be connected to the power grid, providing greater control and flexibility when it comes to energy consumption.
It goes without saying that collecting and managing such large amounts of data may require a little "extra push" in terms of both storage and computing power. That's why IoT solutions are typically combined with cloud technologies, resulting in higher processing capabilities that are cheaper and faster to get compared to using a client-server model.
An example of this synergy comes from Siemens' cloud-based IoT system called Mindsphere. This manufacturing-centered solution can gather data from interconnected factory equipment, process it with AI-powered analytics, and deliver insights to optimize industrial processes.
We've already mentioned the importance of AI and machine learning-powered systems in processing data and predicting future outcomes. But artificial intelligence represents a valuable ally for the manufacturing industry even when it comes to delegating more and more tasks to robots and drones. After all, you've probably already seen some fascinating examples of fully automated assembly lines, in which human employees play purely a supervising role.
Actually, industrial robotics is definitely not something new, but thanks to recent developments in machine learning and other AI-related cognitive technologies, the concept of automation has taken on a whole new meaning. Today, robots can progressively improve their performance by learning through experience, endowed with the ability to see through computer vision.
These skills allow them to be adopted for a myriad of different tasks, replacing or assisting human employees in performing some of the most stressful and dangerous activities faster and with superior accuracy, including assembly, maintenance, logistics, and storage. Regarding the latter, for example, the Austrian automobile manufacturer Magna Steyr deployed a flock of smart drones to enable autonomous inventory, i.e. to scan material labels and assess the stocks available in its warehouses.
The final aspect of digital transformation that is worth mentioning in our brief overview concerns additive manufacturing, typically referred to as 3D printing. This technique implies the creation of a tridimensional object starting from a digital model and depositing different types of materials (usually polymers but even ceramics and metals) layer by layer under computer control.
It may not sound as romantic as carving a magnificent statue from a block of Carrara marble. But this flexible and cost-effective way of translating digital prototypes into the physical realm has ensured a significant acceleration of production cycles as it unlocks the possibility to transform design projects into products directly on-site, without ordering and assembling several sub-components.
Therefore, it is not surprising that the adoption rate of such technologies in manufacturing is projected to grow from 18% in 2020 to 37% within five years, according to PwC's estimates.
This trend encompasses a variety of sectors, including the automotive industry. In this regard, Volkswagen has pioneered additive manufacturing by implementing 3D printing for high-volume production.
Specifically, the German carmaker has adopted a cutting-edge technique known as binder jetting in which an industrial printhead releases a binding liquid onto a layer of powdered material to solidify it, instead of molding the component layer by layer. This procedure allows them to create metal parts weighing approximately 50% less than typical components built from sheet steel.
As underlined by Deloitte, over the past two decades, the manufacturing sector appears to have gotten bogged down, showing an annual labor productivity growth rate that is increasingly negligible. In the United States, for example, this metric went from approximately 6% in 2010 to zero net average during the last five years.
Therefore, we may say that the COVID-19 outbreak was something like the straw that broke the camel's back in a sector already losing its momentum and at risk of stagnation. However, after this series of unfortunate events that not even Lemony Snicket could have imagined, digital transformation may be the key to reverse the recent trends in the manufacturing industry and escape the impasse.
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