The Industrial revolution 4.0, popularly known as Industry 4.0
, has become increasingly crucial in modern manufacturing for a multitude of reasons, and mainly constitutes the next wave of technology by driving efficiency across operations. What would cause organizations to fall behind is when they fail to adopt the technology of the Fourth Industrial revolution
since the operations of these organizations will not be digitalized enough to match their competitors.
According to a MarketsandMarkets research report, the Industry 4.0 market size is anticipated to reach USD 156.6 billion by 2024, globally. Factors bolstering this market growth include the increasing adoption of the industrial internet worldwide in manufacturing units, increased attention on enhanced efficiency of machinery and systems, and reduced production costs.
Revolution On the Anvil
Industry 4.0 is revolutionizing the way factories manufacture, improve, and distribute their products. While manufacturers are integrating enabling technologies, such as IoT, cloud computing and analytics, AI, and ML into their manufacturing facilities and throughout their operations, these smart factories are equipped with advanced sensors, embedded software, and robotics that collect and analyze data which help in better decision-making.
Not just that, even higher values are created when data from production operations is amalgamated with operational data from various sources such as Enterprise Resource Planning, supply chain, customer service, etc, to give a whole new level of visibility and insight from previously siloed information. This technology has not only resulted in increased automation, but also predictive maintenance, and self-optimization of process improvements. Above all, it provides a new level of efficiencies and responsiveness to customers that were not previously possible.
Building smart factories offers an incredible opportunity for manufacturers who are entering the fourth industrial revolution. Furthermore, analyzing the humongous amounts of data that are collected from sensors on the factory floor ensures real-time visibility of manufacturing assets and helps in providing tools for predictive maintenance to minimize equipment downtime. Using IoT devices in smart factories leads to higher productivity and improved quality.
While replacing manual inspection with AI-powered visual insights reduces manufacturing errors and saves money and time, the quality control personnel can set up a smartphone connected to the cloud to monitor manufacturing processes from virtually anywhere, with minimal investment. The highlight is that manufacturers can detect errors immediately, instead of finding them at later stages when the repair is more expensive and this can be achieved by applying ML algorithms.
Challenges in Implementation
Industry 4.0 which is the manufacturing side of digital transformation has the enthralling potential of “smart factories,” which delivers intelligence on demand. Also, the evolution of data-driven autonomous systems and ML tools reinforces the promise of Industry 4.0 since organizations/factories look to connect IoT devices, gather critical metrics and also visualize data in real-time, analyze results and optimize manufacturing processes.
While a typical factory now generates one terabyte of production data every day, 90 plus percent of this data isn’t properly utilized. Then, how do the manufacturers leverage their existing resources, implement new solutions, and also revolutionize factory-floor intelligence?
With the advent of IIoT, amazing technologies such as robotics, 3D printing, advanced analytics, and Artificial Intelligence now offer the potential for connected, additive, and autonomous manufacturing processes. And, this is critical for organizations that are looking to extend their machines’ lifespan, increase throughput and reduce device breakdowns.
However, organizations face a major challenge and the challenge is ‘connection’. When manufacturers are asked regarding IoT, most of them will say that they have been doing it for ages now by leveraging networked “legacy” controllers and sensors. These include PLCs and smart devices that store data both locally and with historians. But unfortunately, today, this is not enough, as most of the assets in factories are in isolation.
Furthermore, the smart factories of Industry 4.0 will have to deliver better device security, ease of connectivity, and common platforms, to meet IIC consortium standards.
Lastly, by bridging the gap between legacy and transformative technologies, the manufacturers can gain access to existing data sets and new data streams as well. Most importantly, before businesses can leverage large-scale production/manufacturing data, they should have a reliable way to save them, store, aggregate, and cross-compare this information.
Innovations in the Manufacturing Sector
Industry 4.0 is beckoning a change in the traditional manufacturing landscape, and it encompasses three technological trends that are driving this transformation such as connectivity, intelligence, and flexible automation.
While Industry 4.0 combines Information Technology and Operational Technology that creates a cyber-physical environment, this convergence has been made possible only through the emergence of digital solutions and advanced technologies that are often associated with Industry 4.0. These technologies include additive manufacturing, the Internet of Things (IoT), Cloud computing, advanced robotics, AI and machine learning, edge computing, cybersecurity, digital twin, etc.
The greatest advantage of embracing industry 4.0 digital manufacturing and the interconnectivity that comes with it is that it provides flexibility, greater agility, and improves operational performance. According to research, AI-powered manufacturing with solutions deployed at the edge can drive up to 30 percent yield improvements and 15 percent waste reduction, and 5-10 percent reduction in operating costs and it can also accelerate an organization’s journey to Industry 4.0.
Now let us look into big data and data analytics, which is one of the most important Industry 4.0 technologies, implemented by most organizations around the world.
Big Data & Data Analytics
Big data is nothing but the large and complex data sets generated by IoT devices. This data is generated from a large range of cloud and enterprise applications, websites, computers, sensors, cameras, etc. that are all coming in various formats and protocols.
Especially in the manufacturing sector, there are various types of data to take into consideration, such as the data being generated from production equipment fitted with sensors and databases from Enterprise Resource Planning systems, Customer Relationship Management, and Manufacturing Execution Systems.
Emphasizing the significance of data, Andy Rowland, Head of Digital Manufacturing, BT, stated that, “Data is fueling the growth of Industry 4.0.” Since data breathes life into modern technologies, it is highly crucial to have real-time data be fed into various technologies – right from AI to AR.
Greater insight into the manufacturing plant’s operations supports better and faster decision-making throughout the entire organization.
But how is the data that is collected turned into actionable business insights?
This can be achieved with data analysis as it helps in organizing and managing the humongous data by making it actionable and ready for actual use. In Industry 4.0, data analytics is what fuel is to vehicles. It is highly critical from every stage right from basic prototyping to manufacturing to maintenance. Data analytics not only helps drive innovation but also strikes the harmony within several ecosystems which make industry 4.0.
Not just that, it also immensely facilitates resource allocation, resource optimization, asset planning, asset utilization. Lastly, Industry 4.0 is all about gathering data from diverse resources, pooling it together in the data lake, and then leveraging it to drive modern tech.
Monitoring the Overall Manufacturing Process
A German multinational engineering and technology company, by combining IIoT and Big data, used it as a recipe to drive the digital transformation of its Automotive Diesel System factory. The company connected its machinery to monitor the overall manufacturing process at the core of its manufacturing facility, which was achieved by embedding sensors into the factory’s machines that are then used to gather data about the machines’ conditions and cycle time.
Once the data has been collected, the collected data is processed by advanced data analytics tools in real-time. Then, these tools alert workers whenever any bottlenecks in the manufacturing operations are identified. Following this approach helps in the prediction of equipment failures, enabling the factory to schedule maintenance operations well before any failures occur.
Therefore, the factory can keep its machinery running and operating for longer periods and the company states that using data analysis in this way has contributed to more than 10 percent output increase in certain areas while improving delivery and customer satisfaction.
And ultimately, a greater insight into the manufacturing plant’s operations supports better and faster decision-making throughout the entire organization, which enables it to reduce equipment downtime and optimize production processes.
Although AI has found its way into everyone’s life, either through a smartphone or smart assistants, etc., the manufacturing industry has just begun to consider AI integration and the various other technologies associated with Industry 4.0, seriously. Also, while the year 2020 has shown us the significance of digitalization in the manufacturing industry, the years to come will reveal who is ready for the factory of the future and who is not. Therefore, organizations that embrace not just the opportunities but also the challenges of a more digital and virtual world will be successful in their businesses.