Before beginning my adventure as an entrepreneur, I worked in business development as a control systems engineer and would frequently go to the city of Vernon, California, a city located close to Los Angeles in Southern California to visit a variety of factories in the area.

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The city of Vernon is a 5.2-square-mile industrial community in Southern California, located several miles southeast of downtown Los Angeles. Vernon, founded in 1905 as the first entirely industrial city in the Southwestern United States, now has over 1,800 enterprises that employ over 50,000 people, making it a key economic engine in the region. With its distinct economic climate, Vernon is home to companies in food and agriculture, clothing, metals, plastics, logistics, and home furnishings. In addition, Vernon has strong charitable relationships with the nearby areas where a large portion of its workforce lives, contributing significantly to public services like health care and education.

Working with different clients at the time and visiting many factories taught me a lot about running and managing a factory. As I embark on the process of launching my venture studio, Seraphim Ventures Studios, in Los Angeles and Nigeria, there are a few concepts for light manufacturing that we are exploring that have the potential to generate favorable opportunities.

The manufacturing industry as a whole is being revolutionized by technology. Therefore, investing in acquiring the knowledge necessary to remain competitive in the coming years is essential. The fourth industrial revolution has been sparked by artificial intelligence (AI) and machine learning. Integrating new technologies, such as big data and predictive analytics, into manufacturing systems will improve efficiency and optimize supply chains.

To meet rising corporate needs, “smart factories” are replacing older, more manual forms of automation with data-driven, networked technologies. A smart factory would be one that can adjust to changing conditions, learn on the fly, and manage the whole manufacturing process with little to no human intervention. Smart factories may function alone or as part of a worldwide network of other similar systems.

Rapid decision-making, rapid change, and data have been integral parts of the manufacturing process. As a result, connecting to new equipment is becoming more important and expensive in today’s linked, complicated production environment. Two potential answers to this issue are the Internet of Things (IoT) and Industry 4.0, each of which has its own unique sets of difficulties and possibilities.

A Look Back

The first industrial revolution, which began in Britain in the late 18th century, paved the way for mass manufacturing by replacing manual labor with water and steam power. Instead of meticulously crafting each item by hand, machines were used to construct the final product.

The First Industrial Revolution

Second Industrial Revolution

Assembly lines and the utilization of oil, gas, and electric power were introduced during the second industrial revolution, which occurred a century later. Mass production and an increase in automation were made possible by these new forms of energy and the improved communication made possible by the telephone and telegraph.

Third Industrial Revolution

Manufacturing processes were further modernized with the advent of computers, high-speed communications networks, and data analysis during the third industrial revolution that started in the middle of the 20th century. To begin the process of automating certain operations and collecting and sharing data, programmable logic controllers (PLCs) were embedded into machines.

Fourth Industrial Revolution

The current era is often referred to as Industry 4.0 or the fourth industrial revolution. More and more steps in the value chain are being automated, and smart equipment and smart factories are being used. Mass customization allows producers more adaptability and better satisfaction with consumer needs. In addition, a smart factory may improve decision-making and information transparency by gathering more data directly from the factory floor and merging it with other company operations.

What the Smart Factory Can Learn from Industry 4.0 Technologies

Smart manufacturing, the method followed by smart factories, is the most efficient use of tools developed during the fourth industrial revolution, often known as the Internet of Things (IoT).

Implementing a single piece of software over the whole factory floor is not enough to increase output in a smart factory. Instead, smart manufacturing efficiency is enhanced by several industry 4.0 technologies working in tandem with one another. Key enablers include:

IoT in Industry (IIoT)

When we talk about the “Internet of Things” in an industrial context, we’re referring to a network of networked devices, equipment, and processes that share information and work together. Typically, these tools feature sensors that upload data to a cloud or offline database so that the manufacturing process may be monitored and optimized. As a result, industrial IoT allows for increased productivity, command, and insight into vital performance indicators.

Sensors

There is now quick insight into several tiers of the manufacturing process thanks to sensors linked to gadgets and equipment that gather data at certain phases of production. For example, using an IoT gateway, temperature sensors in a cleanroom can monitor the temperature and humidity in a laboratory. This information may subsequently be utilized for AI-based course corrections or to flag the appropriate people on the team for inspection.

Cloud Computing

In comparison to on-premises solutions, the flexibility and low cost of cloud computing make it ideal for data storage, processing, and sharing in smart factories. In addition, the ability to rapidly upload vast volumes of data that can be distilled to offer feedback and make choices in near real-time is a significant advantage of the proliferation of interconnected gadgets and equipment on the factory floor.

Big Data Analytics

Insights into production efficiency, essential indicators to prioritize, and failing systems may be gleaned from data collected over time. The massive amounts of data in Big Data make it possible to perform predictive quality assurance accurately and accurately identify error trends. Big data analytics makes faster and more thorough manufacturing process optimization, thanks to the timely delivery of relevant information.

AI and Machine learning

AI and Machine Learning allow manufacturing organizations to fully capitalize on the amount of information created not just on the factory floor but also throughout their business divisions and even from partners and third-party sources. AI and Machine Learning have the potential to provide insights that may provide visibility, predictability, and automation of business processes. For example, there is a high risk of industrial machinery malfunctioning at some point throughout the manufacturing process. With the aid of the data obtained from these assets, organizations can do predictive maintenance based on machine learning algorithms, which results in increased uptime and greater overall efficiency.

Edge Computing

Because of the requirements of real-time production processes, some data analysis has to be done “at the edge,” which refers to the location where the data is being generated. As a result, the amount of time that elapses between the production of data and the need for a reaction is reduced. For instance, identifying a problem with the product’s quality or safety can require taking action with the relevant machinery in almost real time. On the other hand, the amount of time needed to transport data to a corporate cloud and back to the manufacturing floor may be too long; this, of course, relies on how reliable the network is. In addition, when edge computing is used, the data remains close to its source, which helps to reduce any potential security threats.

Cybersecurity

In the past, manufacturing organizations have not always given sufficient attention to the significance of cybersecurity or Cyber-Physical Systems (CPS). However, the connectedness of operational equipment in the factory or field (OT) that allows more efficient production processes also exposes new entry channels for malicious assaults and viruses. OT stands for operational technology. Therefore, it is very necessary to consider a cybersecurity strategy that considers both IT and OT hardware while going through a digital transition to Industry 4.0.

Digital Twin

The digital revolution made possible by Industry 4.0 has enabled firms to develop digital twins, which are virtual clones of processes, manufacturing lines, factories, and supply networks. These digital twins may be used to test and improve the efficiency of these physical systems. Creating a digital twin involves gathering data from various internet-connected sensors, gadgets, and PLCs, as well as other types of items. The utilization of digital twins may assist manufacturers in increasing their efficiency, enhancing their processes, and designing new goods. For example, firms might evaluate modifications to a manufacturing process by simulating the process to identify methods to boost capacity or reduce downtime.

Smart Factory Advantages

To maximize output, smart factories also provide production equipment with features that boost human performance. In addition, smart factories gather data to create an agile, iterative manufacturing process, providing more solid evidence for decision-making.

Smart factories may save money by lowering production costs, minimizing downtime, and maximizing efficiency in their manufacturing processes. In addition, finding and eliminating wasted or underutilized production capacity may lead to expansion without the need for fresh capital or equipment.

Smart Factory: Four Different Levels of Intelligence

The four tiers of data structure may be used to assess the current state of a smart factory and the next set of improvements that need to be implemented.

First Level: Data Analysis

This is most factories as they are right now. The information is there, but it’s inaccessible. Furthermore, manual data sorting and analysis is time intensive and may introduce more inefficiencies necessary for improving output.

Second Level: Data Access

At this point, information has been formatted in a way that is easier to absorb. Data is centralized, well-structured, and searchable, while other tools aid data visualization and dashboard presentation. Although it may take some extra time and work, the factory is capable of proactive examination.

Third Level: Active Data

Data that can autonomously analyze itself with the help of machine learning and artificial intelligence is called “active data.” The system can isolate critical problems and outliers, allowing for accurate failure prediction and timely dissemination of actionable findings.

Fourth Level: Action Oriented Data

This is the point at which the problems discovered in previous phases can be addressed by the machine learning system. This module or system may automatically implement the necessary adjustments to all linked production equipment. Data collection, problem isolation, and solution generation are completed with little human intervention.

How to Build a State-of-the-Art Smart Factory

Although it may appear that replacing every piece of equipment in the production chain would be necessary to switch to a smart factory, this is not always the case.

If you analyze your production line and isolate the most crucial steps, you may implement improvements that will significantly impact your business in a short amount of time. In addition, insight into what needs to be improved after analyzing these foundational facets may be gained through such an analysis.

Experts in various parts of the firm should work together to do this analysis. More employee buy-in means more successful improvements. To ensure staff can effectively handle any new machinery, it may be necessary to provide appropriate training. When personnel monitor systems, compile data and take action on changes, inspections, or repairs, the skills they need will shift rather than decrease.

A strategy should be prepared to investigate optimizing operations, boosting sales, decreasing costs, and saving time throughout production. In addition, engineers should engage with management and I.T. systems experts to uncover areas for an upgrade.

Industry 4.0 and Lean Manufacturing:

Organizations that use lean manufacturing focus on reducing waste while increasing production. The lean manufacturing production approach is a concept that integrates with Industry 4.0 developments to promote efficiency efforts at every level of the manufacturing process. As a result, lean manufacturers benefit from Industry 4.0 by saving time, money, energy, material resources, and human resources, particularly when various IR4 technologies are applied concurrently.

The Effect on Employment

People’s factory jobs will change as the smart factory replaces traditional ones over time. Automated systems will take over routine jobs that are boring or hazardous, while people will take on more sophisticated duties.

While it’s true that automation poses a danger to traditional industrial employment, a new trend known as “the digital skills gap” is also on the rise as more and more businesses choose to use digital technology. Companies’ capacity to adopt digital technology is significantly limited, says MHI’s Annual Industry Report, owing to a lack of trained personnel. Since the widespread implementation of Industry 4.0 technologies will need a higher level of expertise from employees, businesses will need to begin allocating resources toward training and education.

What we Can Learn From These Three Smart Factories

Ocado

Ocado is the biggest UK online-only grocery delivery service. Although it is not a factory in the strict sense, it employs many of the same techniques and methods as manufacturers. Robot arms that can select products and other robots that can pack and move boxes quicker than humans are just a few examples of the high level of automation in the company’s warehouses. Ocado is also substantially funding the development of autonomous delivery vans.

Meanwhile, under its SecondHands initiative, it is creating ‘cobots’ that can learn from and anticipate the activities of human workers. The first results from this internal initiative, the ARMAR-6 robot prototype, were shown off by Ocado in January 2018.

How Ocado came to rely more and more on sophisticated technological systems is a valuable lesson here. However, investing in smart technology and, as the Internet of Business’ Sean Culey recommends in his extensive research, reimagining business procedures for the ‘PAL’ era might be intimidating for some company heads.

However, they may learn from Ocado’s success by understanding that gradual change is not only doable but required to support well-defined strategic objectives.

Hirotec

Hirotec is a Fortune 500 company that exports its vehicle components worldwide. Since scheduled downtime may cost as much as $361 every second, the corporation recognizes this as a significant obstacle.

Hirotec’s goal was to decrease maintenance times, and the Internet of Things made it possible.

Hirotec’s manufacturing facilities use a combination of the Internet of Things (IoT) and cloud-based technologies and compact, durable servers. You may get analytical data from these without giving up any real estate.

Meanwhile, machine learning aids the organization in avoiding costly system problems by predicting them in advance. For example, following the completion of three pilots on its IoT platform and analysis of the resulting data, Hirotec was able to cut manual inspection time for its systems by 100%.

Adidas Speed Factory

Adidas established its speed factory in Ansbach, Germany, with the goal of creating the factory of the future. The company’s speed premise was validated when, with the help of robots, all phases of shoe manufacturing were centralized in a single location. They employ 3D printing technology to produce digital copies or mock-ups quickly, allowing for bulk customization with reduced lead times. Prototypes may be manufactured in a few days, allowing businesses to respond rapidly to fluctuating market demands. Although the Adidas plant in Ansbach has closed, the speed factory technologies are still used in manufacturing Adidas’s sports shoes in Asia.

Using 3D printing, robotic arms, multi-layered particle machines, laser-cutting robots, and computerized knitting, the business claims to have revolutionized production in the footwear sector, displacing human hands in nations like Indonesia, Vietnam, and China. In addition, they are looking at 4D technology and other possibilities to update the manufacturing process.

All of the above-mentioned smart factories are blazing the trail for a brand-new manufacturing method. They represent the zenith of 20 years of digital progress, meeting the time-tested method of mass production. Smart factories reflect the rising need for better manufacturing methods, smarter goods, and a smarter future.

Conclusion

Connected manufacturing in a smart factory uses several technologies to gather and analyze data from the production process to enhance productivity, safety, and other factors.

Optimization may be accomplished in many ways, some of which are listed above. With the use of IoT, data analytics, and sensors, a smart factory may participate in the rise of Industry 4.0, bringing about beneficial changes across the manufacturing process.

Although the advantages are clear, not every company will be able to justify the cost of updating equipment, setting up secure systems, and retraining employees.

Making a smart factory is an investment that should include the whole organization and be justified by thoroughly analyzing the benefits relative to the costs.

Building Tomorrow’s Smart Factory