Industry 4.0: Risks & Benefits of Digitalization

in Manufacturing

Around the world, traditional manufacturing industry is ripe for disruption. The advance of exponentially growing technologies like cloud computing, IoT, Big Data, AI & ML, augmented and virtual reality (AR/VR) herald the most rapid change in how they manage development, production, operations, and the entire logistics chain. Companies need to adapt to this evolutionary bounce (known as the fourth industrial revolution) if they are not to be left behind.

First Signs of Change to Come

From the 70s to the late 20th century, the advent of industrial computers and robotics gave a powerful boost to various industrial sectors, from automotive and electronics to food processing, and propelled new levels of productivity and performance

The advances in electronics and computers provided the means for computer-integrated manufacturing (CIM). For the first time in history, companies could accurately organize key engineering, production, marketing, and support functions of an enterprise in a single system. CIM allowed to reduce errors, increase speed, and improve flexibility primarily through better access to various data and the ability to analyze it for insights.

However, while CIM allowed to achieve a high degree of integration between production and operations, it still lacked in a few critical areas like data collection and processing, data analytics, business intelligence and decision-making. Industrial PCs did make factories smarter and more efficient, yet their capacity to analyze and learn from the data was limited.

Industry 4.0: Enabling Technologies

The next evolutionary breakthrough, from industry 3.0 to industry 4.0, is being propelled by IoT and Big Data, on the one hand, and artificial intelligence and the cloud, on the other.

Industrial IoT

Internet of Things devices mounted on machinery, as well as worker wearables, allow to create a network of machines and products, and also to enable multi directional communication between networked objects. A wider range of specific data points can now be captured for further processing and analysis.

Cloud computing

Traditional CIM systems operate on-premises, which is a limiting factor in terms of processing power, storage, and maintenance cost. With its agility and flexibility, cloud allows to capture, store, and process unlimited amounts of data from IoT, cameras, and sensors quickly and cost-efficiently. It enables real-time communication for and real-time analysis of production systems.

Big Data and Analytics

With all the data from ERP, SCN, MES, CRM, IoT, etc. in the cloud (i.e. Big Data), it can be evaluated and analyzed to drive real-time support and optimization enterprise-wide. It is Big Data — be it structured like tabular data or unstructured like images or video streams — that feeds AI & ML.

Artificial intelligence and machine learning

AI & ML become the driver of smart decision-making (real-time and predictive analytics), accurate quality checks (defect detection, anomaly detection), cost-efficient product manufacturing, effective supply chain management and material handling. AI solutions help companies analyze huge swaths of data to come up with production- and process-critical insights, predict equipment failure, identify defects unnoticeable to human eye, and more.

The combination of these four (though other technologies like additive manufacturing, 3D printing, and AR/VR also play a role) acts as a foundation for industry digitalization and smart manufacturing.

Smart Manufacturing: Benefits and Challenges of Industry Digitalization

A smart factory is a highly digitized and connected production facility, in which data from a wide range of devices is captured, processed, and analyzed in real time to identify opportunities for automating operations and improving manufacturing performance.

Though the smart factory is still in its infancy, manufacturers are slowly digitizing their factories. Early adopters like GE, Siemens, and Bosch report that digital transformation allowed them to reduce labor costs and product defects, shorten unplanned downtimes, improve transition times, and increase production speed.

The full potential of smart factories is yet to be explored. According to McKinsey research, industry 4.0 revolution is expected to generate $3.7 trillion in value for manufacturers and suppliers by 2025. 

 

However, the very same report states that “only about 30 percent of companies are capturing value from Industry 4.0 solutions at scale today,” and this is hardly surprising. Technologies to power the smart factory do already exist, but a number of challenges block widespread adoption. 

 

Primarily, data and systems integration is the problem: connecting all the machinery and people working on the factory floor poses an insurmountable challenge for most enterprises due to poor technical infrastructure and lack of skilled professionals. It can be problematic to assess ROI from industry 4.0 solutions and opportunities (as appropriate business use cases are hard to identify), which results in lack of buy-in from senior management.

Other industry 4.0 challenges are:

  • Resistance to outsourcing

  • Data and cybersecurity concerns

  • Lack of access to proof points

  • Need for large-scale investment

  • Lack of culture of collaboration

It is clear that digital transformation in manufacturing is inevitable. And if you are still asking, “Why do we need smart factories?” consider this: By going smart, on average manufacturers can reduce production costs by ~4%, cut inventory costs by ~3%, drive down equipment maintenance costs by no less than 30%.

Here are just a few smart factory solutions manufacturers can start adopting today:

Quality assurance

Inventory optimization

Predictive maintenance

Supply chain optimization

Product demand forecast

Though implementing any of those can be challenging, contemporary AI quality control systems, for example, can work on data captured from high-resolution cameras and do not require investing into a myriad of sensors. Access control systems need just a few cameras at the facility’s turnstiles for real-time checks while worker safety compliance solutions rely on cameras mounted across the factory floor to monitor PPE compliance. 

 

That being said, oftentimes the resources business think they would need to implement a specific AI use case are exaggerated. And, on the contrary, AI can give them a critical advantage over larger enterprises that still rely on sensors and complex BI systems.

Conclusion

Manufacturing has always been a technically advanced field. With the advance of artificial intelligence, machine and deep learning, however, manufacturers need to move forward to transform their factories — and the entire value chain — even more rapidly. 

 

They have to work hard to squeeze improvements in efficiency, to strengthen their position against the competition and to avoid disruption. Now, their opportunity is to go smart, from industry 3.0 to industry 4.0, from assembly lines and industrial computers to smart manufacturing enabled by industrial IoT, cloud computing, Big Data, and AI.

Thank you for reading! 

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