• Data and Connectivity: It focuses on collecting and managing data points from various sources, including manufacturing components, workforce productivity, and maintenance activity. It also connects multiple production systems to help manage manufacturing more efficiently by leveraging technologies like sensors, IoT, and Cloud.

• Human-Machine Collaboration: Augmented and virtual reality are some of the technologies that facilitate human-machine collaboration. For example, an engineer can remotely monitor maintenance activities performed through robotic automation and intervene when standard automated maintenance procedures fail to solve an issue. Similarly, the human workforce can monitor the manufacturing work of robots. Through human-machine collaboration, manufacturers can delegate dangerous, laborious, and manual work to robotic systems and drive down the production costs and time to market, even in locations that traditionally attracted relatively high manufacturing costs.

• Analytics and Intelligence: This domain focuses on uncovering insights from big data collected from multiple data streams such as machine sensors, consumer activity, energy consumption, and production costs, among others. It also aids in intelligent automation and drastically reducing manual interventions. Senior executives can leverage the insights generated from analytical models to empower their decision-making, leading to more significant gains in cost-cutting and manufacturing operations optimization.

• Advanced Engineering: It helps in leveraging advanced manufacturing techniques and models such as 3D-printing and nanoparticles. While the other three domains are rooted in IT, advanced engineering focuses on introducing fundamental changes in operational technology and manufacturing methodologies to deliver business value.

Industry 4.0 Trends to Unlock Higher Business Value

In the next few years, manufacturing companies will increasingly try to adopt the following Industry 4.0 technologies to unlock advantages in production optimization and revenue generation to gain a competitive edge.

Digital Twins

A digital twin is a virtual representation that replicates a physical system or object using real-world data and simulation techniques. It dynamically adjusts itself to reflect the current state of being of the actual physical system. Digital twins help in closely understanding the behavior of real-world systems and in performing what-if scenario analysis.

In a manufacturing facility, for example, a digital twin can help validate whether a production system is implemented as planned or analyze various what-if scenarios to determine which conditions contribute to less downtime and more production.

Hyperautomation

Hyperautomation is the next level of automation that focuses on automating more knowledge work beyond robotic process automation (RPA). It leverages advanced technologies like machine learning, natural language processing, and computer vision. Moreover, it employs process discovery tools to uncover workflows intelligently and automate them. Furthermore, it uses advanced analytics to measure the business impact of automation and the ROI delivered.

Intelligent Data Platform

Data has become the core of manufacturing companies, and efficiently collecting, storing, managing, and analyzing data is critical to driving innovation and process improvement. An intelligent data platform scalably manages data and integrates seamlessly across various business-critical systems to enable companies to leverage their data more effectively. It also aids in managing compliance, integrating with external data sources, and delivering business-critical insights.

Industrial Internet of Things (IIoT)

IIoT extends the internet of things to industrial systems by interconnecting machine sensors and devices that network intelligently to enable AI-based machine-to-machine communication and data collection. It leverages cyber-physical systems, cloud and edge computing, analytics, and machine learning to deliver business value. It aids in intelligent automation and helps companies create new business models beyond delivering production efficiencies. However, implementing and managing an IIoT strategy could become complex without deep capabilities in managing such distributed systems.

Supply Chain 4.0

The pandemic revealed the importance of digital supply chains and the benefits they provide. Supply chain 4.0 uses Industry 4.0 technologies to deliver even more business value than digital supply chains. It focuses on reducing delivery times from days to hours, offers flexibility by integrating with a multitude of digital supply chain services, and helps achieve micro-segmentation and mass-customization to satisfy increasing customer expectations.

Conclusion

It can be daunting to determine how to implement Industry 4.0 solutions in a company’s manufacturing processes. Instead of profoundly analyzing which type of implementations drive efficiency, it’s essential to adopt a phased approach in which Industry 4.0 solutions are implemented to improve specific sub-processes. On the other hand, such implementations often fail to materialize business goals due to a lack of executive oversight and a skills gap. Manufacturing companies can overcome these challenges by collaborating with consulting partners whose proven expertise in Industry 4.0 systems helps drive successful implementations.