Industry 4.0: Taking Manufacturing to the Next Level

The world’s first “digital-first” factory — an electric vehicle plant in Debrecen, Hungary — will open in 2025. What sets it apart from other factories? Designers are using digital twins to create real-time simulations for setting up and fine-tuning the facility’s robotics, logistics systems, layout, and other vital functions.

Artificial intelligence (AI) makes this project possible thanks to innovative, intelligent products and technologies. Manufacturers worldwide are driving the strategic pursuit of digitalization, and while the Hungary plant is the first, it won’t be the last. We’re seeing more possibilities of how these advances enable new and improve current use cases throughout the manufacturing lifecycle from conceptualization and design to engineering and fabrication to testing and assembly. In short, the sky’s the limit.

Industry 4.0

Digital-first factories are leading the wave of new development in industrial AI. Before 2020, industry 4.0 technologies were influencing manufacturing operations. The concept refers to four foundational technologies used across the value chain.

  • Connectivity, computational power, and data — including sensors, the Internet of Things, cloud technology, and blockchain
  • Analytics and intelligence — including advanced analytics, machine learning (ML), and AI
  • Human-machine interaction — including virtual and augmented reality (VR/AR), robotics, and automation like collaborative robots, autonomous guided vehicles (AGV), robotic process automation (RPA) and chatbots
  • Advanced engineering — including additive manufacturing like 3D printing, renewable energy, and nanoparticles

Today’s modern manufacturing facilities can use Industry 4.0 to streamline and automate operations and much more. For example, a factory can use advanced predictive analytics for demand planning, real-time planning and scheduling. Order management departments benefit from using real-time track-and-trace on customer orders while also optimizing order planning and routing to meet goals.  A planning and control department gains end-to-end visibility with AI and ML, which also facilitates automated root-cause analysis and digitally enables:

  • Cross-functional, integrated business planning. 
  • Robust scenario planning.
  • More accurate decision-making based on strategic cost-benefit, risk-modeling, and revenue.
  • Targeted deployment of RPA.

By investing in and harnessing intelligent technology and infrastructure, manufacturers equip themselves to more successfully weather turbulent economic times and ongoing labor shortages and supply chain issues. Deloitte’s 2023 Manufacturing Outlook survey of 700 global companies found that those manufacturers are focusing on many diverse technologies as they increase operational efficiency in the next year — and beyond — including:

  • Robotics and automation (62%)
  • Data analytics (60%)
  • Internet of Things platform (39%)
  • Additive manufacturing (33%)
  • Cloud computing (32%)
  • AI, ML and cognitive computing (26%)
  • Advanced materials (23%)
  • Digital twin (16%)
  • 5G connectivity (15%)
  • AR/VR (12%)
  • High-performance computing (11%)
  • Edge computing (9%) 
  • Quantum technology (5%)
  • Blockchain (4%)

Meanwhile, the International Data Corporation (IDC) forecasts that spending from manufacturers will account for over 16% of 2023’s $156 billion in global AI sales. Manufacturers are investing in technology designed to increase efficiency, elevate future competitiveness, and keep pace with evolving market trends and customer requirements. 

AI has the power to help manufacturers in three key ways. It offers higher intelligence and data analysis for increasing manufacturing precision, throughput, and yields — at a lower cost. It delivers better agility by facilitating quicker product design and prototyping, improved performance analysis, and increases the supply chain’s flexibility and resiliency. Finally, AI-driven tools improve sustainability to shrink energy costs and environmental impact.

We’ve discussed the importance of complying with environmental, social and governance (ESG) requirements in other sectors, and it’s becoming equally important in manufacturing. Smart factories and manufacturers require and use fewer material resources and less power. Technology helps them reduce emissions, consumption, and waste — and even increases materials recycling, plots efficient delivery routes, and optimizes logistics.  

Current and Planned Use Cases

Here are some current and planned use cases for implementing AI.

Predictive maintenance

AI-driven predictive maintenance catches and prevents minor issues from escalating into bigger, more expensive ones. Manufacturers can use GPU-accelerated computing to analyze significant amounts of operational and sensor data faster, more accurately, and in real-time. Plus, because it’s driven by AI, this proactive maintenance reduces false negatives and positives. And engineers can use the data for pinpointing and mitigating potential problems’ root causes to prevent future quality issues.

Inspections and quality assurance

Many companies have prioritized QI and QA for AI applications, especially since defects can cost manufacturers almost 20% of their overall sales revenue. Defective items can increase product recalls and drive up warranty costs. Frequent issues eventually erode brand image to the point where its reputation cannot recover.

Today’s automated optical inspection (AOI) machines also require more direct human involvement. Future AI-based computer vision applications will employ AI and ML more effectively to catch defects, including bad joints, cracks, misassembly, paint flaws, and even foreign bodies like hair or dust. There’s even a new approach using synthetic data to train a defect detection model for catching manufacturing imperfections.

Supply chain efficiency and resilience

The pandemic threw into stark relief the incredible fragility of the supply chain — and the challenge many companies experienced in pivoting and adapting to unexpected production and distribution issues.

Deloitte’s recent survey also found that 72% of manufacturers identified parts shortages and supply chain disruptions as their most significant concerns for 2023. Shipping lead times remain long — sometimes twice as long as is typical. To help mitigate these issues and boost supply chain resiliency, 90% of supply chain professionals are investing in the cloud. They’re using AI/ML and data analytics to:

  • More effectively forecast inventory and demand levels.
  • Optimize transportation routes and logistics.
  • Coordinate distributors and suppliers.

The manufacturing industry stands to reap significant benefits from implementing AI: higher quality, increased efficiency, more robust supply chains, accelerated time-to-value, and faster innovation. Organizations embracing Industry 4.0 will position themselves strongly for future success.

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