Digitalization in Manufacturing

Manufacturing companies are undergoing a digital transformation like never before. Ten years ago, automation was a competitive advantage, presently, digital transformation in manufacturing is essential for survival. Firms that fail to adopt digitalization in manufacturing are losing market share, customers and revenue. Digital transformation involves planning and implementation.

What is Digitalization

Before we start, let’s define the distinctions among three concepts:

Digitizing manufacturing processes mean changing analog information into a digital form. For example, converting paper drawings into CAD files or digitizing manufacturing processes documentation archives.

Manufacturing digital transformation means using digital solutions to optimize manufacturing processes. It’s not just digitizing data, but changing the way we work: implementing IoT sensors, manufacturing execution systems (MES) and digital solutions for manufacturing processes.

Digitalization in manufacturing means a change of the whole business model of a company based on new digital solutions. This applies not only to manufacturing processes but also to customer, supplier and supply chain interactions. Modern manufacturing processes rely more and more on digital transformation in manufacturing to improve manufacturing operations.

Factory digitalization includes several connected parts:

• Data Collection: Sensors and measuring tools collect information about temperature, vibration, machine speed, material use, and product quality. And it helps you know what is a fail in production.
  
• Data Transmission: Both industrial networks and protocols like OPC UA and MQTT support the digitalization of manufacturing processes by enabling the transfer of digital data from machines to process control systems and analytics systems, which are essential for supply chain management.
  
• Data and processing: cloud and local servers contain large amounts of data in terabytes. Big data analytics and artificial intelligence systems analyze this information, helping manufacturing companies to optimize manufacturing processes and plan predictive maintenance.
  
• Visualization and Management: Dashboards, mobile apps and MES systems allow operators and managers to monitor manufacturing operations, improve manufacturing processes and make decisions based on digital solutions. Effective digital transformation in manufacturing means operational efficiency across the factory floor.

Manufacturing Digitalization: Differences from the Traditional Approach

Aspect	Traditional manufacturing	Digitalization in manufacturing industry
Data collection	Manually, sometimes	Automatically, in real time
Decision making	Based on experience and intuition	Based on data analysis
Problem response	After problems happen	Predictive maintenance
Flexibility	Low, long setup time	High, quick adaptation
Transparency	Limited process visibility	Full transparency of the value chain
Integration	Separate systems	One digital environment

Key Technologies for Digitalization in Industry

IoT and Industrial Internet of Things (IIoT)

IoT in manufacturing is the foundation of smart manufacturing. Sensors and devices collect data on equipment status, manufacturing processes and product quality. This reduces manual processes and supports emerging technologies in the factory.

Practical use cases:

• Tracking materials and work-in-progress: Keeping track of materials and products during their production.
• Monitoring environmental conditions: Checking humidity, temperature, and air quality in many manufacturing companies.
• Monitoring equipment energy use: Watching how much energy machines use to lower manual work and maintenance costs.

Manufacturing Execution Systems

Manufacturing execution systems (MES) is the “digital backbone” of modern smart manufacturing operations. These systems connect the planning level (ERP) with the execution level (equipment and operators). MES functions in digital transformation:

• Real-time manufacturing processes management.
• Material tracking and product lifecycle management.
• Quality control and defect registration.
• Human resources and skills management.
• Digital data collection and analysis.
• Maintenance management.

Digital Twin

A digital twin is a virtual copy of a physical object, process or system. It’s one of the most powerful emerging technologies in digital manufacturing.

Advantages:

• Risk free testing: Changes to a process or equipment setup can be tested on a virtual model before implementing in real production.
• Process optimization: Simulating different scenarios helps find the best operating modes in smart manufacturing.
• Predictive maintenance: A digital twin predicts equipment wear and failure.
• Staff training: Operators can train on a virtual model without risking real equipment, reducing manual processes.

Artificial Intelligence and Machine Learning

Artificial intelligence and machine learning is transforming digital transformation in manufacturing, allowing systems to learn from data and optimize manufacturing processes.

Applications of AI in smart manufacturing:

• Quality Control: Computer vision detects defects faster and more accurate than humans.
• Predictive Analytics: ML algorithms predict equipment failures weeks before they occur by analyzing digital data.
• Production Optimization: AI selects the best manufacturing process parameters for maximum productivity.
• Planning and Logistics: Intelligent systems optimize production schedules and supply chain management.

Cloud Technologies and Edge Computing

Many manufacturing companies use digital solutions via cloud platforms for scalability and data availability. Edge Computing processes data close to the source, reducing latency and enabling smart manufacturing. Critical data is processed locally, while long-term analytics and storage is in the cloud.

Augmented and Virtual Reality (AR/VR)

Augmented reality and virtual reality are part of emerging technologies in manufacturing.

Practical use cases:

• Training: VR simulators for operator training without interrupting production.
• Maintenance: AR glasses show equipment step-by-step instructions and highlight necessary components.
• Remote support: Experts can see what a technician sees on-site and provide real-time assistance.
• Design: Engineers can enter a virtual model of a future plant and optimize manufacturing processes.

Stages of Digitalization in Manufacturing

Phase 1: Status Quo

Before starting digitalization in industry, you need to know where you are. Key questions to ask:

• What digital capabilities exist?
• How automated are the workflows?
• What technology infrastructure is in place?
• Equipment readiness for digital transformation initiatives.
• Staff digital literacy.

Industry 4.0 Maturity model:

Level	Features
0 — Computerization	Separate systems
1 — Connectivity	Local networks, basic data collection
2 — Visibility	Monitoring systems, information dashboards
3 — Transparency	Data analytics, digital twins
4 — Predictive Capacity	AI/ML, automatic optimization
5 — Adaptability	Self-organizing systems

Phase 2: Define Goals and Strategy

Set measurable KPIs for digital transformation strategy: cost reduction, quality improvement, time-to-market, flexibility, occupational safety, regulatory compliance.

Typical goals:

• Cost reduction (energy, materials, downtime).
• Quality improvement.
• Time-to-market.
• Flexibility.
• Occupational safety.
• Regulatory compliance (traceability).

Important: Set measurable KPIs for each goal. For example: “Reduce unplanned equipment downtime by 30% within 12 months” instead of “Improve equipment reliability”.

Phase 3: Pilot Projects

Start with small pilots in limited areas of the plants. This allows you to:

• Test technologies with minimal risk.
• Train the team on real-world tasks.
• Prove the approach to management.
• Identify problems before scaling up.

Phase 4: Scaling and Integration

After successful pilots, digitalization in manufacturing begins at all levels:

• Horizontal Integration: all manufacturing workflows connected.
• Vertical Integration: sensors to ERP.
• End-to-End Integration: suppliers and customers (supply chain optimization).

Critical Success Factors:

• Standardization: Use unified protocols and platforms.
• Change Management: Involve staff at all stages.
• Cybersecurity: Data protection should be built in, not added later.
• Partnerships: Work with proven solution providers.

Phase 5: Continuous Improvement

Digitalization of manufacturing is not a one-time project, it’s an ongoing process.

Key Practices:

• Monitor KPIs and adjust strategy.
• Update technologies and systems.
• Train staff on new tools.
• Benchmark and analyze best practices.
• Innovation: Test new technologies (AR, AI, blockchain).

Real-World Examples of Digitalized Manufacturing

Industry 1: Aircraft Manufacturing (Airbus)

Airbus has digitalized its entire factory.

Technologies:

• Digital Twin: Virtual aircraft models for design and manufacturing process testing.
• AR for Assembly: Operators use AR glasses to see where to install each of thousands of components.
• Automated Inspection: Drones with cameras inspect the fuselage, AI analyzes the images.

Industry 2: Automotive (Tesla)

Tesla is a fully digital, next-gen manufacturing process.

Tesla’s digital factory features:

• High automation: Robots do most of the work, from welding to painting.
• Real-time data: Each car is tracked at every stage of production.
• Product integration: Cars send data back to the factory to improve production.
• Flexibility: Ability to reconfigure production lines quickly.

Obstacles to Digitalization in Industry

Barrier type	Challenge	Solution
Technical	Old Machines	Add sensors, use gateways, replace old equipment step by step
	Different Systems	Use integration platforms, standard protocols, APIs
	Cybersecurity	Separate networks, update security, train staff, use detection systems
Organizational	Resistance to Change	Involve staff, show benefits, train and motivate
	Lack of Skills	Train employees, partner with schools, hire consultants, mix production + IT teams
	Low Budget	Start with small pilots, use subscriptions, get government or vendor support
Strategic	Vendor Lock-in	Use open standards, require APIs, use multiple vendors
	Fast Technology Changes	Choose modular systems, plan updates, follow digital transformation trends

The Future of Digitalization in Manufacturing

Trend 1: Autonomous Smart Manufacturing

The next level is fully autonomous “smart factories” that plan production, optimize processes and respond to demand changes.

Key Technologies:

• Advanced AI and reinforcement learning.
• Highly autonomous robots.
• Self-organizing manufacturing systems.
• Autonomous logistics (drones, UAVs).

Trend 2: Hyper-personalization of Mass Production

Digital enables production of unique products at mass production scale.

Examples:

• Furniture manufacturers offer millions of configurations in mass production.
• Pharmaceuticals are moving to personalized medicine.

Trend 3: Sustainable Manufacturing

Digitalization of manufacturing is key to sustainable manufacturing. Applications:

• Energy optimization through AI.
• Waste reduction through planning.
• Circular economy: product tracking for material reuse.
• Carbon footprint tracking throughout production.

Trend 4: 5G and Ultra-Low Latency Networks

5G will provide sub-millisecond communication for:

• Remote control of robots.
• Real-time AR.
• Synchronization between factories.
• Mobile-first in manufacturing.

Trend 5: Quantum computing

Not yet mainstream but being tested for:

• Optimization of complex production chains.
• Molecular-scale material modeling.
• Encryption for production data.

FAQ