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Manufacturing Digital Transformation: Top Trends & Technologies

Manufacturing Digital Transformation: Top Trends & TechnologiesManufacturing Digital Transformation: Top Trends & Technologies

After the COVID-19 pandemic, digital adoption is accelerating in almost every industry and manufacturing is no exception. According to the 2021 Digital Transformation Assessment study conducted by IBM and The Manufacturer, 67% of manufacturers have accelerated digital projects after COVID-19. We explore how digital technologies affect manufacturing with key technologies and trends.

What does digital transformation mean for manufacturing?

Digital transformation in manufacturing involves integrating digital technologies into processes and products to increase manufacturing efficiency and quality.

Digital transformation in manufacturing focuses on:

  • Improving operational efficiency and reducing costs
  • Ensuring the quality of manufactured products
  • Responding faster to the changing market requirements and customer demands

Why is digital transformation in manufacturing important now?

Customer expectations and increased competition are the main drivers of digital transformation in manufacturing as with other industries. By leveraging digital technologies, manufacturers can improve speed and efficiency, increase production, decrease costs, and provide a better customer experience.

COVID-19 has also demonstrated the importance of embracing digital technologies in manufacturing. Throughout the pandemic, manufacturers faced operational challenges that illuminated the weaknesses in their existing businesses. For instance, having real-time data about the supply chain could enable manufacturers to respond faster to supply shortages and demand spikes during the pandemic. The image below from IBM and The Manufacturer’s study shows the strategies manufacturers focused on during the pandemic and all could benefit from digital technologies.

Priorities of manufacturers during the pandemic.
Source: IBM

Artificial intelligence, machine learning, and advanced analytical techniques are involved in most of the digital transformation applications in manufacturing. Specific technologies and trends include:

Hardware & software

Autonomous systems

Autonomous systems are systems that leverage data about the surrounding environment from sensors (e.g. light sensor, audio sensor, cameras, infrared, radar, ultrasound, etc.) in order to analyze real-time situations, adapt, and react without human intervention. Some autonomous systems in manufacturing include:

  • Autonomous robots
  • Autonomous warehouse and factory systems
  • Autonomous vehicles
  • Drones


Manufacturing robots automate repetitive tasks, reduce the margin of error, and free up human workers’ time for more productive tasks. AI-enabled robots can train themselves to improve their performance. These robots can be fully autonomous or automate some tasks while working safely alongside human workers. The second type of industrial robots are called collaborative robots (cobots).


Industrial Internet of Things (IIoT) is a sub-category of the Internet of Things (IoT) that focuses on applications in industrial sectors. IIoT technology is transforming the manufacturing sector by enabling businesses to monitor the production process in real-time and helping them to make more data-driven decisions with manufacturing analytics. McKinsey predicts that IIoT will be a $500 billion market by 2025. Benefits of IIoT technologies include:

Expected IIoT spending between 2020-25.
Source: McKinsey

3D Printing

3D printing technology, also called additive manufacturing, helps businesses to produce complex objects cheaper and faster. Product prototyping is one of the most common use cases of 3D printing in manufacturing, but applications are not limited to it.

Traditional manufacturing is cost-effective in the case of mass production but 3D printing is ideal if a product is not going to be produced in large volumes. 3D printing also offers a cost-effective way to produce customized products or product parts for consumer goods. 3D printing can use a variety of materials including metal.

Augmented and virtual reality

According to PwC, augmented reality and virtual reality (AR/VR) technologies have the potential to add $1.5 trillion to the global economy by 2030. Applications of AR/VR in manufacturing include:

  • Fast prototyping by enabling manufacturers to see how a product would look without physically creating it.
  • Inventory management involves processes that cannot be automated 100%. AR/VR-enabled devices can instruct human workers in a warehouse and provide information about items on shelves.
  • Maintenance: Different machines require different maintenance processes and having maintenance instructions accessible in your glasses makes maintenance significantly easier.

For all AR use cases, feel free to check our comprehensive article on AR use cases.


RPA and Intelligent Automation

Robotic process automation (RPA) and intelligent automation in manufacturing can decrease the need for human interference in rule-based and repetitive tasks and reduce process errors. The processes that can benefit from RPA tools include:

  • Supply chain optimization
  • Invoice processing
  • Inventory management
  • Manufacturing data management
  • Order fulfillment

Digital Twins

A digital twin is a digital representation of physical entities such as products, devices, or systems that enable businesses to make model-driven decisions. Digital twins have a wide range of applications in manufacturing including:

  • Product development
  • Design customization
  • Shop floor performance improvement
  • Predictive maintenance
  • Logistics optimization

Another application of digital twins is digital twin of an organization (DTO) which is the virtual representation of the entire business instead of a specific product. DTOs enables businesses to infer insights about:

  • Previous performance
  • Business goals
  • Business models
  • Business processes
  • Performance KPI indicators with target levels
  • Detailed transaction-level situational process analysis

Manufacturing analytics

Manufacturing analytics provide insights about machines, processes, and operators, in order to predict machine future use and maintenance requirements, prevent failures and pitfalls, forecast business and inventory requirements, and identify areas for improvement. Leveraging analytics in manufacturing can be used to optimize:

  • Demand forecasting
  • Inventory management
  • Order management
  • Maintenance (predictive and preventive)
  • Risk management
  • Automation and robotics
  • Transportation allocation
  • Product progress measurement
  • End user experience estimation
  • Price optimization

To read use cases in detail, feel free to read our ultimate guide to top 10 Manufacturing analytics use cases.

What are some case studies?

  • Cisco developed a secure virtual MES (manufacturing execution system) platform to better orchestrate its global network of outsourced production plants. The platform involves IoT devices and big data analytics and helps the company trace supply chain quality in real time.
  • With the help of IoT devices and cloud technology, food packaging company TetraPak can predict exactly when equipment needs maintenance and avert breakdowns that could cause spoilage of food products.
  • Porsche has introduced customizable seats for its sports cars by using 3D printing technology and lets its customers select three levels of firmness: soft, medium, hard. The company is planning to expand the extent of customization for their cars in the future.

For more on digital transformation, you can check our other articles:

You can also check our sortable/filterable list of digital transformation consulting companies.

If you have more questions about digital transformation, let us know:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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