How is AI being Used in Manufacturing?

27 Jan 2025

(Source: ibm.com)

How is AI being Used in Manufacturing?
By Samuel Nata Charis

AI is fundamentally changing manufacturing by making production more efficient, accurate, and flexible, especially as part of the Industry 4.0 movement. Key AI use cases for AI in manufacturing include:

  • Digital Twin Technology

Manufacturers use AI to build digital copies of their operations, from individual machines to entire supply networks. These “digital twins” allow them to test, analyze, and predict performance instantly. By creating a virtual reflection of their physical assets, companies can monitor and improve operations remotely. These digital twins are powered by data collected from IoT sensors, PLCs, and advanced AI techniques, ensuring the virtual model remains constantly updated and accurate.

  • Cobots

Cobots are robots created to work safely with people, boosting both productivity and safety by taking on routine or strenuous jobs. For instance, in electronics assembly, cobots precisely place components, leading to faster and more accurate production. These robots represent a step forward in automation, combining the strengths of human workers and machines.

  • Predictive Maintenance

AI helps predict machine breakdowns by analyzing sensor data and using digital twins to monitor equipment behavior. This allows operators to address potential problems before they cause failures. For instance, car manufacturers use this predictive maintenance on assembly-line robots, which reduces unexpected downtime and saves money. This also enables companies to schedule maintenance during slow periods, minimizing production interruptions.

  • Custom Manufacturing

AI allows manufacturers to produce personalized items on a large scale, meeting individual customer needs without slowing down production. By using AI in design, companies can quickly change products based on immediate customer feedback. For example, clothing companies use AI to allow customers to design clothes that fit their personal style. This approach improves customer satisfaction and involvement.

  • Generative Design

AI-powered generative design lets manufacturers explore many design possibilities, considering factors like materials and production limits. This speeds up product creation by enabling quick evaluation of numerous design versions. Industries like aerospace and automotive are already using these tools to create highly optimized parts. While the technology is established, its full capabilities are still being discovered as manufacturing evolves.

  • Factory in a Box

The “factory in a box” idea involves using portable, self-sufficient manufacturing units that can be set up quickly anywhere. These units, powered by AI automation, IoT sensors, and real-time data analysis, allow for adaptable, local production. This lets companies produce goods closer to where they’re needed, cut shipping costs, and adapt quickly to changing demands. Industries like electronics, cars, and pharmaceuticals are already testing these portable factories. This concept will become more common as automation, modular design, and data integration improve.

  • Quality Control

AI improves quality control through real-time defect detection using computer vision and machine learning. This technology, sometimes aided by digital twins, analyzes product images, surpassing human inspectors. For example, electronics companies use AI to ensure component quality, leading to less waste and better customer satisfaction.

  • Supply Chain Management

AI optimizes supply chains by analyzing data to predict demand and manage inventory. Digital twins help simulate disruptions. Machine learning automates procurement and forecasts demand. AI-driven order systems ensure timely delivery. Food manufacturers use AI to anticipate seasonal demand, reducing waste and improving efficiency.

  • Inventory Management

AI optimizes inventory by predicting stock needs and automating replenishment. Real-time monitoring and demand forecasting help maintain ideal levels, reducing costs. Food and beverage companies use AI to track ingredient usage and forecast future needs, preventing delays and reducing waste.

  • Energy Management

AI monitors energy use to find inefficiencies, suggesting changes that reduce costs and environmental impact. Electronics companies use AI to optimize energy, saving money and reducing their carbon footprint.

  • Workforce Management

AI optimizes workforce planning by analyzing employee data to improve scheduling and productivity. It considers workload and skills to create efficient schedules. Manufacturers use this to allocate skilled workers effectively.

  • Product and Spare Parts Search

Generative AI helps customers find products by understanding descriptions, even without specific names. It turns these descriptions into search queries and creates detailed product descriptions for better search results.

  • Document Search and Summarization

Generative AI streamlines document handling in manufacturing by automating search and summarization. It analyzes documents to find patterns and summarizes key information, making complex data quickly accessible.

  • Manufacturing-Adjacent Areas

Generative AI supports manufacturing processes in areas like ticket and call handling, market research, and the creation of product descriptions and maintenance documents.

Adoption of AI in Manufacturing: Challenges and Concerns
Despite the benefits, some companies still have concerns about implementing AI in manufacturing processes, for example:

1. Shortages of Skilled Labor
Successfully using AI in manufacturing requires a workforce with specialized skills. However, AI can also play a role in addressing this skills gap. AI can streamline talent acquisition by identifying candidates with the necessary skills. Moreover, AI-powered HR tools can help existing employees develop new skills through personalized learning programs.

2. Safety, Security, and Responsible use of AI
Like other cutting-edge manufacturing technologies, AI requires clear regulations and safeguards, particularly due to its handling of potentially sensitive information. To address this, two key actions are necessary.

First, manufacturing companies should commit to ethical and responsible AI practices and choose software providers who share those values. Second, to safeguard business and customer data, it’s crucial to partner with AI solution providers dedicated to ethical, transparent, compliant, and secure data handling, especially considering the cybersecurity risks, sabotage, and intellectual property theft faced by manufacturers.

Here are some green flags to look for when selecting a security-minded provider:

  • AI provider doesn’t share your data with third parties for the purpose of training their AI models
  • AI solutions are developed responsibly and with rigorous standards
  • AI provider employs advanced data security measures to protect your data at all times
  • AI provider is committed to transparency and explainability

3. Large-Scale Business Transformation for Complex Enterprise Architecture
Modern manufacturing relies on extensive IT systems, and many companies have complex, outdated setups due to mergers and acquisitions. Implementing AI across such complicated systems can be daunting. However, manufacturers can overcome this challenge by partnering with software providers to develop a streamlined core strategy and an AI-compatible IT infrastructure.

References: