Chapter 7. Industrial AI Use Cases
The manufacturing industry, especially discrete manufacturing industries like automotive and electronics, as well as process and batch manufacturing sectors, are implementing AI solutions along with the surge of wider adoption of Generative AI during the last two years.
In discrete manufacturing, AI has been commonly used for predictive maintenance of production equipment, quality inspection of finished goods, supply chain optimisation, and robot-enabled automation. Computer vision combined with deep learning has proven effective for accurately detecting real-time product defects on assembly lines. This allows issues to be addressed immediately rather than post-production. AI also helps schedule preventive maintenance by analysing machine sensor data to predict failures before they occur.
AI is streamlining batch production processes in process industries like chemicals, energy, and food & beverage. Using Machine Learning (ML) algorithms to analyse plant operations data enables more precise production scheduling and quality control. It also helps optimise yield by determining the best conditions and material combinations. AI monitors plant-wide data and parameters within refineries and power plants to identify reliability gaps and efficiency issues, reducing downtime and waste.
Adoption of AI across manufacturing has accelerated in recent years due to decreasing technology costs, mature use cases demonstrating measurable ROI, and pressure to keep up with competitors integrating these tools. Deloitte found that two-thirds of manufacturers have implemented or piloted at least one AI solution as of 2021. However, most implementations have focused more on routine tasks than fully autonomous systems.
While successful AI projects show benefits like 5-10% increases in productivity, full adoption faces challenges. The lack of curated data for training models limited skilled talent familiar with manufacturing and AI, and concerns about automation replacing jobs are causing some manufacturers to hesitate. Integrating legacy systems with new AI/ML technologies can also be difficult due to format inconsistencies or employees' reluctance to embrace change.
Standardisation of reference architectures, common frameworks for evaluating ROI, and centralised resources for skill/knowledge sharing could help manufacturing scale AI more rapidly. Demonstrating how these technologies augment existing roles rather than eliminating them will also be important to address fears about workforce impacts. As more comprehensive manufacturing data becomes available and algorithms advance, AI can transform operations across industry segments through enhanced decision-making, improved quality, and optimised processes.
1. Traditional Enhancement Methodologies?
Before the advent of Industry 4.0 technologies, manufacturers relied on conventional methods focused predominantly on human and process optimisation to enhance output from asset-intensive operations. Through incremental changes, substantial gains were achieved over time.
Lean Production Techniques
·???????? Just-in-Time Production
·???????? Continuous Flow Manufacturing
·???????? Motion Studies and Ergonomics
·???????? Visual Management Systems
Standardisation and Work Design
· Standardising Equipment Layouts
·???????? Standard Work Documentation
·???????? Skill-Set Communication
·???????? Ergonomic Workstation Design
·???????? Time Motion Studies
Continuous Improvement Culture
·???????? Kaizen and Employee Involvement
·???????? Periodic Huddles and Plant Trials
·???????? Cross-Functional Multi-Skilling
·???????? Lean Coaching Cells
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2. AI As The Game Changer
While manufacturing companies have relied on philosophies like Lean Manufacturing, Total Quality Management (TQM) and Kaizen to drive productivity through optimising processes, eliminating waste and engaging employees in continuous improvement, these traditional approaches yield huge benefits but in the meantime hit limits due to their reliance on human efforts alone.
On factory floors, vision systems incorporating deep learning allow machines to "see" with superhuman precision, accuracy, and speed. Defect detection, real-time yield monitoring, and automated equipment inspections that previously required human eyes now run autonomously 24/7, slashing inspection times by over 90%. This liberates employees for higher-value activities while preventing quality issues.
Beyond perception, AI is optimising processes through reinforcement learning. And product design itself is evolving in AI's hands. Meanwhile, preventive maintenance takes a massive leap with machine learning. Another interesting finding is that intelligent virtual assistants are becoming manufacturing domain experts through natural language learning from enormous technical documentation.
While transforming productivity metrics, AI equally impacts the work culture. In a post-COVID era, manufacturing has entered a stage where intelligence woven into processes dramatically elevates outcomes beyond what traditional optimisation alone could. Early adopters credit AI with revenue boosts as high as 30% from amplified quality, throughput, uptime and design agility.
3. AI For Automotive Assembly Line (referring to the book for the details)
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4. AI Applications in Electronic Manufacturing Process (referring to the book for details)
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5. AI in Baby Formula Manufacturing
The production of infant formula milk powder is a complex process designed to closely mimic breast milk composition. Key raw materials like dairy, vegetable oils, and vitamins and minerals undergo extensive pre-treatment, precise blending, heat processing, homogeneous mixing, and moisture removal via evaporation. Strict quality controls aim to deliver babies a safe, nutritious, and easily digestible supplement.
·???????? Receiving and Storage
·???????? Pre-treatment
·???????? Blending:
·???????? Heating:
· Homogenisation:
·???????? Cooling and Packaging:
·???????? Warehousing:
The intensive processing and multi-level quality controls throughout manufacturing and storage aim to deliver baby formulas with optimal nutrition, stability, and safety that meet stringent international guidelines. Tight process regulation is key to maintaining customer trust in this crucial sector.
Traditional Quality Control methods and limitations in baby formula production
Maintaining consistent quality is paramount in baby formula manufacturing due to stringent regulations and implications on infant health and development. Traditional quality control methods focus on various stages of testing and inspection.
Raw material receiving involves sampling milk, oils and other ingredients for microbial and compositional analysis in the quality control laboratory. Temperature and pressure sensors monitor key process parameters during production to keep them within validated limits. Staff take milk samples after pasteurisation and blending to test protein, fat and solids content. Further sampling is done after heat treatment and powder packaging to check analytic properties.
Manual organoleptic examinations also inspect freshly powdered products for physical defects like clumps or foreign objects. Post-production quality checks involve sampling finished goods at warehouse dispatch. Samples are retained for shelf-life studies to ensure the powder maintains nutritional integrity over its expiration period. Additional testing may occur if issues emerge from market complaints.
While traditional methods aim to catch quality deviations, they come with limitations. Taking samples interrupts workflow, and lab testing can take hours to days to provide results. This only permits quality oversight on a small fraction of the total production volume.
Application of AI and IIoT for enhanced quality control in infant formula production
We'll imagine this technology being implemented at XYZ Baby Formula, a large manufacturer in North America. XYZ uses traditional quality control methods like periodic manual inspections and laboratory testing. However, it wants to improve oversight and maximise efficiency.
The plant houses three production lines. Engineers equip the first line with an array of IoT sensors throughout—thermocouples in steam injectors, pressure transmitters on homogenisers, and flow meters on pasteurisers. Spectrometers and cameras are also integrated at key points.
Sensor data is streamed via OPC-UA to an edge gateway containing an AI/ML model trained on historical manufacturing datasets. The algorithms focus on detecting anomalies, clustering normal patterns, and predicting nutrient levels from spectroscopy.
As the line runs, the model monitors over 100 variables, such as temperature and flow rates. It identifies a steam injector running at ten °C hotter than normal. An alert notifies operators to inspect for a leak and make repairs, avoiding potential quality issues.
Computer vision detects a cracked pipe, allowing the whey to leak into powder. Automatic defect alerts enhance safety versus 1% manual checks. Near-IR spectroscopy and nutrient prediction models flag an off-spec batch before packaging.
By enabling full-time process surveillance, the plant catches problems earlier and improves traceability for quick resolution. They plan to deploy these technologies across additional lines to strengthen production quality assurance.
Chapter 7: Industrial AI Use Cases
Chapter 8: Industry AI – Trends and Forecast
Chapter 9: Drive Industrial AI Strategies
Chapter 10: Implementing Industrial AI
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