How are top industries using AI in manufacturing?
From automated factories and AI-powered robots to smart buildings, AI monitoring, and quality control, digital transformation is the cornerstone of survival for businesses.
The primary objective? Forging a competitive edge through technology, resulting in enhanced customer experiences and reduced operational costs.
An industry at the forefront of digital transformation is the manufacturing industry, leveraging technologies like big data analytics, AI, AR, VR, IoT, predictive analytics, automation, and robotics. The results are tangible:
Machine downtime reduced by 30-50%,
Labor productivity increased by 15-30%,
Throughput improved by 10-30%,
Quality-related costs reduced by 10-20%.
Here are five industries using AI in manufacturing:
Automotive Industry
Automotive manufacturing requires precision and accuracy, and AI can help enhance that.
For example, Ford employs cobots for welding, gluing, and quality control tasks. It uses six cobots to sand the entire body surface of a car in 35 seconds. Similarly, BMW's Spartanburg plant, producing 60% of U.S. BMWs, uses AI-managed robots, saving $1 million yearly and reallocating workers.
Mercedes-Benz partners with NVIDIA Omniverse, deploying digital twins to enhance efficiency and save time in the MO360 production system. It also helps cut coordination processes with suppliers by 50%.
The automotive AI market is projected to hit $7 billion by 2027, highlighting it as one of the leading industries in adopting AI in manufacturing.
Electronics Industry
Electronic manufacturing also requires precision due to its intricate components, and AI can be critical in minimizing production errors, improving product design, and accelerating time-to-market.
For instance, Samsung's South Korea plant uses automated vehicles (AGVs), robots, and mechanical arms for tasks like assembly, material transport, and quality checks for phones like Galaxy S23 and Z Flip 5. These tools can help companies maintain high-quality standards, including inspections of 30,000 to 50,000 components.
Nvidia is using AI to optimize the placement of intricate transistor configurations on silicon substrates, which not only saves time but offers greater control over price and speed. It proved its efficiency by optimizing a design featuring 2.7 million cells and 320 macros in just three hours.
With a vast market and continued AI innovation, enhanced use of AI involvement is becoming table stakes for companies manufacturing electronics.
Aerospace And Defense Industry
AI-driven manufacturing enhances product safety and reliability by producing precise components, boosting performance and system safety. The AI in aviation market was worth $686.4 million in 2022 and is expected to grow at a CAGR of over 20%.
Airbus, with Neural Concept's tech, cut aircraft aerodynamics prediction time from one hour to 30 milliseconds using ML. This kind of productivity boost can enable design teams to explore 10,000 more changes in the same time frame as the traditional computer-aided engineering approach.
Likewise, Rolls-Royce, in collaboration with IFS, uses AI in aerospace manufacturing through the Blue Data Thread strategy. This approach utilizes digital twins and AI for predictive maintenance, resulting in a 48% increase in time before the first engine removal.
Food And Beverage Industry
Food and beverage production requires advanced quality assurance, particularly in the fast-moving consumer goods (FMCG) sector, due to its “high-speed” nature. Equipment breakdowns and faulty products can hinder that; however, integrating AI can boost efficiency, cost-effectiveness, and product quality and safety.
The global AI market for the food and beverage industry is set to reach $35.42 billion by 2028.
Startups specializing in predictive maintenance technology are particularly in demand. Take Augury Inc., for instance. They helped PepsiCo’s Frito-Lay gain 4,000 hours of manufacturing capacity annually through its predictive maintenance systems that decreased unplanned downtime and costs at four Frito-Lay plants.
Frito-Lay employs an ML model and vision system to predict processed potato weights, saving the company from a $300,000 investment per production line (with 35 lines in the U.S. alone). They also use AI with lasers to assess chip texture through its crunch sound, enhancing quality control.
Nestlé streamlines its operations through AI, using predictive maintenance automation via machine sensors to optimize troubleshooting. Currently, it is integrating AI in 500 projects and 45 use cases in multiple facilities globally.
Pharmaceutical Industry
It usually takes a decade to develop a drug, plus two more years for it to reach the market. Unfortunately, 90% of drugs fail in the clinical trial phases, resetting the clock. AI can accelerate drug development and enhance quality control.
Pfizer, for instance, using IBM's supercomputing and AI, designed the Covid-19 drug Paxlovid in four months, reducing computational time by 80% to 90%.
Here are three of the areas where AI can alleviate drug-discovery challenges:
1. Protein Structure Prediction: AI systems like AlphaFold2 have transformed protein structure prediction, enabling researchers to understand the blueprints of complex molecules accurately and potentially saving years of lab work.
2. Function Forecasting: A recent Forbes article explored how AI models can predict how large molecules function and understand how proteins bind to their targets and the movement of antibodies, enhancing the development of therapeutic responses.
3. New Therapies Design: AI algorithms use vast data to design proteins, antibodies, and mRNA structures for diseases like cancer. Genesis Therapeutics, for example, uses AI to design and predict the effectiveness, specificity, and potential side effects of new medication.
AI in drug development could generate 50 new drugs, resulting in $50 billion in sales over a decade. Over 80 companies are advancing AI-driven drug development, attracting investments from pharmaceutical giants.
The Way Forward
A recent survey conducted by Augury of 500 firms reveals that 63% plan to boost AI spending in manufacturing. This aligns with AI in manufacturing market projection, which is estimated to reach $20.8 billion by 2028, according to MarketsandMarkets.
The efficiency gains from AI integration translate into cost and time savings, allowing resources to be redirected to more critical tasks and opportunities.
AI in manufacturing relies on three pillars: problem, people, and process. Here are five steps that can help ensure that you develop all three pillars:
1. Define the problem: Identify inaccuracies causing higher costs. New-to-AI companies should break down the issue into SMART goals and assess AI's potential for long-term cost savings.
2. Address resources and data: Form a diverse team of technical and business experts. Evaluate in-house capabilities and consider outsourcing or hiring. Verify data sufficiency, clean and structure the data, and decide on storage solutions.
3. Assess data quality: Is the data modern, accessible, and sufficient? Modify as needed.
4. AI model considerations: Decide to build, buy off the shelf, or adopt a hybrid approach.
5. Fine-tuning and deployment: Discuss model refinement, deployment, and scalability.
As a final note, be sure to adhere to ethical guidelines and frameworks throughout each step, as it is crucial to rigorously check for bias and develop guardrails against it.
