How Can Artificial Intelligence Transform Manufacturing Industry?

Artificial Intelligence has the potential to transform the manufacturing industry completely. Examples of possible upsides include increased productivity, decreased expenses, enhanced quality, and decreased downtime. Big factories are just some of the ones that can benefit from this technology. Many smaller businesses need to realize how easy it is to get their hands on high-value, low-cost AI solutions.

How Can Artificial Intelligence Be Used In Manufacturing?

With so much data being produced daily by industrial IoT and smart factories, artificial intelligence has several potential uses in manufacturing. Manufacturers are increasingly turning to artificial intelligence (AI) solutions like machine learning (ML) and deep learning neural networks to better analyze data and make decisions.

Predictive maintenance is often touted as an application of artificial intelligence in manufacturing. Artificial intelligence (AI) can be applied to production data to improve failure prediction and maintenance planning. This results in less costly maintenance for production lines.

Many more applications and benefits of AI in production are possible, including more accurate demand forecasting and less material waste. Artificial intelligence (AI) and manufacturing go hand in hand since humans and machines must collaborate closely in industrial manufacturing environments.

Opportunities Provided By Artificial Intelligence In Manufacturing

As a collective and sometimes rather omniscient term, Artificial Intelligence (AI) includes the capabilities of learning systems that are perceived as intelligent by humans. AI and Machine Learning (ML) technologies have become top priorities in manufacturing since they allow firms to alter business models, invent operational paradigms to support those models, and monetize information to achieve higher levels of productivity.

Beyond hypes and fads, AI works because it amasses significant benefits for the manufacturing sector, such as enabling smart production, developing predictive and preventative maintenance, offering supply chain optimization, improved safety, product development, and optimization, facilitating AR/VR (Augmented and Virtual Reality), cost reduction, quality assurance and enabling green operations (energy management), to name a few. AI enablement is most commonly used by manufacturing industries to increase overall equipment efficiency and yield. AI is also being utilized generally as a tool to improve productivity, quality, and consistency, which helps manufacturers forecast more accurately.

It’s safe to say that the manufacturing industry continues to be driven by AI and ML technologies. UST has observed the critical use of AI in transforming operations, improving product quality, and reducing costs through various methods including smart operation, design prediction, quality assessment of products, and more.

We see that scaling AI implementations beyond a proof-of-concept (POC) level remains one of the biggest challenges in manufacturing, as well as other industries including but not limited to logistics, healthcare, insurance, finance, and audit.

Taking advantage of just the technology isn’t enough – there is also a wider aspect of people and cultural change that needs to be addressed. Stakeholders and end-users must also be convinced of the insights regarding the reliability of the data generated with AI. For instance, even when people are aware that the inventory recommendations for raw materials or deliverables are accurate, they feel more comfortable holding a little extra stock or being a little protective in the supply chain. Therefore, incorporating human heuristics becomes a challenge.

Lastly, based on our experience, selecting the right sponsor for the project is another challenge. Manufacturing is a complex area in which the choice of the sponsor is vital to gaining the trust of the stakeholders, especially when it comes to the adoption of new Digital Transformation technologies.

Artificial Intelligence in Logistics

Production losses due to overstocking or understocking are persistent problems. Waste and decreased profits are typical results of overstocking. Businesses might gain sales, money, and patronage when products are appropriately stocked.

AI Autonomous Vehicles

Vehicles that drive themselves may automate the entire factory floor, from the assembly lines to the conveyor belts. Deliveries may be optimized, run around the clock, and completed more quickly with the help of self-driving trucks and ships.

To better plan delivery routes, decrease accidents, and notify authorities in an emergency, connected cars with sensors can track real-time information regarding traffic jams, road conditions, accidents, and more. This change enhances both the speed and safety of deliveries.

AI for Factory Automation

Operators in factories rely on their knowledge and intuition to manually modify equipment settings while keeping an eye on various indications on several screens. In addition to their regular duties, operators in this system are now responsible for troubleshooting and testing the system.

This leads some business owners to ignore or downplay the need to generate a financial return on investment, among other undesirable outcomes.

AI for IT operations

Intelligent automation in IT operations, or AIOps, is essential for this purpose. AIOps, as defined by Gartner, is an approach to IT operations automation that uses big data and machine learning.

AIOps is most helpful in automating extensive data management. Besides these, IT service management, event correlation and analysis, performance analysis, anomaly identification, and causation determination are all potential applications.

Francisco Betti, Head of Advanced Manufacturing and Production, and a Member of the Executive Committee, of the World Economic Forum, said:

“The complexity of current challenges impacting manufacturers calls for the need to go beyond the traditional means of driving productivity. Artificial intelligence can help companies unlock innovation, resilience, and sustainability. We look forward to working with the Network of Centres for the Fourth Industrial Revolution and the global manufacturing community to support its deployment at scale,”

Kay Firth-Butterfield, Head of Artificial Intelligence and Machine Learning at the World Economic Forum also said:

“This paper showcases the tremendous value potential of AI in manufacturing. Not only in terms of efficiency but also in terms of sustainability and worker engagement. The insights were generated thanks to a collaborative effort by the Centre for the Fourth Industrial Revolution affiliate in Türkiye, the Forum’s Platform for Shaping the Future of Advanced Manufacturing and Value Chains and the Platform for Shaping the Future of Technology Governance: Artificial Intelligence and Machine Learning,”

Over 20 use cases were collected from more than 35 senior executives and technology experts from more than 10 industries, including automotive, electronics, energy, textiles, cement, steel, food, and chemicals. These cases demonstrate how leading manufacturers have successfully captured value from AI applications in manufacturing and cover six main areas: health and safety, quality, maintenance, production process, supply chains, and energy management.