Covering Disruptive Technology Powering Business in The Digital Age

Written By: Patrick Yeoh, Director of Information Technology, Global Business Services (GBS), Jabil

Companies are racing to embrace digital technologies like Artificial Intelligence (AI). These technologies are critical enablers of Industry 4.0 and will empower the manufacturing market to continue to be the backbone of the global economy. Artificial Intelligence in manufacturing is bringing factories into the future.

Industry-wide, manufacturers are facing various challenges that make it difficult to speed production while still providing high-value and high-quality products to their customers. Companies need to implement a digital infrastructure that positions them to fully embrace the skills and knowledge of their best assets—people.

The manufacturing industry today relies on automation just as much as people. But the factory of the future, which is a marriage of physical and digital capabilities, requires more real-time data, connectivity, and AI technology at the forefront.

Transforming the Manufacturing Sector in Malaysia

With aims of creating a stronger, more advanced manufacturing sector in Malaysia, the Malaysian government has unveiled the New Industrial Master Plan (NIMP) 2030 to increase the manufacturing sector’s value-add by 6.5 percent to RM587.5 billion by 2030. To this end, the priority sectors identified are electronics, chemicals, aerospace, electric vehicles, and pharmaceuticals.

The Master Plan will help Malaysia revolutionise its industries and ultimately transform itself into a high-value country that produces high-wage jobs.

Next-generation innovations such as AI can help Malaysia’s manufacturing sector move up the value chain.

Here are some ways AI in manufacturing can help:

Refine Product Inspection and Quality Control

A typical manufacturing environment includes automated optical inspection (AOI) machines to identify which products meet standards and which are defective, but these machines have an accuracy rate of about 60–70 percent. In a school setting, this may be a passable grade, but it is not stellar as high quality is one of the predominant goals in the manufacturing sector.

It significantly improves process optimisation when AI is augmented in manufacturing processes like AOIs and is taught to recognise patterns.

High-resolution cameras with AI-based recognition software can perform quality checks at any point of the production process and accurately identify points where a product becomes defective. Is it because the machine is not functioning well? Or is it some other factor affecting the product’s quality? When we can answer these questions, the manufacturing processes become faster and more effective and produce higher quality products. This can be extremely beneficial for closely supervised industries like automotive and aerospace, which must meet stringent quality standards set by regulatory agencies.

“The manufacturing industry today relies on automation just as much as people. But the factory of the future, which is a marriage of physical and digital capabilities, requires more real-time data, connectivity, and AI technology at the forefront.”

Augment Human Capabilities

The ultimate goal of AI is to make processes more effective—not by replacing people, but by filling in the gaps in people’s skills. By working side-by-side, the collaboration between people and industrial robots can result in higher-quality products, reduce human errors, and allow people to focus on higher value and more strategic work processes.

Enable Preventative Maintenance

Predictive maintenance analyses the historical performance data of machines to forecast when one is likely to fail, limit the time it is out of service, and identify the root cause of the problem. Yield-energy-throughput (YET) analytics can ensure that those individual machines are as efficient as possible when operating, helping increase their yields and throughput and reduce the energy consumed.

The ability of AI to process massive amounts of data, including audio and video, enables it to identify anomalies to prevent breakdowns quickly—whether an odd sound in an aircraft engine or a malfunction on an assembly line detected by a sensor.

With machine failure, production stops. Meanwhile, predictive maintenance typically reduces machine downtime by 30–50 percent and increases machine life by 20–40 percent. With manufacturing’s increasing reliance on machinery and the need to boost uptime and productivity, companies require much more than good luck and happy thoughts to keep production humming.

How to Successfully Implement AI in Manufacturing

The big challenge with AI implementation, which exists beyond manufacturing, is the abundance of data. Organisations either do not have enough data or they have so much that it becomes overwhelming and not actionable. In many manufacturing environments, most can still not extract certain data from machinery. Therefore, the AI is unable to highlight patterns and outliers.

The governing principle in driving Industry 4.0 or smart factory initiatives is: “If we can digitalise it, then we can visualise it.” After we can visualise it, we can optimise it.

There is abundant data generated in the manufacturing process and we must aggregate, prioritise, catalogue and use the data to solve business problems. The definition of data and how we govern data is absolutely important. Data must be consistent, reusable, transparent, trustworthy, and open.

Strategising about Data Is Imperative

We must also have a strategy for storing and using data from both physical and logical perspectives.

Data scientists are key to successfully incorporating AI into any manufacturing operation. They are needed to help companies process and organise the big data, turn it into actionable insight and write the AI algorithm to perform the necessary tasks.

“Industry-wide, manufacturers are facing various challenges that make it difficult to speed production while still providing high-value and high-quality products to their customers. Companies need to implement a digital infrastructure that positions them to fully embrace the skills and knowledge of their best assets—people.”

But the data scientists themselves cannot do all the work. Involvement from the business owners who understand the processes involved in manufacturing and production is also crucial as they are familiar with how each parameter and factor affected will influence the outcome of the AI algorithm.

Rolling out successful AI projects takes time. Think about AI as a brain; you need to train it. You probably need to have a process for the machine learning algorithm. We need the process owner and the management’s sponsorship to know this takes time. Immediate effects are not likely; it is a process.

Still, imagination is never-ending and AI capabilities will be too. Think about our brains; they contain unlimited power.

The AI evolution will be the same for manufacturing organisations. Productivity and efficiency will be rocketed to new heights, processes will be smoother, and the possibilities will be endless.

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