Paul Calver, president and chief financial officer of the Data Analysis Bureau, examines how artificial intelligence (AI) and machine learning (ML) are improving manufacturing operations at every level.

Manufacturers are redefining their priorities around resilience, sustainability and operational excellence, and addressing the immediate challenges of securing supplies and recruiting workers.



Paul Calver, President and CFO of the Data Analysis Bureau

Technologies based on artificial intelligence and machine learning play a vital role in accelerating the capabilities of manufacturers in these areas and achieving their goals of capturing market share and maintaining global competitiveness.

What is AI and ML? Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs, and machine learning is a branch of AI that allows a machine to learn automatically, using data and algorithms, without explicit programming, and can gradually improve its accuracy over time. .

Manufacturers have been adopting physical technologies, such as robotics and automated machine tools, for many decades and have recently begun to focus on additive manufacturing.

But as manufacturers become more aware of the power of data, many are turning to advanced data analytics, AI and ML, supported by IoT platforms, to leverage their core data assets. The rate of adoption of these technologies is increasing rapidly.

A recent survey by The Manufacturer and IBM indicated that 65% of manufacturing decision makers were working on adopting, implementing or using AI and ML.

This trend is set to accelerate with AI and ML in the manufacturing market expected to grow at a CAGR of 57.2% over the next five years as manufacturers realize the easy opportunities afforded by data. .

With our experience of working closely with leading manufacturers in the industry and as the winner of an Innovate UK award for building a global predictive analytics service, we want to share with you a vision of applications of AI and ML inside and outside the factory.

AI outside the factory

A typical supply chain involves many sub-actors, logistics interfaces, and geographic locations. The supply chain is described (next page) as a linear flow, but it is usually complex three-dimensional networks. If at any time, especially if the supply chain is lean, there is a failure in one of the supply network nodes, many other parts may fail.

TDAB diagram showing supply chain

Synchronizing supply and demand is essential to avoid the supply chain whiplash we are currently experiencing. Additionally, as manufacturing becomes more sustainable and business models such as product-as-a-service and reuse/remodel become more common, knowing the condition of your product in the field will become essential.

AI and ML have a key role to play in the supply chain where technology can be used to predict risks and future behaviors so that risks can be mitigated and the use of assets and products is maximized.

AI inside the factory

Digital technologies now allow you to create flexible and intelligent, non-human manufacturing processes that can quickly adapt to changes and enable the manufacture of bespoke products at scale. These are the principles of Industry 4.0.

The Internet of Things (IoT) is at the heart of this and provides the informational platform for AI and ML to be applied as it connects the physical world, inside and outside the factory, to the digital world through sensors and edge and cloud computing. .

AI and ML can then, through analytics, provide insights, predict the future, and initiate real-time changes often enabled through the IOT platform and factory business systems.

TDAB_diagram showing AI and ML applications in the factory

However, with advances in APIs, natural language processing, and the advent of federated learning, the data that AI and ML need to learn doesn’t just have to come from your factory or supply chain. .

Information from your factory can be combined with open source and market data, such as raw material prices, weather conditions, market behavior, competitor actions and also from all your factories around the world.

This capability multiplies the power of AI and ML at scale and brings a new level of artificial intelligence to your entire organization. This is called a strong AI. Manufacturers must quickly access their information and overcome all time, financial and management barriers to enable the use of this technology. Click on here to download our free guide to AI inside and outside the factory and learn how to build and scale AI.


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