AI in manufacturing: adopt early to gain market share

New digital technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) offer huge potential for manufacturers. With AI and advanced analytics to identify patterns and trends in the wealth of data generated by the IoT, the barriers between operational technology and information technology are breaking down.

Manufacturers can become data-driven in all aspects of business, enabling the companies to transform operations, restructure supply chains, improve efficiency, address skills shortages and create entirely new revenue streams and business models.

Despite the many benefits, the Manufacturing Leadership Council’s ‘Factories of the Future’ survey revealed that less than one in 10 (8%) of manufacturers are currently using AI – though a further 50% expect to deploy it within two years.

AI is still nascent in manufacturing today, yet these results suggest it could become mainstream in under 24 months. So, what can manufacturers gain from taking an early adopter approach to AI?

Making sense of data

Manufacturers are faced with a virtual tsunami of data – amplified by the IoT. It is now estimated that more than 75 billion connected devices will be in operation by 2025, the majority of which will be in the manufacturing sector.

In a recent McKinsey survey, 60% of executives confirmed that IoT data yielded significant insights, yet 54% admit that they used less than 10% of that IoT information.

So, how can manufacturers collect, process and use the masses of data available to gain market share, as well as to innovate and optimise for greater competitive advantage?

The key will be moving from Big Data analytics to AI-assisted analytics.

As part of this change, organisations will need to focus firmly on Enterprise Information Management (EIM) as a means to ensure information is properly captured, manipulated, managed and made available where needed.

Once a manufacturer has an EIM system in place, AI comes into play. Combined with advanced analytics, AI can bring together information from a wide variety of data sources, identify trends and provide recommendations for future actions – from changes to business process automation to supporting employees in their daily decision-making.

Customer-centricity

Manufacturers are moving from making a product, selling it and servicing it to product-as-a-service or even data-as-a-service business models. Using new business models to monetise data is now just as important as the traditional product profit.

As one example, Knorr-Bremse Group – the leading manufacturer of braking systems for rail and commercial vehicles – is now using business intelligence and AI-powered data analytics software to provide embedded dynamic dashboards and reporting to help its customers reduce maintenance costs and ensure better diagnostics.

Taking this data-centric approach ensures Knorr-Bremse Group can give its customers the flexibility to record and review data from across various IoT subsystems and to build their own reports and dashboards as required.

Eradicating downtime

The more traditional linear supply chains of the past are being replaced with an integrated digital ecosystem of partners, suppliers and customers. Implementing a digital supply network offers manufacturers a more agile and flexible approach to inventory management. With this new supply chain, connected IoT sensors can automatically reorder or replenish inventory, eradicating delays to the manufacturing process due to lack of components.

Similarly, predictive maintenance makes 24/7 uptime possible, while reducing the costs incurred when machinery or assets are out of commission. By using data, analytics and AI, manufacturers can proactively maintain machines, without the need for traditional shutdowns or unplanned downtime.

Closing the skills gap

AI-powered robots are increasingly being used to assist the human workforce, or execute repetitive, low-value tasks so employees can focus on jobs that need specific human skills. Yet, given that the Manufacturing Institute estimates there will be 2.4 million unfilled jobs in manufacturing by 2025 – more than half the total amount of manufacturing positions, manufacturers will need to find ways to automate more business and operational processes.

The time to start is now

While some manufacturers – particularly those in the automotive sector like BMW and Ford – have been quick to adopt AI, others have taken a more cautious approach. As with most technology, some companies prefer to be a fast follower, not an early adopter – choosing to see successful AI use cases before implementing the solutions themselves.

This caution is understandable. Implementing AI is not a simple process and solid foundations have to be in place. Without digitisation and a focus on data quality, it will be virtually impossible to properly apply AI to a process.

Yet companies shouldn’t delay exploring the benefits of AI until after they have fully digitised their processes and harnessed their data.

Harvard Business Review sums up: “By the time a late adopter has done all the necessary preparation, earlier adopters will have taken considerable market share; they’ll be able to operate at substantially lower costs with better performance. In short, the winners may take all and late adopters may never catch up.”

Rather than waiting, organisations should take an evolutionary approach based on building AI into the systems already in place to start reaping the rewards of AI and avoid losing out on market share.