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Reshaping manufacturing business models

Mark Troester, VP of Strategy at Progress

Mark Troester, VP of Strategy at Progress argues that manufacturers should focus on the industry 4.0, the role of AI and ML as well as the potential of predictive maintenance technologies.

With the rise of industry 4.0 and the digital transformation of industrial markets, today’s manufacturing sector is experiencing a seismic shift. Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML) are leading the way and smart manufacturing at the forefront.

The introduction of AI and ML technologies is fundamentally changing the manufacturing process to enable enhanced data analytics that optimise production processes, improve reliability and boost productivity. By shifting the manufacturing paradigm, businesses can ultimately reshape business models to create more value, new revenue streams and enable better end-customer experiences.

Smart machinery and equipment carrying sensors and data points through AI and ML will provide manufacturers with the ability to analyse, visualise and productionise the data – enabling a truly cognitive predictive model for maintenance. Using AI and ML, manufacturers will also be able to provide greater focus on end-customers, establish and maintain better relationships, provide greater value and enhance product loyalty.

Data analysis through AI-powered platforms
Looking at the automotive sector as an example, recalls, faults and broken parts regularly make headlines around the world. They cost brands millions of pounds in repairs and significant reputational damage. Ford has recalled over 1.8 million cars and pickups since early 2019, with 1.7 million vehicles also recalled by Subaru, Tesla, BMW, Volkswagen, Daimler Vans, Mercedes and Ferrari. This number is also set to rise to 70 million, making it the largest series of automotive recalls in the US.

To identify and understand the root cause of the problem, many manufacturers are utilising data analytics. While this approach is effective, it requires a significant investment from data scientists to manually analyse data and spot patterns that will help them identify past failures or faults that are likely to reoccur. Most problems and faults are new and unknown to most industries, and just 20 percent are repeat problems. Manufacturers will need to model and update their products by relying on more than just learnings from past failures.

To move to the next level, manufacturers should look to utilise data analysis performed instead by AI-powered platforms. Using AI, they can track multiple components of heavy machines and identify highly localised and contextual signals to indicate potential problems. The technology enables an automated quality check process that identifies micro-anomalies and small changes that may go undetected in the process. ML also enables these platforms to learn continuously and go beyond the macro-patterns that the human brain tends to spot. In this way, breakdowns or faults can be predicted ahead of them occurring and sparking product recalls or causing downtime.

Predictive maintenance technology
Predictive maintenance can identify and share alerts on defects during the manufacturing process to enable problems to be addressed long before the product goes to market.

Predictive maintenance technology is most effective when it is deployed as a holistic approach across the various touchpoints in the production process. Through ML, the technology enables machines to mimic actions and environments, as well as working conditions and depletion. Using sensors that generate the data enabled by the automatic processing of the data, manufacturers can understand the health and readiness of equipment down to the molecular level.

However, the role of predictive maintenance does not stop once the product is out of the factory. To identify and solve problems faster once in the market, the cognitive predictive maintenance model can be derived from multiple data sources. These might include connected devices and service records, test data from parts that have been replaced, as well as social media. Using this approach, manufacturers can help to initiate a model fix sooner, or avoid a possible recall.

Enabling autonomous maintenance

As cognitive predictive maintenance technology advances, it has the potential to provide manufacturers with higher levels or productivity. It can also deliver real ROI by eliminating production and post-purchase problems and inefficiencies. Product recalls missed SLAs and operational inefficiencies may become a thing of the past. The manufacturing sector will be able to deliver unprecedented customer experience, higher margins, lower risks and a complete transformation of how manufacturing industry operates. The end game is to achieve a level of autonomous maintenance where expert intervention is only needed when necessary.

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