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Digital transformation is revolutionising every aspect of the way manufacturers cater the needs of the consumers. Robotics are replacing manual labour and artificial intelligence is helping companies make smarter decisions. The manufacturing processes of the 20th century are outdated and inefficient. Compared to AI-optimised facilities, older assembly lines experience increased downtime, waste, accidents, defects, and fraud. Here is a closer look at the importance and industry adoption of machine learning and digitisation in manufacturing.

What Is Digital Transformation?

Digital transformation refers to the implementation of digital technologies to replace manual labour or analogue processes. Digital transformation is pushing through in…


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Manufacturing methods continue to evolve. Companies that implement the latest technologies are enjoying increased efficiency, thanks to reduced downtime, fewer defects, and streamlined processes. The advantages come from an interconnected manufacturing facility with access to advanced monitoring and analytics. Connecting, automating, tracking, and analysing are now key practises for successful manufacturing.

Connecting New and Legacy Equipment

The latest innovations in machine learning (ML), artificial intelligence (AI), and automation rely on interconnected devices and equipment. Connected manufacturing equipment can communicate with central data acquisition systems and human-machine interfaces (HMI).

Some of the newest manufacturing equipment features electronic components for monitoring and…


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Digital transformation and becoming digitally mature have been the concepts that many companies have strived for the past 5 years. Investing a lot of recourses into the idea of having more efficient and automated business processes by adopting digital technology and replacing legacy solutions with newer versions. Unfortunately digital transformation success stories are few and far between with many left to wonder, what are the key measures of successful digital transformation?

Recent Gartner study revealed that although 87% of senior business leaders say digitalisation is a company priority, only 40% have been able to bring digital initiatives to scale. …


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As Neurisium is creating machine learning solutions, among other products meant for the manufacturing industry, we’ve seen varied approaches to onboarding new technologies. The most surprising potential customer queries of the past 3 years have been the ones, where machine learning is requested without considering the requirements or internal data readiness. This begs the question, if machine learning is overhyped and why so?

Hype vs Reality

It’s understandable that each year brings about new technologies that are more popular than others, such as Big Data, Cybersecurity and Cloud computing have done recently. Machine learning is definitely one of those elusive…


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The manufacturing industry has gone through several major disruptions in the last 50 years, each of which has happened abruptly and changed the way companies produce goods. The last disruption to take place was that of automation (Industry 3.0), where large portions of entire production lines of workers were replaced by robots of much higher precision and a smaller margin of error. Although automated, currently most production processes are still operated by engineers who manage their processes through intensive manual calculation and information analysis.

Industry 4.0 is aiming to change that. The current trend is to begin using data in…


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The global epidemic is putting the manufacturing supply chains through a demanding year in 2020, with severe supply delays and closing of plants around the world. With China being the largest key supplier of automotive parts, the crisis has been proving the current supply chains to be unsustainable, fuelled even further by trade wars.

Rising complexity of parts showing to be difficult challenge in the industry. When previously suppliers could feel secure providing a steady rate of relatively standard parts, the demand now is moving towards smaller and smaller quantities with rising complexity.

Rising demand for customisation is also a…


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Industry 4.0 initiatives are changing process management for managers, bringing in more data and new challenges with it. Currently only about 50% of business decisions are based on data analytics. The rest is based on experience or opinion. Why isn’t there more data-driven decision-making when the amounts of data in the manufacturing industry is ever increasing?

Gaps in the Collected Data

Although the data is collected many are battling with sticking to a comprehensive strategy, change management within the organisation and gaps in the organisational structure. …


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While the first digital twin model was introduced in 2002, they have truly become a reality in the last 5 years. Digital twins allow to create a complete digital replica of a physical object and use the twin as the main point of digital communication. Although developed mainly for the manufacturing industry, they are being applied to an increasing number of fields, such as facility management, smart cities, logistics and healthcare. Gartner predicts that by 2022 over 66% of companies with running IoT solutions will also have at least one digital twin in production.

There are many types of digital…


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While Industry 4.0 initiatives are being deployed in manufacturing companies, there are increasing conversations around whether processes can be automated and what approaches should be used for that. Although machine learning seems appealing to many, the viability of such solutions and readiness for their deployment should be thoroughly considered.

To make the decision making process easier, here are six aspects to evaluate before saying yes to applying a machine learning solution to a manufacturing process.

1. Aligning the Project with the Company’s Digital Strategy and Budget

Before considering machine learning, there must a Digital Strategy in place, determining how the…


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Machine learning (ML) and artificial intelligence (AI) rely heavily on data. The completeness, accuracy, and scope of the data directly influence the benefits of ML technologies. To increase the capabilities of AI systems, many companies have begun actively sharing their data with other companies. Data sharing provides an efficient way to increase the quantity and quality of available data.

Sharing data benefits everyone from manufacturers to suppliers. With greater data, AI systems are better equipped for improving smart manufacturing processes and reducing waste. They can also improve the logistics of the supply chain. As more companies practice data sharing, those…

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