Sensors are a critical part of smart data collection in the manufacturing sector. A smart sensor can collect data in real-time to help artificial intelligence (AI) software and employees make smarter decisions. To increase the effectiveness of your AI and machine learning (ML) solutions, explore the value of sensors on the production line.

What Data Do Sensors Collect?

Sensors can collect data from machines and equipment or the environment. A smart sensor typically contains a microprocessor and communication capabilities for relaying data to a central system, such as an artificial intelligence (AI) system.

Smart sensors are key components of Industry…

Artificial intelligence (AI) has the potential to revolutionise the manufacturing sector. AI helps manufacturers reduce waste and develop more efficient processes. However, AI is unlikely to provide satisfactory outcomes without a focus on the human end users.

Here is a closer look at the importance of a human-centred AI approach in manufacturing.

What Is Human-Centred AI?

A human-centred design involves the development of AI systems that collaborate with humans. With a human-centred approach, AI systems are set up on the existing data and human input is included where either data is not available or more creative problem-solving skills are needed…

Manufacturing is going through the fourth industrial revolution as many are using large-scale data analysis, artificial intelligence, machine learning, and the Internet of things (IoT) to dramatically improve efficiency. The following information explores the impact of these initiatives on the manufacturing industry and the business processes that often have not changed for decades.

Big data and data analytics

With increasing complexity of Industry 4.0, data-based decision making has become the new norm in manufacturing. Traditionally it has been sufficient to rely on engineer’s knowledge and maintaining buffers to handle fluctuations in production. …

Artificial intelligence (AI) technology has progressed rapidly in the past decade. AI algorithms can process data and make decisions in real-time, achieving a level of decision-making that typically requires human input.

Manufacturers began incorporating AI in the 1960s. However, early AI technologies essentially provided automation and could not make complex decisions.

Manufacturers now use AI for a wide range of applications. More companies are starting to discover the value of industrial artificial intelligence. About 63% of manufacturers are already reporting revenue increases from AI adoption.

Here is a closer look at some of the most impressive ways to incorporate AI…

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…

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…

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. …

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…

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…

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…

Hedi Hunt

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