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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 topics that allows for people to dream about all its future possibilities. Often though, there is a disconnect between the dreams of self-learning robots that have the potential to take over the world and the mathematical and statistical supervised learning models deployed in machine learning today. …


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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 a more connected way, so that the full potential of robotic production could be achieved. We are currently on the cusp of Industry 4.0 taking full swing. Predictions show that “by 2024, enterprises with intelligent and collaborative work environments will see 30% lower staff turnover, 30% higher productivity, and 30% higher revenue per employee than their peers”. Even COVID-19 hasn’t been able to put the brakes on the digitalisation process, instead it has highlighted the need for automated and remote production even further. …


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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 hurdle difficult to overcome for such a complex industry. Current product development cycle can take years whereas customer demand is changing dynamically. …


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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 twins and advantages that they bring but there is no better way to gauge the possibilities for digital twin application, than looking at some real life examples. …


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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 company is planning to go about digitalisation and automation of processes. Often times those strategies are tied to tight budgets that require some creative solutions to implement. …


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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 that refuse to share are likely to be left behind. …


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Collecting data from the manufacturing process is the key to the future success of smart manufacturing. However, there are some rules of thumb to keep in mind.

Machine learning (ML) technologies are used for a wide range of applications, from image recognition to factory automation. No matter the application, the common feature of ML software is the reliance on big data.

As ML depends on data, the benefits quickly disappear when you supply inaccurate or incomplete data. In a survey of executives across various industries, 75% of respondents were not confident in the quality of their data.

Unlocking the full potential of artificial intelligence (AI) and ML requires a marriage of process and communication. Here are eight essential rules to remember for improving your data collection methods. …


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Artificial intelligence (AI) and machine learning (ML) are quickly becoming common parts of the manufacturing process. The latest technologies are simplifying the way that engineers solve problems, reducing downtime and increasing production throughput.

Many of the benefits of AI and ML technologies stem from the ability to replace experience-based decision-making with fact-based decision-making. Precision and efficiency are essential characteristics in the manufacturing industry. Engineering decisions are often made based on a careful analysis of the facts.

When you are dealing with components that require precise dimensions and large-scale production runs, you cannot rely on guesses or estimates. …

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