In the evolving landscape of plastics manufacturing, the integration of deep learning and AI technologies is redefining standards for quality control, operational efficiency, and sustainability. This transformative approach goes beyond enhancing production processes;...

In the age of digitalization, real-time data is transforming the manufacturing landscape. With insights provided at the speed of thought, manufacturers can make informed decisions, refine processes, and adapt to rapidly changing conditions.  The integration of...

In the rapidly changing industrial landscape, continuous improvement in manufacturing has become a prerequisite for sustainable success. This concept refers to an ongoing effort to enhance product quality, streamline operations, and optimize performance across the...

From the steam-powered looms of the first industrial revolution to the automated production lines of today, the manufacturing sector has continually evolved. In this ever-evolving saga of industrial progress, the fourth industrial revolution brings with it the...

The manufacturing industry has long relied on quality control to ensure the safe, efficient, and high-quality production of goods. However, recent advancements in deep learning and machine learning technology have transformed traditional quality control methods,...

As the manufacturing industry evolves, autonomous manufacturing is becoming a reality for many businesses. But manufacturers must first digitize their factories before they can reap the benefits of autonomous manufacturing.  Digitization involves integrating...

Industry 4.0 is the fourth industrial revolution that is changing the way we manufacture products. It is characterized by the use of interconnected technologies such as the internet of things (IoT), artificial intelligence (AI), and machine learning (ML) to optimize...

With the ability to digitize and automate virtually any process, digital manufacturing has become increasingly popular in recent years. However, there can be a number of factors that cause digital manufacturing to fail. Inaccessible or irrelevant data, operational...

Achieving success as a manufacturer requires staying ahead of the competition. This means continually innovating, investing in resources, and leveraging new technology—including Artificial Intelligence (AI) and continuous manufacturing. In this blog post, we'll take a...

Today, pulp and paper manufacturers are under immense pressure to improve efficiency and cut costs. One way they address these challenges is by turning to AI-driven autonomous optimization. AI-driven autonomous optimization can help paper and tissue manufacturers...

The manufacturing industry is facing a skilled labor shortage. As the industry looks to the future, one of its biggest challenges will be attracting a younger workforce. The problem is that the current perception of manufacturing jobs is inaccurate. Many people -...

The skilled labor gap is a real and pressing concern for manufacturers. As baby boomers retire or leave the workplace, there are simply not enough skilled workers to replace them. This is expected to have a major impact on the manufacturing industry as companies...

Over the past few decades, the manufacturing industry has gone through multiple stages of transformation aided by four industrial revolutions. Now we are on the cusp of another major revolution led by data science and artificial intelligence. Though data science is...

In the pulp and paper industry, there is a lot of excitement about the potential of big data analytics and Industry 4.0 to optimize plant operations further. Driven by global demand, sustainability efforts, an aging workforce and margin pressures, the time has come...

Using artificial intelligence (AI) in pulp mills, which have traditionally been paper factories that employ human decision-making almost completely, is one of the ways to lower costs. This article explains why AI is crucial for a successful pulp and paper...

Interesting fact: there are currently no sustainability standards for pulp and paper mills even though sustainability is becoming more important globally. Another interesting fact: It takes more than 400 years for a tree to remove its carbon footprint. Why...

The pulp and paper industry is one of the largest industrial sectors in the world. Over the past two decades, it has experienced considerable change, including digital technology advancements, environmental factors, rising energy and labor costs, sustainability...

In Industry 4.0, there is a need for automated diagnosis of process monitoring alarms along with root cause determination and process adjustment. Computationally intensive and adaptable approaches such as those incorporated into ProcessMiner software are required for...

Artificial intelligence (AI) and machine learning (ML) are creating quite a name for themselves in the manufacturing industry and for good reason. Both AI and ML are helping manufacturers use factory data to streamline operations, improve processes and make better...

US Manufacturing Supply Chain Unprepared for Record Surge in Demand By: Tom Tulloch, Chief Commercial Officer, ProcessMiner™Manufacturing is forecast to come roaring back in the second half of 2021, according to many leading indices. This is fantastic news for...

The Industrial Revolution paved the way for the life we know today and far surpassed the era of simplistic conveyor belts and heavy manual surveillance. Production lines employ machinery and humans alike. Industries have always stepped up with technological advancement and thereby use a plethora of devices designed and produced to meet specific tasks on factory grounds.

A real-world dataset is provided from the pulp-and-paper manufacturing industry. The dataset comes from a multivariate time series process. The data contains a rare event of paper break that commonly occurs in the industry. The data contains sensor readings at regular time-intervals (x’s) and the event label (y).