How manufacturing data analytics is driving production automation
Manufacturing is arguably the most important industry in an economy, as it makes everything we use and love. It’s safe to say that the manufacturing industry has had a hand in making the world as we know it today and will continue to do so in the years + decades to come.
From the advent of coal and steam power to the adoption of electricity, the industry has reinvented itself several times over the last few hundred years. In recent years, thanks to advancements in engineering technology, production optimisation, and better quality control processes, the industry has grown leaps and bounds.
Now, as the world heads into the fourth industrial revolution, however, there is a growing need for the manufacturing industry to adopt novel and independent manufacturing technologies that can not only help handle contemporary challenges but also minimise the use of human resources in the production process.
There is consensus among manufacturers across the globe that production automation is the way to move forward in industry 4.0, and manufacturing data analytics is the key to implementing efficient automation processes that deliver better efficiency and productivity compared to human resources.
Data analytics is not a new concept for the manufacturing industry, but the dynamics of modern manufacturing demands greater integration of data analytics to deliver automated production processes that increase plant predictability, agility, and quality control.
In this post, we take a deeper look at how manufacturing data analytics drives modern automation efforts in the manufacturing industry.
How is data analytics used in the manufacturing industry?
Manufacturing data analytics platforms collect data and analyse it to reveal useful insights, which can be leveraged for various decision-making purposes. The additional insight makes these solutions incredibly useful across the production chain.
From equipment maintenance to studying market trends, manufacturing analytics can be leveraged for different use cases to make decisions that generate a higher ROI and lower operational costs.
The use of edge analytics to build smarter production equipment
Unlike in traditional manufacturing, modern production hardware is always connected to the web using a wide range of IoT devices in the form of sensors and cameras. These IoT devices collect and transmit data to centralised data processing systems to produce insights that help optimise the next production cycle.
That said, the increasing pressure to produce quality throughput on manufacturing plants means that they can’t wait until the next production cycle to optimise processes and minimise errors.
With edge analytics, instead of streaming collected data to centralised data analytics systems, manufacturers can build smart manufacturing equipment that process data in-device, and make real-time optimisations in the production process—ensuring tighter tolerances and reduced resource wastage.
Material handling in manufacturing plants, for example, relied heavily on human resources until recently. Today, with the use of manufacturing data analytics, manufacturers have automated material handling by using Automated Material Handling Systems (AHMS) that detect material requirements and deliver them to the particular workstation—making the interaction between different stages of the production process seamless.
The integration of robotics in the manufacturing process
Traditionally, manufacturers relied on human intensity to conduct tasks that require extreme precision or a high level of complexity—think businesses operating within the fashion and automotive industries.
That said, now, due to advancements in manufacturing data analytics, artificial intelligence, machine learning, and robotic technology, manufacturers can leverage advanced robotics systems that handle complex tasks that require a high level of precision.
Adidas, for example, has opened a robotic manufacturing plant, dubbed the Speedfactory, based in Germany, which utilises robots in every step of the production process—reducing the six-week shipping time from Asia, where Adidas produces the majority of its 400 million shoes per year.
Another fashion giant, Zara, operates 14 robotic apparel factories in Spain, which deliver new fashion products to the showroom floor within ten days of finalising the design.
When it comes to robotics in the production process, we can’t miss the automotive industry, as it was one of the early adopters of production automation through robotics. Almost all of the major auto brands utilise robotics in their production process to some extent. Tesla is even building multiple Gigafactories around the world that use fully automated production processes.
How can manufacturers benefit from data analytics?
When considering the big picture, manufacturers benefit from data analytics through a significant reduction in uncertainty across decision-making.
Today, senior executives often make choices with incomplete sets of information that lead to unforeseen consequences. What’s more, visionary and innovative choices may be perceived as risky because there is a lack of evidence to back up the practicality of the ideas behind them.
Manufacturing data analytics can eliminate the risk from the decision-making process by providing a complete set of information to back up more creative, intuitive choices.
Manufacturing data analytics is building the factory of the future
As the business world evolves and consumer preferences change, the manufacturing industry needs to reinvent itself one more time to keep up with the growing demand.
Manufacturing automation and data analytics hold the key to building future factories that are smarter and more efficient.