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.
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.
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.
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.
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.
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.
Innovation in the manufacturing industry has allowed facilities to become more software-oriented than ever before. Data collected during standard operations can identify inefficiencies even when they are relatively minute, allowing for adjustments that will make processes as streamlined as possible. Over time, these manufacturing data analytics can increase production rates, limit waste, and even save energy.
As the use of sophisticated sensors increases, allowing real-time data to be available for analysis, the capacity of big data in the sector may even become more powerful.
Read on to find out about how to transform your manufacturing processes with the help of manufacturing data analytics.
Increasing cost efficiency and ultimately fattening the bottom line
Purchasing is a standard part of most companies’ supply chains, but one that can easily be ignored when you’re too busy trying to improve upon other aspects. Starting off from a faulty supplier or one that is a few cents too expensive per component may not seem like the end of the world, but if you produce thousands of products a day, a cent here or there will turn into thousands of dollars on your ledgers.
Manufacturing data analytics can help you understand the cost and efficiency of every component in your production lifecycle, all the way from your suppliers’ trucks. Advanced manufacturing data analytics can help you reach better decisions by visualising how each aspect impacts the final result. If certain components are constantly failing or are not doing exactly what they need, powerful analytics will help you spot them before they become an issue.
Predictive maintenance with manufacturing analytics
Manufacturing systems are constantly operating under heavy loads and any stoppage in work can translate to spiralling losses. Few things are costlier to a manufacturer than downtime. In some industries, it can cost thousands of dollars per minute and millions of dollars per year. Now, with all of the various sensors and connected devices included in today’s advanced equipment, it’s possible for manufacturers to use algorithms to uncover complications before they arise and fix minor issues before they become costlier problems.
Predictive maintenance has the potential to save manufacturers millions of dollars over the course of a year, prolonging the life of equipment and ensuring efficient operations. Thanks to the growth and advancement of big data platforms, it’s becoming easier and more cost-effective to gather these insights.
Effective time management translates to more productivity
One of the biggest problems manufacturers run into is wasted time. While manufacturing chains can be built with efficiency in mind, different factors may play a contributing role in reducing the overall efficiency of the line because of poor installation, misuse, or simply a lack of downtime coordination.
By using sophisticated data analytics and platforms, companies can gain real-time insight into how well their manufacturing lines are operating, both on a micro and macro scale. Understanding how downtime for a single machine can affect the chain, or how different configurations may improve overall efficiency, isn’t just a pipe dream, it should be a necessity. Generating actionable data that lets you realise real improvements in the overall process is a major advantage of applying analytics to manufacturing.
Create better demand forecasts for products
Every manufacturer knows that they are not just making their products for someone today, but also for the perceived demands that will/may emerge in the near future. Demand forecasts matter because they guide a production chain and can be the difference between strong sales or a warehouse full of unpurchased inventory. For most companies, forecasts are based on previous years’ historic values, and not on more actionable forward-looking data.
However, manufacturers can combine existing data with predictive analytics to build a more precise projection of what purchasing trends will be.
Manage your warehouses better
Another overlooked aspect of the manufacturing process is storage. Once products are ready to be shipped, they must be placed in warehouses before leaving for their destination. At this point, seconds and minutes become important, especially in a world that is increasingly embracing ‘just-enough’ and zero-inventory models.
Managing warehouses is more than simply finding space for products to wait. Establishing efficient arrangement structures, better product flow management, and the most effective replenishment procedures can improve operations, as well as your bottom line. Advanced analytics make it easier to understand how to improve your inventory and manage your warehouses better.
Bringing your manufacturing processes into the 21st century can be a straightforward process. By incorporating robust analytics and visualisation tools, you can build a more granular understanding of how your production line operates, and how you can streamline it further.
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