Big Data and AI in Manufacturing Processes 

We live in a consumerist society, and that means that products of all sorts are being made all the time. That’s been the case since by and large the start of the Industrial Revolution, but what’s being made in more recent years is definitely of a greater variety and that’s a good thing. So much more of what is made these days AND has widespread buyer appeal for people will be classified as tech, and that really shouldn’t come as a surprise given how incredibly digital our lives are now.

There’s more a cyclical arrangement with this though; technology is affecting manufacturing, and manufacturing is affecting technology to much the same extent. There’s a term that’s come into use recently and it’s probably one you’re not familiar with – the Industry 4.0 paradigm that puts some structure to how there is an exponential increase in the use of technology in manufacturing. For many of use the first thing that will jump to mind is the use of 3D printing for example.

That gives us one great example of how technology is at least to some extent revolutionizing manufacturing, and here at 4GoodHosting we’re going to always take an interest in any of these kinds of topics because better technology in manufacturing is front and center with data centers too plus a whole lot else that’s tied into what we do for people. So let’s take this week’s entry to look at big data and artificial intelligence and how they’re contributing to redefining what manufacturing is.

Collection of Building Blocks

Getting back to the Industry 4.0 paradigm, it is most characterized by an exponential increase in the use of technology. Software solutions are at the forefront for this, with enterprise resource planning (ERP), manufacturing engineering systems (MES), and computerized maintenance management systems (CMMS). Then there are the technology building blocks required for industry 4.0 with the IoT (Internet of Things), cloud computing, low latency network connectivity with 5G / Wi-Fi 6E, and big data analytics. And it’s big data working with the IoT that is situated to have the most integral role in manufacturing processes.

IoT sensors help with the various parts of the manufacturing process by capturing generated data, and the right ones are capable of sending this data over a low latency network to the cloud infrastructure. Data collection is continuous and happens at all times and as a result it is always a huge volume of data that is collected. One single manufacturer could generate terabytes of data every day and so using the term ‘big data’ is warranted here.

Making the right type of sense of the data and incorporating it properly is not easy. Plus, traditional analytical methods don’t fit here because they either can’t be scaled at all or they can’t be scaled anywhere to the extent they need to be. But here’s where AI steps in and assists in a big way. By enabling big data analytics gaining more accurate and usable insights from large volumes of data becomes possible.

To a lesser extent machine learning and deep learning technologies come into play here too.

Proven Impacts

Big data and AI have major impacts on modern manufacturing in more than a few ways. The five biggest of them are these”

  • Supply Chain Improvement

The complexity of most supply chains usually makes them challenging. Multiple vendors, suppliers, customers, warehouses, and distribution centers are all parts of them for most manufacturers. Organizations supply chains produce a large volume of data and existing procedures don’t cut it when making data visible in the way and extent it needs to be. AI can process large volumes of data and give the optimal way to manage the supply chain, plus more efficient synchronization of data from vendors, suppliers, and other supply chain partners. The result has the potential to be a more streamlined end-to-end supply chain.

  • Costs

Artificial Intelligence can also aid in the manufacturing process by reducing costs. Manufacturing facilities are cost-centers for organizations and it’s possible to cut back on the coast for manufacturing operations that boosts profits and / or the ability to sell products at more competitive prices. This can be done through mapping cost distribution and AI can also determine the importance of each line item in the expense table, identify waste, and be more actively involved in maintenance too.

  • Predictions

When it is paired with a large quantity of historical data AI can make solid predictions about various aspects of a manufacturer’s operations. This can be anything from optimizing production according to demand or getting ahead of predictive maintenance can also be done where machine breakdowns are predicted. Better inventory management can be a part of this too.

  • Quality Control

Artificial intelligence can contribute to quality control using hi-resolution images to identify defects. Machine learning and deep learning algorithms compare the products produced with the standard product and this may allow products with shortcomings to be identified and removed from the production line.

  • Product Development

Better analysis of big data may also mean better new products. One thing that is available is usage patterns from consumers made available as digital data. By identifying unfulfilled needs of consumers to develop new products AI can be very effective, and may be contributing to anything and everything from design, testing, production, and marketing to sales.

Post Navigation