9 Ways Machine Learning Is Changing Supply Chain Management
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9 Ways Machine Learning Is Changing Supply Chain Management

  • General News
  • 4th December 2021

There are always challenges when it comes to supply chain management. Businesses have been looking for ways to improve the system over time, and right now many have turned to machine learning AI. It has its place in many different industries, including supply chain management itself. Here’s how it’s changing the face of the industry right now.

Visual Pattern Recognition

One way machine learning really succeeds is in visual pattern recognition. After being fed data, it can start picking out patterns and help you explore what they mean for your company. When it comes to supply management, that helps you with physical inspection of physical assets, across the whole supply chain itself.

This helps you automate quality inspections, something that would normally be very time consuming and staff intensive. Some machine learning systems can check containers for damage, whether the product inside is damaged, and recommend the best course of action for that container.

Improving Forecasting Accuracy

The one thing every supply chain has to content with is the future demand for products. You can have a good idea of what your customers will want in the future, but it’s never as accurate as you’d like it to be. That’s something that machine learning is currently improving all the time.

‘Machine learning can use the data from past purchasing history and use it to help forecast future demand’ says Graham Ferguson, a business blogger from Write My X and Australia2write. ‘It uses advanced simulation modeling in order to do this, and give you a more accurate forecast.’ It allows you to take into account certain factors that are very hard to do so with other forecasting methods.

Reducing Costs

There are so many costs when it comes to supply chain management, and you’ll want to try and keep them down wherever possible. Sometimes though, there’s going to be incidents and times when the costs are going to shoot up and eat into profit. Many companies are finding that machine learning can actually help with this.

Machine learning uses data to improve shipping performance and reduce risk, so you can keep those costs down. For example, it can help you find the best times to ship a product out, so it can get to its destination as quickly as possible.

Lower Inventory And Operations Costs

Related to the above point, as a business you want to be able to lower inventory and operations costs, without losing any of the quality or expedience of your current supply chain. Machine learning is a way you can do this, without sacrificing anything else.

There are lots of different ways that you can see this in action. AI driven inspection will speed up inspection while requiring less staff. Self learning and self navigating AGVs can move stock around without direct intervention. You can also use vision based inventory management in your warehouses. They will all help with inventory and operations costs.

Forecasting Demand For New Products

The need for forecasting has been touched on above, but what about forecasting for brand new products? If it’s something that’s brand new and not seen by the public before, it’s very difficult to understand just how much will be needed.

This can be helped with the use of machine learning. ‘Good machine learning can take into account any factors that aren’t fully known yet’ says technical writer Alan Kieth, from Assignment Help and Brit Student. ‘It helps narrow down all the models that are currently in use, allowing you to get a more accurate forecast.’

Improving Supplier Quality Management

One of the biggest issues with supply chain management is finding the best suppliers available. You can use machine learning to make this task a lot easier, and improve the quality of your overall product.

Machine learning AI will evaluate quality patterns from suppliers, giving you a granular insight into what they do well. That allows you to see these patterns more clearly, and save a lot time and energy that would be put into this manually.

Extending The Life Of Supply Chain Assets

You may not have thought of this, but machine learning can actually extend the lifespan of key assets in your supply chain. There can be anything from machinery and engines to transport and warehouse equipment. This is done with the AI detecting usage patterns, and identifying more efficient usage patterns that will help you get the most out of the equipment overall.

That helps you save on assets, as you’ll get more usage from your equipment. That means less repairs and less replacement, saving money for your business.

Improving Build To Order Systems

Not all businesses will have stock ready and waiting to go when its ordered. For businesses that instead build to order, there’s good uses for machine learning AI too for their supply chains.

Using the AI, you can balance your supply chain latency. Using the AI, you’ll be waiting less time for key components, so you can get products to your customers quicker. The parts that are used in making the most customised parts of the product can be enhanced with machine learning too, making it much quicker to get products out the door.

Create End To End Visibility

What’s really exciting about machine learning is that it’s allowing companies to create much more end to end visibility overall. That’s something that you’ve never been able to have before. These patterns and insights are available for everyone to see, so all parties in the supply chain will be in the loop.

This can be done in conjunction with advanced analytics, IoT sensors, real time monitoring, and more.

Machine learning is fast changing the face of supply chain management for the better. These are just a few ways you can improve your supply chain, and make it better than ever before. Start looking into how you can make improvements, and get a better understanding of your business’s logistics.

George J. Newton is a business development manager, writing with Write My Dissertation and PhD Kingdom. He focuses on logistics, and how new tech can improve them. He also writes for Next Coursework.

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