7 Reasons Why Machine Learning Is Crucial in Supply Chain Management


Machine Learning and Artificial Intelligence have revolutionized Supply Chain Management, making it more efficient, cost-effective, faster and safer. Machine learning models streamline the process of deciding not only how to deliver a product to a particular place, but also when to do it and where to send it. This saves expenses without compromising quality and efficiency, and maximizes profits at the same time.

Since the emergence of effective machine learning algorithms and techniques in the early 2000s, new applications for machine learning are being found every day. Machine learning techniques have found uses in every aspect of every industry.

Today, I will showcase how machine learning has become a crucial factor in supply chain management. Here are a few reasons why:

1. Analyze large amounts of data to provide improved and accurate demand forecasts

Using machine learning algorithms, industries can analyze data in large amounts and with a large variety. These analyses used to be carried out manually, which was very time and resource consuming.

Machine learning algorithms allow industries to analyze data with different variables that, in the past, would have make the data too complicated to be analyzed manually, while also improving the accuracy of the results significantly. Variables like historical data, promotional activities in the market, trends, competition, market specifics, online presence, and more, all can affect market demand. By teaching learning-models to process all these variables and more, we can increase their accuracy to unbelievable levels.

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2. Plan goods movements to satisfy predicted demands

Every step in a business is a min-maxing problem. You have to minimize the costs while maximizing the profits and gains. Finding a cheap yet fast and safe way to deliver your product in profitable quantities to places that have demand for them is a good example of such a problem.

Machine learning models can predict demand for products with reasonable accuracy and can also figure out the best way to deliver the goods to the desired locations to meet this demand. They do this while also managing the balance between quantity, costs, safety, and speed.

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3. Improve customer satisfaction while minimizing cost by managing last-mile delivery

About 28% of the delivery costs for any product, corresponds to last-mile delivery. Everybody online enters their addresses and contact details differently. The delivery firms need to decipher these addresses to be able to deliver the products correctly.

A machine learning model can learn how to decipher them accurately, and also manage last-mile deliveries of every product increasing efficiency and customer satisfaction and minimizing the costs all at the same time.

4. Assist in inspection to confirm product quality

In any supply chain, the goods and products change hands multiple times. If the goods are damaged at any point it is difficult to verify the guilty party. As a result, it is the current holder’s responsibility to inspect the damages and inform the next link in the chain.

Every holder of the goods in the chain does their own verification before handing over the product. Product verification and inspection is a lengthy and tedious process if done by humans.

Machine learning models with visual recognition can make this process fast and efficient. This not only reduces manpower costs but also the time delays in the chain.

5. Manage supplier contracts

Managing suppliers is one of the most difficult tasks in any supply chain. Judging the reliability, timeliness and product care of a supplier is crucial to selecting one. Another important factor is the terms of the contracts with these suppliers. What kind of guarantees does the supplier make and what precautions will you have to take on your end.

Machine learning can analyze available delivery records, suppliers, and existing contracts to judge the most trustworthy and reliable parties and advise for future dealings.

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6. Improve production planning and factory scheduling

When you have predicted the market demands and also optimized your goods delivery process, you have a good idea of how long before you should start the production of the goods. Your product should be at the correct location at just the right time before the demand picks up.

With machine learning, you can find the right time to start acquiring the raw material and start the production so that your product can be on schedule to meet the demands and make you the desired profits. You can plan your production cycles and also schedule your factories and production plants appropriately to maximize the profits.

7. Detect abuse of the supply chain access and privileges

Throughout history, import/export businesses and large supply chains have been abused for smuggling operations and other illegal activities. Also, the access privileges to the goods are also misused quite often.

Machine learning models can keep track of the goods easily and efficiently, which can help reduce the losses on the way. It can also reduce the chances of theft, privilege misuse and other criminal activities occurring around and related to the goods.

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