7 Ways Machine Learning is Improving Supply Chain Management.

The main news

  • Machine learning refers to the application of artificial intelligence that allows systems to learn and improve automatically based on experience.
  • Experts predict that 95% of supply chain planning providers will rely on supervised and unsupervised machine learning for their solutions by 2020.
  • When combined with the Internet of Things, machine learning can deliver cost savings of around $6 million per year.

When it comes to the future of the supply chain, machine learning is one of the most exciting artificial intelligence (AI) technologies today. Machine learning is a method of data analysis that provides systems with the ability to learn and improve automatically from experience without specific programming.

Gartner recently planned In the year By 2020, 95% of supply chain planning providers will rely on supervised and unsupervised machine learning in their solutions. Furthermore, it is not just expert predictions that show the impact and potential of machine learning for the supply chain. An example is Amazon. Using machine learning For Kiva Robotics to improve accuracy, speed and scale and DHL is based on machine learning. To develop the Predictive Network Management system.

So, what is it about machine learning that is ideal for meeting the challenges faced by supply chain companies? The answer is because machine learning algorithms are good at identifying patterns, anomalies, and predictive insights. This makes it the best technology to help supply chain companies predict error rates, reduce costs, improve demand planning productivity, and increase on-time deliveries.

Here’s how these amazing technologies are improving supply chain management.

7 Machine learning is improving supply chain management.

1) Logistics solutions

Especially when it comes to resource scheduling systems, machine learning algorithms are driving the next generation of logistics technologies. An April 2019 report from McKinsey “Machine learning will make a significant contribution by providing supply chain operators with more significant insights into how to improve supply chain performance, reduce logistics costs, and anticipate anomalies in performance before they occur,” he predicts.

2) Internet of things

Internet of Things (IoT) sensors, intelligent transportation systems, and traffic data create a huge diversity in data sets. Machine learning has the potential to deliver added value by analyzing these data sets, thereby optimizing logistics and ensuring timely delivery of materials.

In addition, machine learning can reduce logistics costs by identifying track and trace data captured by IoT-enabled sensors. December 2018 A study by the Boston Consulting Group It determined that combining machine learning (specifically Blockchain) with IoT could contribute to cost savings of $6 million per year.

3) Preventing the misuse of various credentials

A A recent article in Forbes It points to privilege abuse as the “leading cause of security breaches in global supply chains.” Machine learning can prevent these attacks by verifying the identity of a person, as well as the nature of the request and, most importantly, the risk associated with the access environment.

4) Reducing the potential for fraud

In addition to reducing risk and improving product and process quality, machine learning also reduces the possibility of fraud in the supply chain. For example, a machine learning startup Inspector It is a solution to the problems “lack of inspection and visibility of the supply chain, focusing on how to immediately solve it for brands and retailers.” Their algorithm provides insights that instantly reduce the risk of fraud.

5) reduce forecasting errors

As stated recently Report from Digital/McKinsey“Sales lost due to product unavailability are being reduced by up to 65% using machine learning based planning and optimization techniques. The same report states that “inventory reductions of 20 to 50 percent are currently being achieved when machine learning-based supply chain management systems are used.”

6) Identify inconsistent supplier quality standards

Machine learning can help manufacturers address one of the biggest challenges they face today: the lack of consistent quality and delivery performance from suppliers. These technologies can quickly detect and resolve errors, as well as identify high- and low-performing suppliers.

7) Preventive maintenance

Preventive maintenance is an excellent strategic asset for the supply chain. And, when combined with machine learning, it can “combine data from advanced IoT sensors and maintenance logs as well as external sources to better predict and avoid machine failures,” according to the same Digital/McKinsey study cited above. Not only that, “resource productivity can increase by up to 20%, and overall maintenance costs can be reduced by up to 10%.

Bottom line: Machine learning is revolutionizing supply chain management.

Not only is machine learning of great value to the supply chain, but the nature of this technology means the possibilities are endless. Algorithms continue to become more sophisticated, and, as new challenges arise, machine learning evolves and evolves to meet them.