7 Ways Machine Learning is Improving Supply Chain Management
Machine learning is shaping the future of supply chain and logistics management, improving accuracy, speed, scale, and more. Here’s how.
- Machine learning refers to an application of artificial intelligence that lets systems learn and improve automatically based on experience.
- Experts predict that 95% of supply chain planning vendors will rely on supervised and unsupervised machine learning for their solutions by 2020.
- When paired with the Internet of Things, machine learning can provide cost savings around $6 million per year.
When it comes to the future of the supply chain, machine learning is one of the most exciting applications of artificial intelligence (AI) technology out there today. Machine learning is a mode of data analysis that provides systems with the ability to learn and improve automatically from experience, without being specifically programmed.
Gartner recently projected that by 2020, 95% of supply chain planning vendors will rely on supervised and unsupervised machine learning for their solutions. Furthermore, it isn’t just expert predictions that demonstrate the impact and potential of machine learning for the supply chain. Amazon, for example, is using machine learning to improve accuracy, speed, and scale for its Kiva robotics, and DHL relies on machine learning to power its Predictive Network Management system.
So, what is it about machine learning that makes it ideally suited to meet the challenges commonly faced by supply chain companies? The answer lies in the fact that machine learning algorithms are brilliant at detecting patterns, anomalies, and predictive insights. This makes it the ideal technology to help supply chain companies forecast error rates, reduce costs, improve demand planning productivity, and increase on-time shipments.
Here’s how these remarkable technologies are already revolutionizing supply chain management.
7 ways machine learning is improving supply chain management
1) Logistic solutions
Particularly when it comes to resource scheduling systems, machine learning algorithms are driving the next generation of logistics technologies. An April 2019 report from McKinsey predicts that “machine learning’s most significant contributions will be in providing supply chain operators with more significant insights into how supply chain performance can be improved, anticipating anomalies in logistics costs and performance before they occur.”
2) Internet of Things
The Internet of Things (IoT)’s sensors, intelligent transport systems, and traffic data generate a tremendous variation in data sets. Machine learning has the potential to deliver increased value by analyzing these data sets, thereby optimizing logistics and ensuring that materials arrive timely.
Additionally, machine learning can reduce logistics costs by uncovering patterns in track-and-trace data captured through IoT-enabled sensors. A December 2018 study by Boston Consulting Group determined that pairing machine learning (specifically Blockchain) with the IoT can contribute to cost savings of $6 million per year.
3) Preventing privileged credential abuse
A recent article in Forbes points to privileged credential abuse as “the leading cause of security breaches across global supply chains.” Machine learning can prevent these abuses by verifying the identity of anyone requesting access, as well as the context of the request and, most importantly, the risk associated with the access environment.
4) Reducing fraud potential
In addition to reducing risk and improving product and process quality, machine learning can reduce the potential for fraud in the supply chain. For example, machine learning startup Inspectorio is a solution to the problems “that a lack of inspection and supply chain visibility creates, focusing on how they can solve them immediately for brands and retailers.” Their algorithm provides insights that instantaneously reduce the risk of fraud.
5) Reducing forecast errors
According to a recent report from Digital/McKinsey, “Lost sales due to products not being available are being reduced up to 65% through the use of machine learning-based planning and optimization techniques.” The same report observes that “inventory reductions of 20 to 50% are being achieved today when machine learning-based supply chain management systems are used.”
6) Detecting inconsistent supplier quality levels
Machine learning can help manufacturers combat one of the biggest problems they face today, namely a lack of consistent quality and delivery performance from suppliers. These technologies can quickly detect and address errors, as well as determine highest and lowest performing suppliers.
7) Preventative maintenance
Preventative maintenance is a tremendous strategic asset for the supply chain. And, when paired with machine learning, it “allows for better prediction and avoidance of machine failure by combining data from the advanced IoT sensors and maintenance logs as well as external sources,” according to the same Digital/McKinsey study mentioned above. Not only that, “asset productivity increases of up to 20% are possible, and overall maintenance costs may be reduced by up to 10%.”
The bottom line: machine learning is reinventing supply chain management
Not only has machine learning already realized tremendous value for the supply chain, but the very nature of this technology means that the possibilities are virtually endless. Algorithms continue to become more sophisticated, and, as new challenges arise, machine learning grows and evolves to meet them.
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