How companies in the supply chain are using anomaly detection

It is no secret that machine learning is one of the most exciting technologies for modern enterprises. Machine learning has the potential to turn immobile and inaccessible datasets into insights that can be mined instantaneously. One of the largest areas of growth can be seen in the adoption of anomaly detect systems. The growth of these tools is such that the anomaly detection market is expected to reach $4.45 billion by 2022.

The growth of anomaly detection platforms is being driven by the upward trajectory of big data. By 2026 the revenue derived from big data is anticipated to hit $92.2 billion. Organisations around the world are relying on data to tailor services towards customers and improve organisational efficiency.

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Anomaly detection is the perfect tool for navigating this environment because anomaly detection platforms can analyse datasets to find patterns and can find anomalous instances that don’t match normal patterns established. The broad applications of this technology have found it developing a presence in almost any industry you can think of.

Managing supply chains, an anomaly detection example

Anomaly detection is all about making better decisions and there is no industry that needs accurate decisions more than the supply chain industry. In the UK alone supply chain inefficiencies cost $2 billion with more than 100 million hours wasted in procurement alone. Machine learning is just one of the ways that companies in the supply chain industry are confronting these challenges.

In an OpexAnalytics case study, a CPG company that used anomaly detection for demand planning saw a better forecast than their current solution 75% of the time. Using an anomaly detection platform for demand planning can help suppliers to ship goods with more efficiency.

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This is just one implementation of many that could turn the supply chain industry on its head. Traditional models for managing the transfer of physical goods are doing a poor job of fighting off inefficiency. Machine learning acts offers another way to ensure that items are shipped to the customer faster with less delays and damage.

No matter what industry you look at, it is clear that there is a vast gulf between traditional data monitoring practices and the demands of modern day enterprises. In the realm of the supply chain insights need to be delivered instantaneously to ensure deliveries are made on time. Machine learning will not only help to improve visibility over the supply of goods but will actively refine the transfer of goods from supplier to customer.

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Written by Tim Keary, Freelance Tech Writer.
Written by Tim Keary, Freelance Tech Writer.