Candidates for supply chain jobs who can bring knowledge of machine learning-based anomaly detection platforms could help spare their organisations’ significant costs caused by supply chain inefficiencies, a new article in the online technology news source, Information Age, has claimed.
Anomaly detections systems based on Big Data and machine learning tech have seen a huge uptake in recent years, with the market in these tools expecting to hit $4.45 billion by 2022. The platforms are capable of analysing vast data sets, ‘learn’ typical patterns within them and pinpoint anomalous changes to these normal patterns at lightning speed. And they can make a significant difference to the performance outcomes of professionals holding procurement and supply chain jobs. In Britain alone, supply chain inefficiencies, which are instances of undetected anomalous activities proceeding unchecked, cost $2 billion and use over 100 hours of wasted procurement time. Machine learning provides a means of confronting the anomalies and stopping them in their tracks.
The article cites a case study of a consumer packaged goods (CPG) company that implemented an anomaly detection system for demand planning. The platform delivered better forecasting than the firm’s conventional method 75% of the time. Such platforms improve demand planning, helping suppliers ship goods more efficiently and preventing the nightmare ‘Out of Stock’ empty-shelf scenarios. This example represents one of a host of implementations of anomaly detection platforms – a technology that could turn the supply chain industry on its head. The truth at present is that traditional methods for managing the movement of physical goods are not combating inefficiency well enough. Machine learning provides a sophisticated means of ensuring that items get shipped to the customer faster, eliminating delays and delivering without damage.
As professionals working in supply chain jobs can testify, their work would be transformed if they could have access to instantaneous insights to ensure timely deliveries. Machine learning-based anomaly detection platforms can do that.
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