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How Machine Learning Helps Supply Chain Forecasting

Michael Wilson | Mar 8, 2019

When paired with the Internet of Things, machine learning can create a truly powerful system for supply chain forecasting. Machine learning is able to improve supply chain ...

When paired with the Internet of Things, machine learning can create a truly powerful system for supply chain forecasting. Machine learning is able to improve supply chain management in a multitude of ways, primarily through analysis and pattern recognition. Meanwhile, IoT devices can produce large, accurate data sets from which an organization's forecasting algorithms and tools can learn.

Analyze Large, Diverse Sets of Data With Machine Learning

Machine learning is frequently used for the analysis of "big data" sets—sets of data that are too large for traditional methods of analysis to work. When it comes to supply chain forecasting, a prohibitively large amount of data may be collected regarding an organization's processes and equipment. When data sets become too large for human eyes to process, artificial intelligence takes over.

The amount of data being collected by most companies is continually increasing—and this data can be used to provide very accurate simulation models for supply chain forecasting. Moreover, this data is often not in very rigid data sets; it is collected organically in diverse formats. Without machine learning, many systems would not be able to compare and contrast these diverse forms of data.

Achieve Better Pattern Recognition Through Artificial Intelligence

Machine learning can account for causal factors that traditional demand forecasting models may not be able to predict. AI machine learning doesn't just look for preset patterns for forecasting; it is able to mine deeply into extremely complex data sets to identify potential correlations. Through these complex data sets, machine learning will provide better simulation models of future environments. 

Traditional demand forecasting is generally based on correlations that have already been noticed by human eyes. Seasonal shifts in demand are one of the most notable examples. Machine learning can dig much more in-depth than these apparent connections, thereby improving the accuracy of its forecasting models. 

Reduced Costs and Quicker Response Times Through Better Intelligence

Having a more accurate supply chain forecasting system means an organization has the capability to better optimize its forecasting. Organizations can pinpoint areas of inefficiency that they can improve, while also projecting where they may experience roadblocks or bottlenecks in the future.

Better intelligence means that companies can also respond quickly to emerging threats; artificial intelligence and machine learning can be used to detect issues in the supply chain before it begins to disrupt the organization. The faster an organization can respond to problems, the more cost-effective the response will be. 

Better Management, Maintenance, and Repairs of Key Supply Chain Assets

When placed on supply chain assets such as machinery and equipment, IoT sensors are able to predict a need for repairs. When gear breaks down, these IoT sensors will immediately report the damage, so that the supply chain experiences far less disruption. When paired with machine learning, IoT sensors may also be able to predict when failure is about to occur. These predictions may lead to servicing equipment and machines before an issue arises, thereby reducing the costs associated with those repairs. Some MRO companies currently use this technology to save their customers time and money. Replacing a component before it breaks, during off hours, is much less expensive than a mid-day breakdown.  

Better maintained equipment will last longer, while also making sure that the supply chain experiences as little downtime as possible. IoT devices provide a far more cost-effective way of managing and maintaining equipment that would otherwise be achievable through human inspections. IoT analysis can also be done far more frequently than human inspections could be arranged.

End-to-End Visibility Through Real-Time Monitoring

IoT devices and machine learning further provide real-time monitoring throughout the supply chain. With the right sensors and reporting, an organization can track every item through its supply chain with ease. This allows for the identification of core inefficiencies that need to be resolved, as well as the ability to optimize and streamline supply chain routes.

End-to-end visibility further enhances accountability and transparency, making it easier to report upon the position of items within the supply chain, and reducing the chances that deliverables moving through the supply chain could potentially be lost or damaged. Real-time monitoring systems can be used to improve everything from logistics and to scale customer support. 

By integrating machine learning analysis into supply chain forecasting, organizations can achieve a better overall understanding of their supply operations and logistics. With IoT devices collecting large volumes of data—and artificial intelligence analyzing this data—companies can streamline and optimize their supply chain, providing better maintenance and superior overall outcomes.

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