Predicting the future demands for production is one of the essential task in supply chain. At present, techniques like statistical analysis and averages to advanced simulation modeling are there. These methods are neither scalable nor efficient to track change in forcasting over time.
ML can be essential at minimizing freight costs, improving supply performance and minimizing ETA keeping various factors such as traffic, weather, time of day etc. under consideration.
This routing is vital for logistics and supply chain companies to ensure that transportation is done via a route on which the drop off locations are arranged in a manner so as to ensure minimum delivery time through an optimized route.
Machine learning, for instance, can be used to identify the type of delivery address – whether it is office or home – and the system automatically figures out the best time to make the delivery attempt. This increases the likelihood of addressee’s presence at the delivery address ensuring successful delivery and improving the customer experience.
ML also helps to keep the supply chain updated about weather forecasts, traffic situations and other important factors directly or indirectly impacting the delivery schedule. Incorporating all the variables for a best-case delivery schedule increases the likelihood of successful delivery and improves the customer experience.
With a combination of various algorithms like unsupervised learning, supervised learning and reinforcement learning, machine learning is a powerful technology that regularly finds key indicators which most influence supply chain performance.
With Machine Learning, physical inspection and maintenance of assets across an entire supply chain network can be done via pattern recognition eliminating the need to do it manually.
Using data from previous launch histories, machine learning can predict the category, region or product that will be most beneficial to launch for a company.
For assembly of a given product, a typical company depends on external supplier for more than 70% of the components. Supplier dependency in such instance increases tremendously. Based on historical data, a decision can be made for selecting a supplier who can provide the components with factors such as delivery time, reliability, cost etc.
In manufacturers who deal on build-to-order and make-to-stock production, machine learning makes easy to balance the constraints. By checking the prediction for demand of a particular product, manufacturers can focus on supplying the products which will have higher demands in the market.
Combination of machine learning with blockchain, analytics, IoT and real-time monitoring provides end-to-end visibility across many supply chains.
Based on the dimension of transport vehicle, optimum loading can be done by tracing the size of previous shipments and dimensions of the transport vehicle.
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