Artificial Intelligence for Effective Supply Chain Management
AI and ML have disrupted traditional business processes in many industries. Supply-chain and transportation processes are also passing through an era of transformation with the introduction of AI and ML tools. Many leading logistics companies are harnessing these technologies to fine-tune their core strategies like warehouse locations. AI and Ml tools also help to enhance real-time decision-making issues like availability, costs, inventories, carriers, vehicles, and personnel.
The transportation industry has been accumulating data for decades as trucking, rail, and sea cargo was being tracked by satellite via telematics. Recently, IoT devices have been a significant contributor to data accumulation. These data feeds enable AI and ML tools to provide greater optimization and responsiveness across the whole of logistics, supply chain, and transportation footprint. Here are a few applications of AL and ML tools for supply chain, logistics, and transportation industry:
Predictive analysis: AI tools can collaborate with sales and marketing tools to predict when the customer will be ready to order. This collaboration can help the logistics team in the itinerary. For transportation needs, these tools offer predictive maintenance which helps to avoid breakdowns. Predictive maintenance also helps to meet the customer's needs and expectations by reducing the risk of failure.
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Strategic Optimization: many leading supply chain companies are learning to gather and comb information to make the best decisions regarding the deployment of inventories and transportation assets. IoT technology helps the industry to connect all the dots from origin to the customer location. The data provided by IoT tools can be fed to AL and Machine learning tools to present a range of range of scenarios for optimization.
Augmented Real-time Decision Making: Logistics companies handle a myriad of complex and repeatable tasks like combing through thousands of possible candidates, routes, and schedules that require analysis of a large amount of input data to make the best choices. AI and other related tools can automate the analysis and narrow their selections to very few numbers within a span of two or three seconds.
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