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Nowadays, the logistics and supply chain industry is taking the opportunity to adopt artificial intelligence at a very large scale. The core objective of leveragingAI in SCM is to boost productivity and increase efficiency. With diverse capabilities and huge potential, AI is rapidly revolutionizing traditional supply chain approaches to make the industry more sustainable, and future-ready. In this blog, we will discuss how artificial intelligence controls supply chain disruptions. Let’s get started.

AI-enabled use cases for supply chain management

AI and Analytics-based systems help supply chain businesses with automated and reliable data visual platforms. Let’s have a look at the top 5 AI use cases that efficiently manage supply chain disruptions.

1. Demand forecasting in warehouse management 

Demand forecasting refers to data-rich modeling that helps optimize inventory levels, improve customer satisfaction, and manage warehouse operations efficiently. AI-based forecasts also allow warehouse experts to make informed decisions and build effective strategies for inventory stocking. Important use cases of AI-based demand forecasting are:

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  Data analysis

  
  


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  Inventory Optimization

  
  


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  Statistical forecasting

  
  


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  Collaborative Forecasting

  
  


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  Continuous Monitoring 

  
  


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  Resource planning

  
  

2. AI-based vehicle maintenance

Artificial intelligence helps in vehicle maintenance recommendations by offering data-driven and proactive solutions. This leads to improved vehicle reliability, optimized vehicle maintenance schedules, and minimized downtime. Here is how artificial intelligence is used for vehicle maintenance: 

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  Predictive maintenance

  
  


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  Anomaly Detection

  
  


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  Data Analytics

  
  


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  Cost optimization

  
  


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  Fleet management optimization

  
  


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  Prescriptive maintenance

  
  

3. AI in adding portability to the loading process 

Supply chain operations involve detail-oriented analysis. This means how goods are loaded or unloaded from the containers. Logistics and supply chain agencies leverage accurate supply chain data analytics and the combination of hardware and software to get real-time visibility into the loading and unloading process. AI significantly improves the portability and visibility of the loading process in the supply chain by managing loading strategies, minimizing errors, and increasing efficiency. Some of the key benefits of AI in adding portability include:

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  Optimal loading strategies

  
  


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  Predictive analytics

  
  


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  Dynamic load balancing

  
  


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  Visualization & Simulation

  
  


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  Route optimization

  
  


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  Quality control 

  
  

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