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Machine learning technology is increasingly being used to drive warehouse management and control systems. Our Kl nepal app itself is a technology driven logistics app which can parse data from multiple sources to calculate the time required to complete warehouse tasks. It can then use the results of those calculations, which factor in rapidly changing priorities and conditions, to optimise the way assets and resources work.

Such systems can interpret inputs and adapt to changes more quickly than any human could, which is why they hold so much value for e-commerce operations. Has AI transformed warehouse management or not? ,you might find answers in the following examples of AI application in supply chain operations interesting, intriguing—or even inspirational.


  1. AI in Transportation Management


The ROI of transportation management solutions (TMS) has been proven many times over. However, with the integration of AI technology, new platforms are taking TMS capabilities to unprecedented heights, offering even more potential for companies to gain efficiency in transportation and reduce freight costs.

Even without AI onboard, TMS applications can save money and help operators to increase service performance. They can do this by:

  • Helping resource planners to optimise routes for truckload and less-than-truckload shipments

  • Identifying when and where multi-stop routes prove more economical than single-stops

  • Highlighting the comparative performance of carriers

  • Analyzing data to answering questions such as “Which specific geographic areas are impacted most often by late deliveries?”


  1. Integrated AI in Warehouse Management Systems


AI can use live weather and traffic congestion data to create a more accurate, dynamic route plans, and real-time vehicle tracking can provide them with comparisons of planned versus actual route performance.

 Better still, machine learning enables all of these functions to be carried out without first building complex models, since the closed feedback loop (planned routes versus actual) enables the AI-equipped TMS to train itself by perpetually comparing inputs with outcomes. Several WMS ( Warehouse Management Systems)  vendors are integrating machine learning into their platforms to create new opportunities for companies to improve productivity and efficiency in their warehouses and distribution centres.


  1. AI as an Aid to Procurement Communications

Implementation of a chatbot, integrated with multiple systems, to solve issues relating to the inefficiencies of human-to-human dialogue is an example of AI as an aid to  procurement operations.

The potential uses of artificial intelligence in supply chain management are many and varied. For example, aside from enhancing the power and capability of systems like WMS and TMS, AI can enable computers to perform tasks traditionally reserved for humans.

Computer vision, natural language comprehension, and similar sensory developments have all been made possible by developments in AI technology, essentially giving machines the ability to see, hear, and communicate in similar ways to people.

Here are three examples we’ve shared above, we will be sharing more about companies using artificial intelligence to solve a wide range of supply chain problems in our next part of this blog. Till then use Kantipur logistics, KL nepal app for more efficient supply chain management.