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4 Examples of AI for Supply Chain

Artificial intelligence (AI)  is not simply affecting supply chain management, it is revolutionizing it.

With the power to drastically increase efficiency in all areas of the supply chain, McKinsey estimates that firms could gain $1.3 trillion to $2 trillion a year from using AI in supply chain and manufacturing. 

Here are four examples of AI and how it’s changing supply chain management for the better.

Autonomous transport

There’s nothing more exciting than the field of autonomous transport for supply chain management (SCM). We’ve all known for many years that driverless trucks have major potential to affect supply chain management and logistics.

We aren’t there yet – and “anyone employed as a driver today will be able to retire as a driver” —  but if autonomous trucking can be developed to its potential, the technology would effectively double the output of the U.S. transportation network at 25% of the cost.

The conversation is no longer simply talking about vehicles on the road either. Google and Rolls-Royce recently partnered to build autonomous ships too.

Image courtesy: Rolls-Royce

Image courtesy: Rolls-Royce

Final-mile delivery route efficiency

One doesn’t have to have a driverless vehicle, however, to use AI to optimize delivery logistics.  For example, in the “devilishly complex”  task of delivering 25 packages by van, the number of possible routes adds up to around 15 septillion (that’s a trillion trillion). 

That’s where route optimization software and AI-powered GPS tools like ORION — which UPS uses to create the most efficient routes for its fleet — are making their mark.  With ORION, customers, drivers and vehicles submit data to the machine, which then uses algorithms to creates the most up-to-date optimal routes depending on road conditions and other factors.

And there are also other autonomous entities out there besides cars, trucks, and ships. Robots using Light Detection and Ranging (LIDAR) technology are now being used to deliver items in crowded city environments. For example, Marble’s robots deliver medicine, groceries, and packages, and they also track their routes and the condition in order to continuously improve delivery for the next time. Additionally, last-mile solutions with drones continue to be explored due to their ability to move quickly and bypass almost all ground-level obstacles.

Image courtesy: Marble Robotics

Image courtesy: Marble Robotics

Demand forecasting, particularly for warehouse management & SCM strategy

Machine learning has the ability to quickly discover patterns in supply chain data by relying on algorithms and constraint-based modeling to find the most influential factors. This ability to find data patterns without human intervention has applications in every aspect of SCM, but demand forecasting is a particularly influential component beneficiary.

Warehouse management and SCM strategy rely heavily on correct supply, demand, and inventory-based management. Forecasting engines with machine learning offer an entirely  new level of intelligence and predictive analysis of big data sets that provides an endless (and constantly self-improving) loop of forecasting, overhauling the way we manage inventory and the way we create new strategies for our industries. 

Chatbots for marketing and operational procurement

The increasing popularity of chatbots is making it harder to ignore how AI is helping shape not just the daily logistics but also the business-to-business (B2B) marketing landscape and operational procurement for supply chain industries.

A chatbot is a computer program that simulates human conversation using auditory or textual methods. It communicates with your customer inside a messaging app, like Facebook Messenger, and is similar to email marketing without landing in an inbox.  Mimicking a human conversation, chatbots currently allow for increased customer engagement through messaging app technology that isn’t yet saturated with marketing. They are just one of the many ways to integrate AI and marketing.

There’s so much more than this handful examples to consider when discussing AI and the supply chain: prediction of delivery arrival times to the warehouse and to the customer,  cargo sensors, automated purchasing, financial applications…the list literally may be endless.

Choosing what to focus on for this article, and more importantly, for all supply chain and logistics businesses, is a tough decision, but one thing is clear:  in the “arms race” to leverage AI in SCM, some will come out on top and some will be left behind.

3 comments on “4 Examples of AI for Supply Chain

  1. ChristopherJames
    March 24, 2019

    Now we're talking! It is time for AI to step into the supply chain world to improve how logistics all around the world is being run. We need to ensure that we can approach a more progressive methods that can further improve efficiency on order fulfillment from an overall perspective.

  2. UdyRegan
    March 26, 2019

    I have actually been reading quite a fair amount of articles that have been talking about how the logistics industry needs a artificial intelligence. There are a lot of contentions though. A lot of people are going to be out of a job when that happens, as much as efficiency is going to be increased, and that's not something that we should take lightly.

  3. harryramos
    March 28, 2019

    AI incresing very rapidly it's have high rate cons as compared to pros

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