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Artificial Intelligence & Supply Chain Management

· Ai,Supply Chain Mgmt,Supply Chain,Automation,Automated Delivery

Artificial Intelligence & Supply Chain Management

Artificial Intelligence will continue to cut up the economy and remake it in a more efficient light. Jobs that pay less than $20 per hour are going to see the most initial seismic disruption. 83% of those jobs are in danger of being replaced by Ai systems being deployed at firms such as McDonalds and Kroger. How will these jobs be replaced? Will there be a replacement for people who lack a high school degree or any advanced skills?

Jobs that require human emotions, intelligence and creativity seem to be irreplaceable. However we just witnessed an Ai-created work of art sell at Sotheby’s and we are seeing automated psychiatrists being created through Ai as well.

Supply Chain Management is a complex skill that is being more and more efficiently run by artificial intelligence systems. A skill some thought would have stayed under human oversight for much longer than it is looking currently in the industry. The delivery industry has utilized artificial intelligence algorithms a lot for a number of years. Location, street layout, traffic patterns and weather forecast are included in the artificial intelligence parameters. The core value for artificial intelligence is to make accurate predictions. In the delivery business, it is important to make more precise predictions with help of advanced statistics. Using advanced stats, it is more efficient to predict the roads and time for the delivery. So there is the natural connection between Delivery and Supply Chain Management.

Predicting demands for the future can be a big problem for supply chain management. There are several areas that are important inputs for machine learning model, including promotions, media, web, market model, new products, and historical demands. The output from a machine learning model is going to include promotional lift, halo effect, segment, NPI launch profiles, seasonality and web lift. All of the information is going to run through demand modeling by Base-Line processing. There is a lot of information that needs to be collected before running all of the data through a machine learning model. Having comprehensive data sets can be the most important requirement to increase the accuracy of a prediction. As time passes, there are more data that collected from internet and predictions can be closer to what happened in real life. This is what makes many people worry about Facebook, Google and Amazon’s seemingly insurmountable lead in the data race.

Written by Yan Zimo & Edited by Alexander Fleiss

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