Estimated reading time: 2 minutes, 35 seconds

If any industry demonstrates the power of artificial intelligence to optimize performance, it’s supply chain. Product manufacturing, warehouse management, and distribution must each work to keep the whole system going. A break down in one area could lead to disaster, breaking the chain.

A comparison between traditional practices and the use of artificial intelligence shows the ways in which the industry could leverage AI to mitigate or even prevent an interruption to the supply chain.


From automating procurement to machinery maintenance, AI anticipates and corrects problems with production.

  • Before: At the manufacturing level, materials management, production, and fill rates require people to track, respond, and predict. However, fluctuations in customer orders and delays in response times due to staffing or line capacity created unpredictable interruptions or shortages in stock levels. While preventive maintenance schedules kept machines running, they could not prevent all unexpected downtime, causing delays often at the worst possible times.
  • After: With AI, programs are becoming more sophisticated in monitoring and predicting ordering patterns in order to ensure proper material and labor levels required to meet demand. Algorithms analyze data to predict customer orders and adjust production as spikes in orders occur. Equipment can be fitted with sensors to send alerts of impending breakdowns before they occur to keep lines moving.


From order picking to loading the trucks,  machine learning has reduced massive labor-intensive tracking and monitoring into integrated systems that update and analyze data.

  • Before: Warehouses relied on humans to enter data to track inventory. Databases required massive lists that followed items through the system to provide enough supply to meet demand. Adjustments were constant: surplus created from a typo or pickers on the floor might mistake item numbers or pull wrong quantities. The result was copious amounts of hours spent tracking and monitoring data, and then cycle counting to verify accuracy.
  • After: With machine learning for warehouse management, suddenly those hours are reduced to seconds as programs manage data analysis to forecast and, in some cases, make decisions. Now, order-fulfillment systems integrate inventory monitoring programs, AI assisted or even robotic picking, and intelligent product staging for efficient, accurate loading onto trucks to prevent customer stock-outs.


While still only potential tech, autonomous vehicles will optimize logistics of distribution to be faster, more accurate, and cheaper.

  • Before: The labor for transporting goods by truck, rail, and air are controlled by laws limiting the hours that drivers, engineers, and pilots can operate equipment, increasing the cost and limiting efficiency of transport.
  • After: Autonomous trucks, trains, and even delivery drones and cargo planes will eliminate the need for drivers and human-limitations, speeding up delivery times and increasing efficiency from dock to stock.

The ability for artificial intelligence to reduce disruption in the supply chain at so many steps in the process mitigates the issues that lead to delays, stock-outs, or excess inventory. Where once efficiency was limited by human interactions, artificial intelligence can augment and / or autonomously manage the same work with less mistakes. Where once the system was labor-intensive and disjointed, AI can make the supply chain leaner and integrated.

Last modified on Wednesday, 10 April 2019
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Deborah Huyett

Deborah Huyett is a professional freelance writer with experience working for a variety of industries. She enjoys and works with all types of writing, and she has been published or ghostwritten for blogs, newsletters, web pages, and books. A former English teacher, Deborah’s passion for writing has always been grounded in the mechanics while appreciating the art of writing. She approaches projects as creative challenges, matching voice and tone for any audience.


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