Estimated reading time: 2 minutes, 48 seconds

supply chain aiSupply chain transactions, mostly in the form of EDI transactions, generate huge amounts of data. But the advent of IoT devices, RFID tags, and retail sales transactions are making what seemed to be unimaginable volumes of data unmanageable and as useless as when the records were simply deleted after they had served their purpose. Analytics was supposed to be the answer but even that has turned out to be insufficient. Artificial intelligence is stepping in to extend analytics and add action to its insights.

Money for nothing

Over the last few years companies have devoted resources to storing their data and developing processes to extract meaning from it. That’s provided some interesting dashboards and suggestions. But those insights still need to be evaluated by people and then processed and either acted upon or set aside. As those actions move through the process they are inevitably delayed and modified based on personal observations and opinions. And in many cases they end up being far from the original recommendation. So the value of the analytics has become watered down and ROI is questionable, leading some companies to rethink their data storage and retention practices.

Artificial intelligence is proving to be the answer to reduced effectiveness and for companies making the investments, AI is putting the results of big data and analytics into immediate action. The delays introduced by human processes are being replaced by instant responses that are making a difference in how and what happens, and leading directly to positive outcomes and reduced expenses.

Where’s the action

AI isn’t magic but its uses can seem like magic. But the reality is that AI builds on known actions like humans but does so based on amounts of input beyond what humans can consume, combined with rules and insights that are derived from human input and conclusions. Here are just a few of the ways AI is being put to use in supply chain operations.

Chatbots are making customer interaction faster and more efficient, and reducing the number of people involved by taking orders, answering questions, and then taking action based on those interactions.

SCM planning is predictive and AI is very good at making predictions based on the analytics it receives from historical data and current conditions. It can also take current world conditions into account and make adjustments based on what it understands.

WHM and forecasting as a segment of SCM can look at inbound and outbound shipments and take over the manual allocation of stock, something that humans can do but can also get wrong.

Self-driving transport is becoming a reality on the nation’s highways, making shipping easier and more reliable supplementing the shortage of OTR drivers.

Data management is important as the different data expands. Storage is less expensive than it was historically but is still an expense that can be minimized. AI is able to understand past and future trends in data volume, legal requirements for retention, and usage requirements, then manage data migration and any deletion that might be called for.

Supplier selection and management is a checklist operation for many companies. AI is able to collect information, evaluate performance and make decisions at scale.

Nuances of these and other functions are being developed by companies that understand the need for automated functions to reduce costs and increase efficiency.

Last modified on Tuesday, 20 March 2018
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Scott Koegler

Scott Koegler is Executive Editor for PMG360. He is a technology writer and editor with 20+ years experience delivering high value content to readers and publishers. 

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