Compared to the previous year, 2020 showed no significant increase in adoption of artificial intelligence, according to a cross-industry survey of nearly 2,400 organizations by consulting firm McKinsey & Company. Just 9% of supply-chain management professionals say they’re currently using A.I. tools to optimize logistics networks and inventory.
Perhaps stunted by the pandemic, A.I. still appears exotic in the supply-chain industry, and many organizations are trying to ascertain where it fits in to provide the most value. Here are some factors they should consider.
Decision making. Many envision A.I. as a tool to predict the future — to foresee outcomes, know precisely where trucks are and see how much capacity is available. But prior to adopting A.I., logistics organizations need to focus on what data they want to uncover and how the data will inform decisions.
For instance, being able to predict how a shipment will be impacted a day into its journey due to a weather event might not make much difference if the delivery window is three to four days and will still arrive on time.
Process optimization. A.I. is well suited for consolidation and optimization, using machine data to gather business intelligence and increase visibility. Processes are optimized through A.I. to catch errors early or answer repeated questions that don’t require human intervention.
A.I. could also be useful for fleet maintenance, routing, uncovering data on scalable service windows, etc. There could also be potential for determining capacity if more data can be uncovered and made widely available.
Troubleshooting. A.I. can provide proactive messages if there is a failure in the supply chain or a load doesn’t move. But organizations need to determine when it is appropriate to deliver a proactive message. Weather events or a delay at the border might be expected, making a proactive message less useful. Proactive messages are only useful if they are actionable.
Cost vs. need. If an organization has high-value products being shipped, the additional cost of A.I. makes good business sense. For instance, organizations shipping hazardous materials will likely pay more to know more.
If A.I. has an impact on business — reduces costs or makes life easier — a strong business case can be made. If not, there’s really no point in adopting it.
Outlook
The supply-chain industry is finding more ways to apply data to risk management and process optimization. As the next generation of digital-native leaders emerges, A.I. is likely to find its place.
