Artificial intelligence can be harnessed in a few different ways to optimize the logistics chains . It can be particularly impactful for optimizing transportation . In a world constrained by labor , AI augments human decision-making . Organizations are increasingly investing in advanced machine learning techniques to help unlock additional value in the form of bots that mine enterprise and external data to find insights . These bots can now surface scenarios such as , “ What if I skip a particular node in my network ?”, “ What if I switch to an alternate mode of transportation ?” or “ What loads can be consolidated along some lanes ?”
For transportation , AI is and will play an increasingly relevant role in optimization . AI algorithms can optimize delivery routes for maximum efficiency , reducing fuel consumption , transportation costs , and delivery times . They can also greatly help with load optimization , determining the best way to load goods onto vehicles to maximize space utilization and minimize the required trips .
Blending automated systems with human labor on the warehouse floor is particularly critical at times like these when the industry is facing an epic labor crunch . Warehouse labor as a sector has a high turnover . However , the pace of warehouse automation is being outstripped by increased demand and consumption patterns , and companies can ’ t seem to hire fast enough to make up for the turnover . Because of this , in the short term , there is still plenty of room for warehouse labor to work alongside robots rather than be replaced by them . Over the longer term , as robots take on more of the mundane daily tasks , warehouse workers can be reassigned to higher-level tasks such as sorting ecommerce returns or troubleshooting robots .
Supply chain leaders and workers should not fear new technologies but embrace their possibilities . In the year ahead , we can expect to see continued advancements that help reduce the burden on the supply chain and logistics communities , empowering us all to work smarter , not harder .
Conclusion
At the core , the goal of improving last-mile delivery has a lot to do with ensuring an optimal experience for customers . In the year ahead , I expect to see companies focused on balancing a focus on their bottom line and the customer experience . Artificial intelligence and machine learning are key technologies being woven into supply chains to help these businesses meet their goals .
For logistics providers understanding the challenges and advancements that your customers are making in optimizing their supply chains is one step you can take in becoming an even better partner . Each step of the supply chain is labor-intensive and requires numerous processes which often result in silos . Any steps that can be taken to increase efficiency and reduce friction ultimately help benefit the end customer . CLDA
Nari Viswanathan is Sr . Director of Supply Chain Strategy at Coupa , where he manages the Go to Market strategies for various areas of Procurement and Supply Chain . Nari Viswanathan is a six times SDCExec Supply Chain Pro to Know award winner . Over the past 20 years , Nari has held VP and Director of Product Management , Research and Marketing roles at various companies . For more information , go to Coupa . com
28 customized logistics & delivery Magazine I fall 2023