The biggest impact of supply chain disruptions is felt at the tail end—in road transport, one of the most critical factors in a supply chain. The problem is compounded by driver shortage across the US and the UK, which is causing salary escalation as transport operators compete for this skilled but scarce resource. These disruptions have compounded the challenges that omnichannel had triggered long before the COVID-19 pandemic. For example, smaller parcel sizes with multiple destinations have made middle and last-mile transport planning particularly complex. Besides, customer expectations for fast, flexible fulfillment; and notifications on inventory availability, and scheduled deliveries or delays make transport planning a vital cog in the customer journey.
Ironically, despite being the nerve center of the supply chain, many businesses still consider transport as a necessary evil—an ancillary function requiring investments that cannot be justified—rather than as an integral and valuable aspect of the supply chain. Almost all operators of large (and many small) road transport fleets adopt either a template-based approach or utilize a route optimization product to roll out their transport plans, typically every day. Operators can put an end to temporary workarounds for operational challenges and realize both service improvement and cost reduction opportunities by leveraging improved data streams and new technologies. By leveraging the combinatorial power of AI and connected vehicles, GPS tracking, and sensors, retailers can now derive insights from large data sets, simulate what-ifs, pre-empt disruptions, and make recommendations for dynamic disruption handling.
This article emphasizes the need to pivot from a template-based approach for transport planning and offers six AI business cases for handling all the dynamics at play.
The original version of the article was published on Supply Chain Digital.