Creating Value from AI in Logistics
Automation is continuing its trajectory in the logistics sector as companies continue to adopt AI-enabled tools, digital twins and visibility platforms.
A recent article from McKinsey explores the best practices of how companies are turning this into value.
But first, a few statistics.
- 95% of shippers have now adopted at least one transportation AI use case, and a third already have five or more transportation-related AI use cases in operation.
- 93% plan to adopt four or more of the surveyed use cases.
- 74% of shippers now rate themselves four-plus out of five in their AI readiness across people, processes, systems, and governance.
The authors of the article discovered that companies that have been able to capture value by employing these technologies have three behaviors in common. (Excerpted below)
Making trade-offs visible
By pooling disparate data sources and layering in powerful analysis and simulation tools, the implications of decisions on cost, service, utilization, and risk become more visible within the same decision-making environment.
Moving analytics closer to operational decisions
Another way companies are creating impact with AI and digital capabilities is by moving analysis into the operating environment, instead of treating analytics as a separate activity. Simulation, optimization, and AI-powered analysis are applied where decisions are made: in transportation and warehouse operations, in inventory decisions, and in exceptions management.
Connecting decisions directly to execution
Companies that are linking information and analysis more quickly with actions that influence operational outcomes are creating value in diverse areas—from carrier selection and fleet allocation to warehouse configuration, exception intervention, inventory positioning, and fulfilment routing.