Driving down distribution costs with Location Intelligence

The logistics industry faces pressure due to rising costs, a shrinking labor force, and unrealistic customer expectations. To address this, companies are using spatial data science to build data models that simulate existing network conditions, providing insights on existing constraints, inefficient territory assignments, and more. By analyzing transportation problems in the first mile logistics network, companies can define distribution routes, reduce fuel and labor costs, and improve delivery times. An Origin-Destination Matrix is used to determine global combinations for distance and time travel estimates, while a logistics optimization algorithm applies constraints to filter down to optimal routes. The Location Intelligence imperative is driving logistics planning, enabling scenarios such as removing fulfillment centers from the network due to outages or natural disasters, and finding optimal routes at certain times of day to avoid traffic and high toll prices.

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