How do you know that your drivers and field workers are using the most efficient delivery routes? Minutes shaved off of a route can become a competitive advantage, so route optimization is the key to balancing high delivery volume with low delivery costs. Every route is an exercise in trade-offs among variables like driver availability, delivery volume, load/unload time, fulfillment promises, vehicle size, road conditions, and traffic. It’s not a set-and-forget calculation, because most of the variables change from one day to the next. Small companies can optimize routes manually, but as soon as they have more than a few drivers, it becomes apparent that the process doesn’t scale well.
When your company outgrows the use of spreadsheets for manually creating records, blending data sets, and consolidating reports, it’s time to use route optimization models based on geospatial analysis. Drive-time optimization workflows take pre-built packages (or the APIs offered by map providers) to transform data on latitude and longitude into spatial objects for both source and destination. Parsing tools extract relevant data points, then spatial tools build a turn-by-turn route to the destination. The APIs estimate distance and time to complete the trip. By combining external data on drive time with internal data on deliveries and fulfillment, you can allocate workloads to drivers according to capacity, seniority, and geography for more efficient logistics.
With Alteryx, you can:
- Automatically connect to multiple sources of geolocation and spatial data using APIs like the MapQuest API
- Download the Spatial Analytics Starter Kit and use tools like the ‘Create Points’ tool from the Alteryx Spatial tools to transform longitude and latitude into spatial objects
- and use tools like the ‘Create Points’ tool from the Alteryx Spatial tools to transform longitude and latitude into spatial objects