Delivery concept. Cardboard boxes on a conveyor line. 3d illustration

Last-mile delivery planning- How to leverage data?

Last-mile delivery – the final leg of getting items from warehouses to customer doorsteps – is rapidly becoming a competitive differentiator for retailers and e-commerce companies. Fulfilling last-mile requests is becoming increasingly important as consumer expectations rise. However, efficiently coordinating last-mile operations is complex with many moving parts – routes, drivers, fleet, inventory, and more needing alignment.

Leverage historical data for accurate demand forecasting

Understanding demand trends by analyzing past order volumes across regions, lead times, and customer delivery patterns is a useful tool. When do order surges occur? Which areas have higher demand density? What delivery windows do customers prefer? Forecasting future demand is aided by historical data, which reveals behavioral insights. With advanced analytics tools, demand forecast models can be created that factor in seasonal peaks, promotions, new product launches, and macro trends. With predictive demand intelligence, logistics managers optimize daily/weekly routes, fleet sizes, driver schedules, and inventory planning. Fulfillment is aligned with expected order loads.

Optimizing supply chain strategy is crucial to on-time customer satisfaction. Using traffic data, one can optimize routes and reduce travel delays in real-time and in the past. Data from telematics can also provide insight into actual versus planned routes for improving routing continuously.  Geospatial analytics allows grouping delivery addresses into proximity clusters – simplifying route planning. All pieces of spatial data are synthesized to chart smarter routes and map new delivery territories or warehouses.

Gain visibility into realistic driver work cycles

Sensors and IoT connectivity in fleet vehicles provide rich telematics data on driver patterns – location, mileage, speed, acceleration, braking, etc. A clear picture of driver workloads and work cycles is formed based on this data. Drivers’ workload is measured using various metrics, such as deliveries per hour, average stop times, fuel consumption, and driving behaviors. Logistics managers assign optimal routes and volumes based on data intelligence to driver productivity. Getting real-time visibility into last-mile fulfillment is enabled by leveraging GPS, sensors, and proof of delivery data. Logistics teams view live shipment statuses via analytics dashboards and get alerts for delays to proactively resolve issues. Apps also enable real-time communication between dispatchers and drivers – guiding them to mitigate obstacles as they occur. With real-time data, route schedules, and driver assignments dynamically optimized as the day progresses. Managers have the visibility to make smarter coordinating decisions on-the-fly.

Measure last-mile performance with data-driven KPIs

Data streams from last-mile operations are synthesized into quantifiable KPIs measuring critical success parameters – cost per delivery, route efficiency, real-time ETAs, driver output, customer satisfaction, etc. Data analytics provides logistics managers with fact-based insights into performance. By benchmarking metrics against targets, weaknesses in last-mile delivery are identified and addressed, such as unrealistic routes, low driver productivity, or excess fuel consumption. Data is indispensable for continuous optimization.