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How Route Optimization Can Improve Estimated Time of Arrival (ETA) Accuracy

Learn how to predict estimated times of arrival with route optimization software for increased customer satisfaction and fleet efficiency

September 21, 2020

7 mins read

Waiting for a parcel to be delivered without knowing the estimated time of arrival (ETA) leaves customers in an uncertain situation, feeling shackled to the delivery destination. This struggle has become even more severe today, with the pandemic keeping many of us at home. Demand for delivery services is on the rise. But what about customer satisfaction?

According to Bizrate, 43% of customers leave positive feedback about delivery services when they get packages on the estimated date. At the same time, 65% highlight an estimated delivery time as an important criterion in choosing a delivery provider.

How Route Optimization Can Improve Estimated Time of Arrival (ETA) Accuracy
Source: Bizzrate Survey – Five consumer insights shaping logistics and delivery

Knowing the estimated time of arrival is important for everyone: logistics service providers, fleet managers, small delivery companies, big chains, warehouses, etc. Accurate and predictable ETAs lead to increased customer satisfaction. But to predict ETAs accurately you need to consider a lot of factors. One way to improve the accuracy of ETA calculations is with route optimization.

For predictable ETAs, you need to build optimal routes for customers, fleets, suppliers, and manufacturers: routes that are cost-efficient, flexible, and fast. Route optimization software plays a critical role in this.

Main factors to improve ETA calculations and predictions

  • Delivery routes — Building an optimal route that covers the needs of customers for faster delivery and offers efficiency for suppliers increases performance and customer satisfaction.
  • Flexibility of operations — Adapting to customers’ needs by partnering with several logistics providers for optimal delivery eliminates delays due to high loads on a single provider or inconvenient routes.
  • Transparency with customers — Hassle-free and direct communication with drivers gives transparency in relationships with customers.
  • Planning and scheduling — Keeping enough items in stock close to where demand is high makes delivery faster and more predictable.
  • Automated workflows — Eliminating manual work and paper forms increases performance and saves resources.
  • Transportation diversity — Using several means of transportation such as trucks, scooters, bicycles, and drones for last-mile delivery ensures fast and accurate service.

Why route planning and optimization software is so important for ETAs

Route optimization software can cope with much more than planning the way from point A to point B. Route planning optimization solutions based on advanced technologies like mapping, GPS tracking, artificial intelligence (AI), and data analytics allow companies to manage fleets, plan drivers’ loads, track assets, adjust routes to accommodate multiple stops, find charging stations for EVs, etc.

Apart from the usual route planning functionality incorporated within vehicle navigation systems, fleet owners can design custom truck routing optimization software. Custom routing software can build resilient and efficient maps with custom layers depending on fleet needs. Logistics companies and fleet managers already use GPS tracking to identify vehicle speeds. Today, truck route optimization software can also be used to alternate routes in order to send trucks in the optimal directions. Fleet route optimization software can avoid congestion, geofenced driving areas, tolls, and more.

Advanced features of route optimization system

  • Route optimization software can leverage maps and navigation to adapt routes in real time based on delays in delivery due to road incidents, weather changes, or construction.
  • Route scheduling software can give a day-ahead view of your routes and schedule to adjust routes at the last minute in response to driver availability, vehicle repairs or maintenance, the addition of new vehicles to the fleet, etc.
  • Truck scheduling software can automatically assign a delivery order to a route, a particular driver, and a required vehicle.

Route planning software for truck drivers can replay a vehicle’s route history for specific dates and road stretches to identify reasons for delays or incidents. This helps logistics providers and fleet managers identify how specific events, such as speeding or stops, impact ETAs, why these events occur, and most importantly, how to address them.

Fleet managers and logistics providers can identify KPIs and insights critical for the business using advanced truck routing. Companies can build comprehensive historical reports on fleet performance to find insightful data and prepare reliable forecasts.

One widely adapted technology for route planning is GPS tracking. But GPS is just one tool for predicting ETAs using maps. By applying more advanced technologies that use data from vehicle sensors, road infrastructure, and traffic monitoring systems along with crowdsourced data, you can align routes with ETAs considering many more factors along the way.

Learn how we applied GIS services to accelerate the compilation of maps for optimizing logistics routes

What to consider when developing route planning and optimization software

How Route Optimization Can Improve Estimated Time of Arrival (ETA) Accuracy

The place of machine learning and data in route optimization

A machine learning model applied to route scheduling software can calculate ETAs taking into account contextual data and the amount of time spent at each delivery stage. For training these models, data pre-processing and feature engineering are vital. Route scheduling and optimization software can rely on additional variables to train a machine learning model. These could be vehicle type, customer segment, time period, pickup and destination locations, and traffic speed at a particular hour. Contextual data on things like weather conditions, public holidays, and scheduled vehicle maintenance can also make ETA predictions more accurate.

The machine learning workflow for a route optimization engine is built on these steps:

  • Pre-processing data and engineering features
  • Training models
  • Evaluation
  • Deploying to production
  • Making predictions
  • Monitoring and optimization

Fleet Management Software Development Services

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Optimizing truck routes for accurate ETAs

When it comes to the accuracy of ETAs, route planning optimization makes a real difference. There are two major approaches to planning delivery routes with several additional routing scenarios that use the benefits of these two. The first approach is static routing, when the route is pre-planned and taken by a driver without any changes to the destination. The second is dynamic routing, when the driver can adjust the route based on factors influencing changes in the ETA.

Standard/static routing
How Route Optimization Can Improve Estimated Time of Arrival (ETA) Accuracy

Standard or static routing works best when you have a set of established routes for long-term customers who want exactly the same route each time you deliver their goods.

Pros

  • Be sure direct communication between the driver and customer won’t turn into crazy changes to destinations or delivery times and will provide the required transparency
  • Ensure consistency of service and alignment of customer expectations and delivered services
  • Analytics and predictions are much easier due to a lower quantity of different data

Cons

  • Limited optimization options, inefficient, costly
  • With addition of new customers, old routes may not work efficiently and a new strategy should be taken
  • ETA depends on road conditions and a lot of factors along the way; possible delays

Dynamic/on-demand routes
How Route Optimization Can Improve Estimated Time of Arrival (ETA) Accuracy

Emphasizing the customer, dynamic routing adjusts routes to specific requirements and changes that may appear just before sending cargo or even in real time during delivery. This method requires accurate data about customers, their preferences, and their order histories along with contextual data related to products. In this method, routes are built automatically considering all constrains, while customers can make changes on the way.

Pros

  • Optimal use of vehicle capacity
  • Smart sequence of stops along the way
  • Fewer miles on the road
  • Lower cost
  • Decreased delivery time
  • Can classify and divide customers by areas

Cons

  • Limited consistency, frequent changes, difficulties for drivers related to direct communication with customers
  • Chosen route can be new for a driver who is unfamiliar with the area
  • More resources needed for customer account management and analyzing customer preferences

Fragmented routes

This is a combination of static and dynamic routing. With fragmented routing, you build a standard route while adding new customers dynamically who are located along the way. You can add new points before, during, and in between planned deliveries.

Pros

  • Flexibility for drivers
  • Easy to add new points
  • Lower cost
  • Lower run time

Cons

  • Less effective and economical compared to a fully dynamic route

Preferred routing ID

Each route taken can be a base layer on which to add new destination points with accurate calculations of projected ETAs. Fleet managers can assign customers to already built routes.

Pros

  • Allows you to add more customers to an existing route for the lowest cost and least effort
  • Fewer miles and less time for planning
  • Cost-effective method with limited run time

Cons

  • Cost efficiency lower compared to dynamic routing
  • More resources for account management compared to standard method

Grouping areas

Geographical zoning of customers can combine the benefits of static template-based routing and dynamic routing. By grouping areas, drivers can operate within areas and cross boundaries in some cases to improve ETAs.

Pros

  • Drivers are familiar with areas but still flexible to cross their boundaries
  • Similar characteristic and demand for products can be recognized and covered

Cons

  • Dependance on density of customers in the area
  • Artificial boundaries can raise conflicts between drivers

Conclusion

The modern world of fleet management and supply chains lives in real time. Logistics providers should be responsive to customers’ nonstop needs. Market leaders are recognized for their ability to adopt always-on tech solutions and customer-centered business models. By applying advanced technologies that enable continuous route optimization, logistics and fleet companies can offer on-time delivery with predictable ETAs. This improves customer service and reduces costs. At the end of the day, customers enjoy their ordered goods, retailers implement marketing strategies with full stocks, and fleet owners count the savings brought by route optimization software.


If this story resonates with you, don’t hesitate to contact Intellias, an engineering partner who can help you implement a technology strategy with route optimization software for fleets and logistics operations.

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