Predictive Delivery: Know How it can Optimize the Last-mile Delivery Process

Business success depends majorly on two factors: product quality and shipping quality. In order to increase revenue and brand loyalty, it is essential to retain customers, which you can achieve by delivering high-quality products as soon as possible. 

Getting it right entails gaining the customer’s trust and respect. However, making a mistake here could be disastrous for the business. But how can you ensure that your business makes deliveries on time?

Predictive delivery can help you ensure that your business makes timely deliveries. Companies across the supply chain industry are beginning to recognize the value of investing in predictive delivery.

And it’s not surprising, given that this cloud-based system allows businesses to review real-time information, reports, and other useful data.

What is Predictive Delivery?

Predictive delivery is a method that uses analytics to reduce the delivery time and improves the estimate of the time required to make the actual delivery. This method uses historical data as a reference and uses statistical techniques to forecast the delivery time.

How does it work?

Predictive delivery predicts future outcomes by combining historical data with statistical modeling, prediction models, big data, and machine learning algorithms. These include linear and non-linear regression, decision trees, and support vector machines.

Data scientists use machine learning and deep learning algorithms to find patterns and predict future events in order to gain insights from this data. Learnings from predictive analytics can then be applied to prescriptive analytics to drive actions based on predictive insights, which can help you in boosting the last-mile delivery process.

What is the Importance of Predictive Delivery?

Customers can easily choose another company in online shopping if the requested item is out of stock or shipping will take too long. An eCommerce company’s goal is to anticipate its customer’s needs to show them that they have the goods and can ship them quickly.

eCommerce businesses can use predictive capabilities to develop actionable insights, improve operations, and increase sales along with customer satisfaction. This gives eCommerce businesses a competitive advantage. 

Here’s how predictive delivery can help eCommerce businesses improve their operations by utilizing advanced analytics:

  • Accurately forecasting delivery times and communicating delivery statuses promptly and efficiently.
  • Lowering the number of “attempted” deliveries, optimizing transportation and delivery costs, including the carbon footprint.
  • Forecasting demand and managing resources to ensure that the delivery network operates at full capacity.
  • Better accuracy and communication will improve the user experience and consumer product offering.
  • Quickly responding to unplanned changes and challenges.

How Does Predictive Analytics Play a Vital Role in Boosting Last-mile Logistics?

If you don’t use predictive analytics for your business, you are putting it at a competitive disadvantage. You need to use the technology to optimize and improve your last-mile logistics. Let’s look at key areas where predictive analytics solutions can help optimize the last-mile delivery process.

1. Scheduling deliveries and route optimization

The first and most important application of predictive delivery is a route optimization and delivery schedule. This entails using artificial intelligence to plan optimal routes for each shipment, considering traffic conditions, transit times, distribution center location, dropoff time slots, car capacity, weather, and other valuable data points. 

The goal for each courier is to complete as many successful deliveries as possible while running as few kilometers as possible to reduce the cost and time of deliveries.

Predictive delivery is an excellent option for the route optimization problem because it can consider a large number of factors and constraints simultaneously and find the best route for each shipment in real time. It can assist couriers in better adjusting their routes and avoiding congested areas, resulting in the time and cost-effectiveness of last-mile delivery operations. 

This is a significant improvement over traditional optimization methods and basic route optimization software, which are frequently incapable of considering all constraints simultaneously, as well as not taking into consideration real-time changes such as traffic conditions.

2. Predicting delivery times

Predicting delivery times is another critical use case for predictive delivery in parcel shipping optimization. It is essential information for customers and businesses because it allows them to plan their days and ensure that someone is available to receive the package. It also assists businesses in tracking their shipments and ensuring they are delivered on time.

Shipment tracking and tracing are one thing; accurate time windows for parcel dropoff are another, and both are required to meet rising customer expectations. Customers nowadays expect transparent last-mile visibility of their packages. However, when they order time-sensitive goods such as groceries or frozen foods, they need an even greater accurate delivery time prediction.

To provide accurate predictions, predictive delivery analysis considers various factors, such as designated driver schedules, delivery distance, the number of required depot visits, traffic conditions, and so on. This data can be gathered from GPS data, weather forecasts, social media data, and other sources to provide even more accurate information to end users and increase visibility with updated ETA notifications that drive customer satisfaction.

3. Predicting detailed deliveries

When we discuss predicting delivery times, it’s important to remember that we assume someone will be at the delivery address during specific time windows to receive the parcel. But what if they aren’t at home or the business has already closed? Failed deliveries add significantly to the overall cost of the process, not only because a second attempt is required but also because undelivered parcels must be returned to the depot and, eventually, to the shipper.

This is where predictive delivery can help by anticipating failed deliveries. Models can consider various factors, such as the customer’s address, the type of delivery, historical data on parcel deliveries, and so on. This data can be used to forecast whether a specific delivery will fail and to adjust delivery schedules during delivery planning.

4. Dynamic pricing for last-mile delivery

It is common for the end user to pay nothing or very little for delivery. This presents a significant challenge for delivery companies and shippers because their operational costs are significantly higher.

Traditionally, parcel freight rates are either static or calculated and are contracted to the shipper based on the parcel’s size, weight, origin, and destination. However, as we all know, more factors are needed to provide a complete picture of how much it costs to deliver a specific parcel to a specific customer.

The predictive delivery model can help in this situation by dynamically pricing each delivery based on various factors such as distance, size, supply and demand, and so on. This data can be used to calculate a real-time delivery price that reflects the delivery cost. 

Dynamic pricing occurs when the price of a product or service changes in real time in response to demand. In logistics and shipping, dynamic pricing models are widely used. This approach allows transportation companies to evaluate their pricing strategy and make it more efficient with a higher profit margin.

On the other hand, large shippers can use this data to better understand their delivery costs and carrier performance, forecast shipping budgets, and so on.

Improve Your Predictive Delivery Planning by Upgrading to Upper Route Planner

Last-mile delivery optimization could seem difficult as it requires a comprehensive approach to automation and data analytics. It involves integrating with all other logistics systems to provide end-to-end shipment visibility and, in many cases, real-time data processing. 

However, predictive delivery can be complex if you do not have the right team with you. Even for people who have done this for years, it is time-consuming and prone to errors. But don’t worry if you are a new delivery business owner facing the same issues. We have got you covered. 

We at Upper have been successful in assisting companies in finding solutions to their last-mile delivery. Upper’s features serve as a one-stop shop for your business requirements. Hence, Upper would be an ideal companion for delivery company owners.

Route planning

Upper software includes a powerful route-optimization algorithm that assists in creating efficient routes for all of your deliveries. It considers the number of required stops and designs the route to complete the deliveries in the shortest amount of time. This will allow you to complete more deliveries in less time and reduce the required time.

Route optimization

Cutting operational costs is critical for any business to remain profitable. Upper assists you in doing the same. Upper’s optimized routes can help you save up to 40% on fuel costs. It also ensures drivers reach their destination in a minimum time.

One-click dispatch

Upper includes a one-click dispatch feature that allows you to send route plans to your drivers. For example, if there is an issue with the driver, you can reassign the route to another driver to ensure that deliveries are completed without delay.

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FAQs

The benefits of predictive analytics are:

  • Helps to gain a competitive advantage
  • Finds new revenue opportunities
  • It improves fraud detection
  • Optimizes processes and performance
  • It helps in route optimization
  • It can accurately predict fluctuations

Customers’ demand keeps fluctuating depending on the price and seasonality. The predictive model can bridge the gap between your insights and accurate forecasting by building demand forecast models with historical data. To better predict changes in demand, it uses statistical models, and you can haggle with suppliers and distributors as necessary.

Some of the challenges are:

  • Drivers occasionally have to make long trips for a single delivery
  • Many businesses and residential addresses can be found in city centers with heavy traffic
  • Predicting the best time to deliver a parcel to a specific address while minimizing failed deliveries

The goal of any business is to please its customers and increase sales. In the case of parcel delivery, this means delivering parcels on time and in good condition. Recent advances in machine learning fields and the growing popularity of the deep neural networks approach enable carriers and shippers to develop predictive models capable of forecasting various parcel delivery events.

Last-mile delivery costs are high as a percentage of total shipping costs, accounting for 53% overall. It may cost around $10 per delivery.

Wrapping Up: Future of Predictive Delivery

Last-mile delivery optimization requires an all-encompassing approach to predictive delivery or analytics. Predictive delivery entails integrating with all other logistics systems to provide end-to-end shipment visibility and, in many cases, real-time data processing.

All businesses can benefit from predictive analytics. However, implementing predictive algorithms can be overwhelming if you don’t have the right knowledge and a team that thoroughly understands your business. 

if you want to optimize your last-mile delivery operation, you need a route planner like Upper. It uses predictive delivery algorithms that can help you in churning out the fastest routes. Try a 7-day free trial now.

Author Bio
Rakesh Patel
Rakesh Patel

Rakesh Patel is the founder and CEO of Upper Route Planner. A subject matter expert in building simple solutions for day-to-day problems, Rakesh has been involved in technology for 30+ years. Looking to help delivery businesses eliminate on-field delivery challenges, Rakesh started Upper Route Planner with the ultimate goal of simplistic operations in mind.

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