Unlock the potential of your Data: Getting started with Big Data Analytics in the last mile

By 2027, the global parcel shipping volume is expected to reach 256 billion parcels. Consequently, while managing a massive flow of goods, last-mile parcel delivery companies also create extensive data sets. 

These companies must track the origin, destination, size, weight, and current location of the billions of parcels delivered daily. But just monitoring the endless amount of data does not create value. 

Big Data Analytics refers to analyzing large quantities of data to gain valuable insights for optimizing business processes or creating new business opportunities. As the last-mile industry, like any logistics branch, heavily relies on information flows, applying Big Data techniques can help tap into the unutilized potential. 

However, often the question is where and how to start. 

To help parcel delivery companies navigate the journey to becoming data-driven organizations, we simplify Big Data Analytics, explain its benefits, and provide the necessary steps to getting started. 

The importance of being data-driven in the last mile

Often decisions are based solely on a gut feeling or the experience of last-mile professionals. 

An example of this is developing an out-of-home delivery network with the “spray and pray” method - choosing multiple random locations to create a dense network and hoping it works. 

It’s also not uncommon to use data analytics to confirm already made decisions. All of the above usually leads to unnecessary costs and revenue or market share loss. 

To avoid the adverse effects and stay afloat in the dynamic logistics environment, parcel delivery companies must employ a data-driven attitude. Adopting a forward-looking approach to data analysis helps companies gain new insights and a better understanding of the business. 

But to embark on the data analytics journey, you need to have data. Delivery companies must first identify valuable types of data and collect them.

Data sources

The type of tools used to capture data and their sophistication can vary depending on the volume of deliveries a company handles. Common data sources include:

• Warehouse management systems (WMS) 
• Transport management systems (TMS)
• Fleet management software
• GPS tracking systems
• PDA
• Mobile Apps

These tools capture different types of data, which can be historical, real-time, or geographic. They’re related to shipments, vehicles, participating entities, etc. For example: 

• Vehicle location, corresponding timestamps, and mileage 
• Shipment ID, content type, and sender/receiver address
• Event logs

If the company has a PUDO or parcel locker network, the accompanied software is also a valuable data source. Apart from this, third-party data sources can provide traffic, weather, demographic and movement data, as well as locations of points of interest (POI).

Unfortunately, data collection alone isn’t enough to turn a data-rich organization into a data-driven one.

Data health

Most data is unstructured and needs to be refined to attain the necessary quality for further analysis. 

First, the data must undergo an audit. Good data health means the organization’s data is complete, valid, easily discoverable, and high quality. This is a prerequisite for transforming it into business value.

Weak inputs are sometimes easy to spot—for example, inconsistencies such as different units or periodically missing data. For large volumes of data, examining every single record can be daunting. A good approach is using a platform that provides automated data audits or has both integration and governance abilities.

Once you’re confident in the quality of your data, you can begin the analysis and start learning new things about your business. The question in this phase is what are you trying to improve, or what type of value are you expecting from applying Big Data? 

Use cases of Big Data Analytics in the last-mile

Increasing operational efficiency is one of the main reasons for applying Big Data Analytics in the last-mile environment. Last-mile delivery businesses also benefit from analytics by identifying and confirming new services or models for additional revenue streams. 

Focusing on the two previous reasons, we’ve compiled a list of use cases present in the last-mile industry. Additional examples of applying analytics to revolutionize last-mile delivery processes are available in our free and ungated white paper.

Improving courier performance

Couriers play a crucial role in the final stage of getting packages to recipients. To understand their performance and identify areas for improvement, transparency is key.  Analytics assist in highlighting issues in real-time and recognizing patterns in historical data. Ultimately, applying analytics leads to helping couriers achieve more stops per route, minimizing idle delivery times, and evenly spreading workload amongst couriers.

Creating better delivery plans

A delivery plan is crucial to delivering items to recipients on time or within the promised delivery windows.  Both historical and real-time data help create solid delivery plans. 

An example of how data can be used is by combining shipment and location information to determine the most efficient route. Enriching the route with terrain information (such as whether it’s a rural or urban area), types of delivery vehicles and modes, and previous courier performance is needed to create tailored plans and provide accurate estimated times of arrival (ETA).

A step further is rapidly processing real-time data to allow a continuous adaptation of the delivery sequence based on geographical and environmental factors as well as the recipient status.

Strategic delivery network planning 

The capacity of the delivery network should be adapted according to anticipated future demand, emerging trends, and historical capacity. Accurate predictions increase efficiency and lower risk for storage and fleet capacity investments. 

Analyzing delivery routes, times, areas, and regions can show inefficiencies in hub placement. For example, a courier driving almost an hour daily from the hub to the first stop creates unnecessary mileage and can cause delays. A solution could be placing micro hubs closer to the delivery area. 

Introducing out-of-home delivery broadens the delivery network with PUDO points and parcel lockers. Location is one of the most critical factors for their efficient utilization and profitability. Analytics help companies scout and choose ideal locations for OOH points.

Whatever the desired goal is, you must have the right technology to explore information in a higher order of detail and speed.

Choosing the right analytics solution

Analytics software varies in type, depth of reporting, and visualization capabilities. The choice of software can depend on the volume of deliveries, generated data, and business needs. When deciding on an analytics tool, try to answer the following questions:

1. What are you trying to optimize? 

Commonly used analytics tools usually only solve one particular problem. 

2. What is the learning curve?

Who needs to have access to the tool or analysis results? Some tools are complex and require experience, education, or expertise. They can be costly and time-consuming, especially if additional support is needed.

3. Can it easily integrate with other tools?

Efficient gathering and analysis of relevant data are ensured through easy integration with various sources. However, if the integration process is manual or complex and time-consuming, it can delay implementation and hinder the ability to quickly leverage data analytics benefits. 

4. What are the visualization capabilities? 

Although charts and tables can be helpful, having spatial context in logistics is essential. Interactive maps, heat maps, and other types of geospatial data visualization can be vital to uncovering trends and outliers.

5. Can it handle large quantities of data?

The speed of comprehending data to act timely is critical. Therefore, the software used to analyze or forecast has to handle large volumes of data without lagging. Having up-to-date data is very important. Needing to upload updated datasets can become overwhelming and slow things down. 

In summary, the increased visibility and strategic decision-making enabled by Big Data Analytics can improve last-mile delivery in a number of ways. Industry experts predict that its use will only continue to grow during the following years and will soon become the standard.  

Still unsure how to get started?

Book a demo to explore how we assist you throughout your data-driven journey and how Mily Tech’s delivery analytics platform can help you master the last mile.