Big Data in Logistics: Use Cases and Implementation

The logistics industry is evolving in response to dynamic business trends and customer preferences. The workflow becomes more complex every year, resulting in process bottlenecks and extra costs. To keep logistics companies afloat and optimize their delivery, business owners have to consider innovations, and data science is one of them.

According to Grand View Research, the US supply chain analytics market is expected to grow at a 13.3% CAGR between 2022 and 2030. At the same time, Allied Market Research projects the global supply chain analytics market to reach 16.82 billion USD in 2027 compared to 4.53 billion USD in 2019.

In 2022, big data in logistics and supply chain management is by no means one of the best solutions for multiple business issues. We will explain why.

Top 6 Data Science Applications in Logistics

Logistics companies have to handle vast data sets. Shipment origin and destination points, weight, size, and contents — these are only a small portion of the processed information. Companies also consider vehicle characteristics, fuel consumption, weather reports, traffic, and many other factors.

Let us not forget about transparency, strict schedules, and increasing customer expectations impacting transportation businesses. It indeed looks overwhelming. But it’s going to change. There use cases will give you an idea of how to harness possible complications with a big data solution.

1. Route optimization

Route optimization in logistics implies trying out several scenarios to pick the most cost-effective route. This process may result in minor management flaws which eventually lead to major business issues. Why is it happening?

To find the best route, one should consider numerous factors, like weather, traffic, or road conditions. No wonder managers sometimes make errors in their assumptions. But big data can remove or at least considerably reduce business risks by taking into account all necessary aspects and coming to an accurate conclusion. Your task is only to provide big data tools with essential information.

2. Transportation tracking

Big data analytics in logistics has brought transportation tracking to a whole new level. Companies and individual consumers can track their parcels in real time, getting automatic updates via email or app notifications.

Data from GPS-powered devices, bar codes, and advanced RFID (Radio Frequency Identification) tags enables big data solutions to track every package, plan delivery dates, and notify company managers and receivers if something does not go as planned.

The latest IoT tracking tools help transportation companies keep a close eye on the temperature, humidity, and other conditions in the fleet. Data from sensors is particularly important when transporting perishable goods.

3. Optimization of last-mile delivery

Even though it is the last mile, it brings a lot of trouble to logistics businesses. Let us say, a big truck cannot park in an urban area. It means a courier has to walk some distance to deliver a package. And what if this package is heavy?

In such cases, workers have to spend extra time and effort to accomplish their tasks. It is only one real-life example out of many, which often get overlooked.

Implementing data science in logistics can dramatically change this and a lot of similar situations. Just by using information from GPS devices, big data software can track every step of the courier (literally) and identify delivery trends, regardless of the miles traveled. Company managers, in their turn, make data-driven decisions to improve internal processes and service quality.

4. Innovative warehouse management

With every innovation in online shopping, customers become more demanding. They want to know exactly what goods are available in stock and get them as soon as possible. Traditional warehouse management sometimes can’t handle increased workloads. Plus, some errors may emerge due to the human factor.

Big data solutions significantly improve existing logistics operations. With their help, company managers always have easy access to updated data about goods in stock, their locations, expiration dates, and other information. They won’t miss a thing and, hence, can satisfy demanding buyers and avoid overstocking.

5. Verifying address information

Delivering packages to wrong or even nonexistent addresses is another logistics pain point. Company clients are humans who naturally can make mistakes in indicating the delivery address. How can you solve such an issue?

Verifying every destination point by company employees is not an option. It eventually can lead to even more errors. What you should consider instead is using software tools.

Ideally, address records should be standardized and then validated. Standardization is the process of correcting records, while verification means checking if the address exists. You can do all that with a powerful big data solution at hand. It will help you decrease the human factor impact on your processes to a bare minimum.

6. Predictive maintenance

Data science in logistics opens the door to advanced analytics insights previously inaccessible to managers and decision-makers. From now on, they can monitor driver habits that directly impact fleet wear and spot damaging trends.

Company employees can also track the car’s condition and plan maintenance in advance by collecting data from fleet sensors. Predictive analytics provided by big data tools is the key to fleet durability and reasonable use of business resources.

How to Implement Big Data Analytics in Logistics and Supply Chain Management

When it comes to big data implementation, there is no one-size-fits-all approach. Every organization has a unique software ecosystem, goals to achieve, workflow issues, etc. However, some development stages do not change much. Here they are.

1. Studying & Preparing

Before getting to planning, a development team studies individual business characteristics, assesses the feasibility of the future solution, and calculates probable development costs.

2. Planning

At this stage, developers and their clients go into detail, outlining specific software requirements. Taking some time for thorough planning at the beginning will save you from numerous risks, including overdue deadlines and extra costs.

3. Architecture Building

Big data solutions require extra effort at the stage of architecture building. These are the steps to take before coding starts.

4. Development & Testing

The development part begins. Typically, big data solutions for logistics have complex logic and cover several working processes. These are some of the most common software features to consider:

● Real-time tracking of fleets, goods, and couriers,

● Automatic invoicing,

● Comprehensive reporting,

● Data-based analytics, etc.

Needless to say, quality assurance and testing should take place at every development stage to make sure the system works as expected.

5. Deployment

Software deployment is the final step to make sure your software runs properly on specified devices and can be scaled in the future.

Note that big data solutions grow with your business. The more data comes to your organization, the more capabilities you will need for efficient data management. This is why deployment plays a fundamental role in the entire development process.

Keep reading to find out what business benefits big data brings to your transportation company and how big data can be implemented in practice.