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Big data is no longer a buzzword; instead, it has become a valuable and powerful tool for companies to improve their processes and add visibility to them. The logistics industry is no exception, and for the period between 2024 and 2032, the market size of big data in logistics is expected to register a CAGR of more than 21.5%. These numbers tell us that more and more businesses in logistics are recognizing the importance of big data and are actively using it to become more competitive, sustainable, and client-focused.
In this article, we discuss the role of big data in logistics, the core steps of its implementation process, and what real-life use cases already exist in the industry.
What is big data in logistics?
Before diving into the specifics of big data in transportation and logistics, let’s first recap what it actually means and what makes it different from the regular data. Big data refers to massive amounts of both structured and unstructured information. This information is usually collected from various sources and thus comes in various formats. The main difference between big data and regular data is the size of the dataset: for big data, regular processing tools are simply not enough.
Several important characteristics define big data:
- Volume: the size of the datasets that we’ve already discussed;
- Variety: comes in both structured and unstructured formats;
- Velocity: implies real-time information generation and processing at high speed;
- Veracity: should be kept relevant and accurate;
- Value: the main purpose is to bring valuable insights to users.
Now that we’ve defined big data technology, let’s explore how it is generated in logistics. The most common sources include RFID tags, GPS devices, IoT sensors, and, obviously, financial transactions from customers. Basically, data in logistics is generated continuously at every moment when an item is stored, transported, or processed. If a business owner knows how to properly collect and analyze this data, he can enjoy an array of benefits that big data brings.
Key benefits of business big data for the logistics industry
So why should businesses in logistics pay attention to their data and set up a specialized infrastructure for its collection and processing? Here are the key advantages:
Increased transparency
With the help of big data and real-time monitoring of transported goods and inventory, business owners can always know the state and location of their assets as well as immediately learn about any issues and emergencies. This can help create a more proactive strategy where companies are able to prevent common challenges such as transportation delays or inventory overstocking.
Improved efficiency
The use of collected information can help improve a variety of processes, such as route optimization, inventory management, or risk management. In this way, the use of big data greatly improves the efficiency of your logistics business due to accurate asset distribution, automation of tasks, and preventative maintenance.
Demand forecasting
Big data is the core of predictive analytics, which is often powered by machine learning technology. The main goal of predictive analytics is to create accurate forecasts about potential future demand and customer behavior. This is possible due to the comprehensive analysis of massive sets of historical data. Hence, the more information you have at your disposal, the more accurate the forecast will be.
Route optimization
One of the biggest challenges in the logistics industry is optimizing routes. While some believe that the shortest route is the best, the safety of routes should actually be prioritized, especially when transporting fragile goods. By using big data, companies can always know the state and conditions of both the road and the vehicle, analyze which routes are the most efficient ones, and plan the logistics accordingly.
Reduced costs
Big data in logistics helps companies greatly reduce their operational costs in several ways. First, companies can distribute assets according to demand, thus eliminating the need to rely on guesswork. Second, companies can better manage their inventory and avoid surplus or understock, which also impacts costs. Lastly, big data enables companies to better plan and organize their processes and select the most fitting solutions.
How to implement big data in logistics: a step-by-step guide
As you can see, big data plays a significant role in the modern logistics industry, but some companies still hesitate to adopt it. One of the main reasons behind this hesitation is the misconception about the complexity of the implementation process. Below, we break down the process into clear steps so you can understand how it works and what has to be done at each stage:
Identify and define key objectives
Every new solution that you introduce to your business should serve a specific purpose and the same applies to big data. You cannot just start collecting and processing it – you need to have a clear understanding of the exact issue / challenge this data will be solving. Examples include:
- Route optimization
- Cost reduction
- Delivery time reduction
- Improved customer experience
Based on the goals, you will plan your big data strategy and will also set KPIs for monitoring and measurement.
Data collection and integration
Once you’ve defined your business big data goals, you can start collecting the data to create a rich dataset. As already mentioned, there are various sources of information in the logistics industry:
- GPS and telematics for vehicle tracking in real time;
- IoT sensors for monitoring the transportation conditions;
- Warehouse Management Systems (WMS) and CRM systems;
- External sources such as weather forecasting or reports on traffic.
Note that you need to have a data storage solution ready before starting the information collection process. We recommend cloud storage such as Amazon S3 or Google Cloud Storage due to its scalability and efficiency. We also recommend consulting with an expert IT provider who will help you set up and configure your cloud solution properly.
Select the right analytics tools
We’ve already mentioned that big data requires specialized analytics tools so the next step is to select the right ones. Some of the most popular tools for that include Apache Spark, Hadoop, and Snowflake. Let’s review the most well-known one, Apache Spark, for your better understanding of what such tools offer.
As the official website says, Apache Spark is a unified engine for large-scale data analytics. What it means is that the tool is suitable for working with big data in terms of data engineering, machine learning, data science, and analytics. This is very convenient since you can manage all your data-related projects from a single platform. Apache Spark supports Python, SQL, Scala, Java, and R, and provides an array of libraries and frameworks for convenient work with big data.
Implement data-driven strategies and operations
With the implementation of big data, you will naturally have to review your current business strategy and operations. In simple terms, you will use big data as a base for certain processes, such as route optimization, dynamic pricing, or predictive maintenance. This will allow you to optimize your current processes, replace outdated or inefficient ones, and add visibility to your supply chain.
Train the team
With the introduction of new processes, you will also need to provide corresponding training to your team. This includes training in data analytics and new tools as well as the comprehensive training in big data as a whole. One more important thing to remember is to encourage and promote data-driven decision making. Before the implementation of big data, many processes in your business were most likely based on guesswork and intuition, which naturally led to poor outcomes. Hence, it is important to change the way of thinking in your organization and gradually lead employees to rely on the data in the first place.
Ensure security and compliance
For any company working with the sensitive data, security and compliance should be top priorities. Even the smallest data breach can lead to massive financial consequences and loss of trust from the customers’ side, so companies should pay extra attention to the ways they secure their information. Some of the most common security practices include encryption, well-established access control, and use of firewalls to prevent external attacks.
Companies should also make sure that the way they store and process the information comply with the needed regulations. You might have heard of GDPR (General Data Protection Regulation) or HIPAA (Health Insurance Portability and Accountability Act), which are among the most well-known industry regulations regarding the protection of sensitive data and its management. Compliance with these regulations means that your organization understands the importance of data security and follows security best practices. If you don’t know where to start, the official websites of these regulations usually contain information and even frameworks on reorganizing your processes in order to achieve compliance.
Monitor and optimize your strategies
Working with big data is a dynamic process so you will have to continuously monitor your processes and optimize them, if necessary. Based on the feedback from stakeholders and the KPI metrics that you’ve defined before, you will be able to understand how effective your newly implemented processes are and what areas can be improved. You might also need to adjust your algorithms and systems based on their performance and business needs.
Real-world examples of big data use cases in logistics
To wrap up, let’s look at the most interesting real-world examples of big data use cases in logistics and the ways it benefits the companies.
Route optimization
Example: ORION by UPS
Optimization of routes in logistics is highly important as it allows to reduce fuel consumption, speed up the delivery time, and even improve the security of the delivery. UPS (United Parcel Service), one of the biggest supply chain companies in the world, has invested a significant amount of time and resources into developing ORION – an On-Road Integrated Optimization and Navigation system.
ORION is a delivery platform route optimization software. It uses the company’s historical data and collects a variety of information in real time, thus helping UPS significantly cut down on operational costs and efficiently organize multi-driver and multi-stop routes through data-driven planning.
Predictive maintenance
Example: Smartification by DHL
The world’s leading logistics company DHL has implemented big data in their process many years ago, thus tapping into predictive maintenance, smart facility monitoring, and asset tracking. Let’s take a closer look at the way DHL manages its maintenance processes.
According to the information on the official website, the company uses noise sensors for predictive maintenance of sorters – machines used for automated sortation processes. DHL also uses IoT sensors for vehicle monitoring and analyzes the information in real time, thus being able to prevent the majority of maintenance-related issues and cut maintenance costs.
Inventory management
Example: Inventory forecasting by Amazon
Amazon offers business owners an innovative tool called an Inventory Manager to automate the process of inventory management by using historical big data and advanced analytical tools. With the help of the Inventory Manager, companies can not only predict their inventory needs but also prevent the risk of the understock or surplus. In this way, the tool solves one of the biggest challenges in the logistics industry aka correct management of assets and avoidance of under- or over-stocking.
Customer experience
Example: Analysis of customer feedback by FedEx
Constant improvement of services helps businesses grow and retain customers in a highly competitive environment, and FedEx knows that perfectly. The company uses big data to analyze customer feedback and, based on that, identify ways to improve its services.
Customer experience is often overlooked when it comes to the use of big data. While companies focus on reducing costs and optimizing processes, it is also important to perform thorough and detailed research and analysis of customers. This will help not only understand what works best and why but also tailor future services and products to predicted customer requests. And don’t forget that you can also use big data to forecast the approximate delivery time and notify users in case of a delay.
Real-time asset tracking
Example: IoT monitoring by DHL
One more example of companies using big data in logistics is real-time tracking of transported goods. DHL uses a variety of IoT devices to monitor its shipments and always know their statuses and location. This allows for improved transportation processes, safer transportation conditions, and improved customer service.
Final word
Big data in logistics is a powerful asset that grants companies a significant advantage over the competition and helps them understand how to make existing processes and services more efficient and customer-centric. However, you need to keep in mind that in order to benefit from the use of big data, it is important to implement specialized analytical tools. We recommend partnering with a reliable IT vendor like SoftTeco, which has experience providing various big data services, from consulting to implementation and support. In this way, you will get access to unparalleled expertise and knowledge as well as to an international talent pool. Contact us for more information and we will gladly discuss your project.
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