The importance of data-driven internal logistics

As companies face increasing demands for efficiency, speed and accuracy, data has become a critical tool for making strategic decisions and optimizing operations throughout the supply chain. In this post we analyze the value of data in the internal logistics of the industry. In addition, we explore the origin of data flow and the key technologies to leverage it.

Why is data-driven internal logistics important?

Gone are the days of traditional formulas based on instincts or estimates. As companies move towards greater automation and digitization of their operations, leveraging big data enables more accurate decision-making based on facts and analytics. It is the way to generate the knowledge that is indispensable for:

Identify business opportunities.
Detect areas for improvement.
Optimize resources.
Adopt a strategic vision.
Improve productivity.
Customer satisfaction.
Increase sales.

Optimizing operational efficiency

The data provides a detailed view of how operations are performed throughout the supply chain. This makes it easier to identify bottlenecks, process inefficiencies and opportunities for improvement. In addition, when the company has a constant flow of data on operations, in real time, it becomes possible to dynamically adjust tasks and redesign the layout of processes, products, warehouses, etc. This ensures that materials and goods are moved more efficiently, faster and almost error-free.

Continuous improvement

The use of data not only allows for the optimization of existing processes, but also provides constant feedback that enables companies to create a cycle of continuous improvement. When detailed and accurate information (not estimates) is used to implement adjustments or changes in operations, continuous testing and re-evaluation can be carried out to measure effectiveness and achieve higher and higher levels of performance.

Cost reduction

Data gives leaders the ability to make informed decisions in real time. This can have a direct impact on cost reduction. For example,

Performance data from automated machines, whether in the production process, order picking or packaging, helps to identify signs of wear or failure before a serious breakdown occurs. This allows predictive maintenance to be planned, reducing downtime and costs for emergency repairs.

Where does the data flow in intralogistics come from?

Intralogistics processes involve the transformation and constant movement of materials and products, from receipt until they are ready to be used in manufacturing, or to be sorted, stored and shipped. These internal processes are highly dynamic and increasingly complex, and are constantly generating data. Data that reflect operating conditions and are converted into valuable information to identify patterns and evaluate the performance of each phase. However, these data can only be useful if they are quality data:

What is quality data?

It is not just a matter of collecting large volumes of information, it is also necessary to ensure that the data is accurate, relevant and accessible in a timely manner. Therefore, the data must:

Validated and verified before use to ensure completeness and accuracy.
Filtering, to analyze only the critical information for each operation.
Be stored on centralized platforms that enable real-time visualization for operators, departments and company leaders.

This is only possible if the data is captured, managed and analyzed efficiently, through digitization and automation technologies that allow the information generated in each operation or transaction to be collected, structured and stored, and facilitate its subsequent analysis.

Key technologies for data generation in internal logistics

There are multiple technological tools that contribute to the collection, storage and analysis of intralogistics data. Among the most relevant are:

ERP enterprise resource planning system. It is a modular software that automatically captures and records data from each process, distribution or manufacturing. It serves as a central platform to access information from any department.
Warehouse Management Systems WMS. Centralize and manage all data related to the operation of a warehouse or logistics center, from inventory to the location or movement of products.
IoT devices and smart sensors. This interconnected system is responsible for capturing and transmitting data in real time, enabling the monitoring of equipment and goods flow. For example, they can provide information on the status, location and performance of assets.
Identification and tracking technologies, such as RFID tags or barcodes. They allow products and materials to be tracked along their path within the plant or warehouse. Scanners or readers capture data every time a product enters or leaves a specific area.
Automated solutions for the packaging process, such as the automatic carton erector, which can capture and deliver data that provides insight into production speed, carton batch consumption, downtime and operational efficiency.
Automated internal conveyor systems, conveyor belts and picking robots are equipped with sensors and software that capture data on operating times and efficiency of goods handling.

In addition, there are automated technologies or solutions for specific sectors. For example, in the market there are:

ERP software for companies in the food industry, which are designed to generate data related to this industry.
Hanging garment counters in loading or unloading docks. They are automated solutions for the textile and fashion industry. In addition to providing the quantity of garments for each batch, they record the size, quality, model and color of the garment. They even generate the respective packing list.

4 applications of data analysis in intralogistics

Some of the uses of data analysis in the field of internal logistics are described below.

Demand forecasting

Thanks to the data and its analysis, it is possible to forecast future demand and adjust production or inventory accordingly. For example, it is possible to identify consumption patterns in sales history and detect seasonal fluctuations. This is associated with lower costs.

Improving traceability in the supply chain

Traceability is crucial for all companies, especially those that produce or market sensitive products. Data enables accurate tracking of each item throughout the internal supply chain. This also ensures regulatory compliance.

Optimization of internal flows

Data facilitates the optimization of routes within warehouses and other logistics centers. Thanks to data, it is possible to know and analyze the internal movement of goods or equipment. This allows companies to identify the most efficient routes and redistribute areas to reduce replenishment, picking or order preparation time.

Optimization of the use of resources

Data analysis helps to effectively manage equipment, materials, vehicles and human resources. Mainly because it is possible to improve the organization of operations, helping to minimize unnecessary movements and reduce production or shipping times.