Data analysis in logistics: How big data is transforming supply chains

Rising customer expectations in the e-commerce sector present warehouse logistics with the challenge of organising processes and supply chains more efficiently. The solution? Precise data analysis of logistics with the help of big data, the trailblazer for Logistics 4.0. Smart data optimisation enables accurate predictions of demand and supply as well as a reduction in time and costs, especially for logistics, but also for the customer.

Empty transport vehicle in the Rhenus warehouse

Data analysis in logistics offers new potential for warehouse optimisation

The e-commerce sector is constantly presenting logisticians with new challenges, particularly in terms of flexibility and storage capacity. Customers’ expectations are increasing because they are able to do their shopping conveniently round the clock – and this creates greater pressure on warehouse logistics. The goal of fulfilment is therefore to make processes even more efficient, faster and less expensive. When looking for solutions, one possibility is often neglected – the huge amounts of data, also known as big data, that are generated by sensors, cameras & co. every day. But how can this help optimise processes in a warehouse?

E-commerce in transition: challenges in warehousing and logistics management

Small-scale orders and increasing numbers of returns: the list of challenges in e-commerce is long. Then there are customers’ high expectations for very short delivery times and maximum availability. The frequent changes to the range of items means that warehouse stocks need to be constantly adjusted. When combined with the increase in item picking, these factors create expensive and labour-intensive processes. Quality and precision are enormously important, too: returns and corrections are expensive and endanger the relationship between consumers and brands as well as the agreements between warehouses and their customers. Seasonal peaks, which further increase the pressure on warehouse logistics, are a special challenge. The need for optimisation in workflows, deliveries and costs is increasing to an enormous degree.

Big data in logistics: the basis for continuous optimization

There are many technologies on the market for organising warehouse processes more efficiently – ranging from autonomous mobile robots to AR glasses. But managers can also make adjustments at many points in the existing infrastructure. One area, which has often been neglected in the past, is particularly suitable: collecting and analysing big data in logistics. This involves very large, complex and short-lived amounts of data from a wide variety of sources.

When combined with artificial intelligence (AI) and the Internet of Things (IoT), the warehouse management system becomes much more responsive. This enables e-commerce providers to adapt to the constant changes in market requirements. This creates benefits because machines, warehouse management systems, cameras and sensors – to name just a few examples – generate the relevant data anyway. If the right solution is available, data analyses in logistics can be used for continuous optimisation purposes.


Best practice: How can you usefully employ in e-commerce fulfilment?

Data analyses in logistics are based on data collected by the warehouse management system in a particular logistics project that is suitable for big data analysis. It may be based on activities and stock resources, productivity or quantities that are processed within a particular period. There are special tools to make the data usable for companies. They analyse the current state of various process areas and uncover potential for optimisation when they are combined. Once the processes are compared in relation to one another, it becomes evident that they change every day and then it can be determined to what degree these changes can be predicted. Modern technologies also provide huge flexibility for drawing up reports and all kinds of dashboards. Automatic warehouses are always preferable to manual warehouses. Automated processes can generate more information. Depending on the number of sensors, all the workflows can be reviewed every second. It is also possible to generate feedback which is sent to the systems based on the data. Particular workflows can be automated in the warehouse management system, for example, but may create delays in procedures and therefore time lags.


“The link between the warehouse management system and the big data tool can be provided via an outside platform. This then enables us to understand and diagnose specific problems, e.g. the loss of resources.”

Marta Kunikowska | Marketing Manager at Rhenus Logistics Poland


Big data use cases: Which processes in a warehouse can be optimised?

There are many use cases based on big data in logistics. One general possibility involves constantly checking the basic data in the warehouse and then rapidly responding to any changes. An order number can be used to see which stages in the complete warehouse process an item is passing through and what the results are. The warehouse operators can check where and when a particular order and the items contained in the order were processed – and by which employee.

If any bottlenecks occur, it is worthwhile discovering how often this happens and on which days of the week they are most frequent. Then the warehouse can be reorganised in line with this information. If any anomalies occur in the warehouse, e.g. two items in one place, automatic emails can be sent to the corresponding people. Improving customer services may be another benefit. Comparing warehouse processes reveals hidden cost factors. For example, it is possible to draw the attention of the e-commerce provider to the high costs of cancellations of orders by customers and determine the scale of these losses.

Distributing resources intelligently thanks to big data

If the potential of data analysis in logistics is used correctly, it is possible to proactively respond to customers’ many different needs. This then creates a better shopping experience. Greater warehouse efficiency and procedures that are better planned and prepared have a positive effect on customers’ experience. Logistics companies can specifically adapt to fluctuations in demand and order peaks by analysing data. For example, how long does it take to pick or pack an order? Resources like employees, warehouse space and infrastructure can be sensibly assigned if this kind of analysis work has been completed.

Regardless of which processes are involved, it is important to ensure that the incoming data is reliable and matches the real situation. The challenge here is whether a direct link to the original source of the data is possible, regardless of the storage space. If there is no direct link, CSV files are used, but they need more storage space and also slow down the analysis processes. In this case, it is preferable to download the largest data models at night when the server load is lowest.

Competitive advantages for logistics specialists through big data

Major stores and online e-commerce platforms normally outsource fulfilment services to specialist service companies. The external provider has experience and has developed procedures and resources. It can handle orders in a flexible way and still maintain high quality levels. If the company uses big data in logistics, it also has greater insight into the processes. As the information is available in real time, it can respond to inefficient procedures quickly and flexibly and make changes. This minimises the risk of not meeting set KPIs. Bottlenecks are eliminated at an early stage. As a result, the fulfilment service provider can process orders and dispatch them to end customers faster.

Strategic use of big data for marketing purposes

It is necessary to quickly analyse logistics processes in real time to respond to unforeseen consumer behaviour in a flexible manner. Fulfilment providers have become aware of how important it is to use big data and appropriate tools, particularly through the Covid-19 pandemic. Thanks to analysis work, they can predict the behaviour of e-commerce customers to a certain degree. The retailer can then plan its advertising and sales campaigns differently. If the retailer knows what products customers buy together with other products, it is possible to control their choice to a certain extent and sell products that are not so popular.

Transport vehicle with goods in the Rhenus warehouse

Conclusion: Optimised logistics create a positive brand awareness

Big data prepares the way for Logistics 4.0 by combining numerous data sources. It will be essential to log and analyse data in the future to be able to adequately predict supply and demand. If used intelligently, the data supports the expansion of business and reduces the time and costs for handling customer orders. Whether the data involves flows of goods, downtimes, or routes through a warehouse: it offers an enormous breadth of opportunities for improvement potential that is ongoing. This is an enormous advantage, particularly in the short-lived world of e-commerce and its sharp fluctuations. Overall, big data strengthens logistics through data analysis and ultimately leaves its mark on an important part of a customer’s brand awareness.

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