In the narrow sense of the word, the term big data simply describes data sets that are too large, too complex or too unstructured to be evaluated using conventional, manual methods. In logistics, the evaluation of data collected during the transport process, for example, aims to ensure that the right information is available to the right addressee at the right time, in the right quantity, at the right place and in the required quality.
Although this technology is IT-based, it is not a concrete method or even a machine. Big Data serves above all to optimize the “Business Intelligence” of companies. As in other sectors of the economy, Big Data is used in logistics primarily to gain competitive advantages. The extent of these competitive advantages always depends on the way Big Data is used.
Possible applications of Big Data in logistics
Big Data is already being used in fleet management in logistics today. The vehicles continuously send data about their location, loading, driving behaviour and consumption values. These data can be evaluated in real time so that in combination with driving and rest times as well as arrival information fuel can be saved, personnel and vehicles can be optimally deployed and the overall economy of the fleet can be increased.
Big Data is also used in risk management. For this purpose, data is collected on events that can have an impact on logistics chains, such as the weather, strikes, the traffic situation, but also crime in certain regions. It uses specially collected reports as well as data from social media (Facebook, Twitter etc.), blogs or newspaper reports. The digital tachograph, which will be mandatory for all newly registered trucks in the EU from mid-2019, will, for example, supply traffic volume data to a central traffic control centre in real time. This makes congestion predictions more precise.
An application example that will rapidly gain in importance in the near future is linked to RFID technology and the Internet of Things. RFID chips make it possible to make goods traceable down to individual components. The goods could then use this technology to find the best route to the customer themselves, so to speak. For this vision of the future, however, a big data infrastructure is still needed that can cope with the complexity of the data.
Big Data has also long since arrived in medium-sized logistics companies
According to a study by bitkom research, six out of ten companies in Germany are already using Big Data for their business this year (57%). Only 51 percent the year before. Big Data solutions are mainly used to increase efficiency (in logistics, for example in fleet management). Overall, three quarters (75%) of companies report making relevant business decisions based on data analysis findings. But: Only 12 percent of the companies have a big data strategy. In other words: 88 percent of the companies using Big Data have not developed a separate strategy for this. This is particularly strange because it is the strategy that decides whether big data can really be used to gain competitive advantages.
More successful with the right big data strategy
Data is not an end in itself. Big Data is therefore not a panacea that can be used blindly to increase a company’s effectiveness. Each company, and within each company also each department, is interested in very specific data sets. However, the strength of Big Data is only played off when these different interests are brought together and seen in context. Therefore, “silos” grown in companies often have to be abandoned before Big Data can be used sensibly. Big data is therefore always a management instrument. With the help of data analyses, the business and its environment can be seen more clearly and understood more deeply.
A big data strategy should therefore always be
- be integrated and know where to set which integration points (networks to other companies, for example). The data should therefore always be collected in a structured and scalable manner. Only then can they be connected to external data sources and used efficiently.
- be company-related and connected to as many parts of the company as possible (e.g. warehouse, fleet, office).
- be open to innovation. On the one hand, data analyses often lead to new insights, on the other hand, Big Data is not a fixed technology and is, therefore, itself subject to continuous transformations.
With the right strategy, Big Data can help to make logistics processes more efficient, avoid empty runs and protect the environment.
Risks of Big Data
For companies, the main risk of this technology is to draw the wrong conclusions from the data and thus invest in inappropriate business areas, for example. There is a great temptation to be tempted by the flood of data to mistakenly believe that the figures would automatically point the way into the future.
Another risk is a lack of data protection. Big Data requires a lot of data and therefore the consent of many people and groups to data collection and processing. If this consent is misused – whether deliberately or not – the image damage is usually enormous. In the wake of the career of the term “big data”, data protection is becoming increasingly important.