The inclusion of Big Data in transport and logistics favours a more efficient management in all processes, and above all, a significant optimisation translated into profitability and an increase in the number of clients. Taking advantage of all the opportunities offered by the study and analysis of Big Data, and applying it to the current and growing urgent demand, the concept of preventive logistics appears.
Data analysis already allows predictive logistics to find patterns of behaviour and use them as a tool for the management of the entire supply chain. With the possibility of making a forecast of all the factors involved in each process and make the work more efficient by the following points:
Detection of risk.
The possibilities of predictive management are infinite. It offers good data management, applied to logistics, and based on our needs or the problems we want to solve. They are a reliable source of data and of great importance in order to create strategies or behaviours that are more profitable, quicker or more effective. We will be able to detect situations such as the following:
- Know beforehand and specifically the dates of the year in which there may be a sudden decrease or increase in logistics demand.
- Identify the points on a route at high risk. For example, by the amount of withholdings that occur.
- Recognize goods that require special care.
- React efficiently to certain adverse situations due to meteorological causes.
- Identify faster or more fluid routes for certain stages.
Preventing the logistic capacities of the specific demand allows to adapt all the technical, material and human factors to approach it only with the necessary resources reducing the expenses. In addition, no gaps are generated and unforeseen situations are avoided.
The data can be known to detect the key dates for carrying out maintenance services, the situation of the equipment and the warehouse. These maintenance forecasts prevent deterioration and prevent unexpected breakdowns that can lead to very costly situations.
The process can be speeded up from the source. It is possible to implement more dynamic and efficient management models in order to offer more fluid and faster services.
Analysis from the source of the data.
We can have access to a wide and differentiated amount of data in each day. Geolocators, sensors, databases and integral management services are the most important source of data to make the most appropriate decisions. With them, we can create the necessary and customized strategies to make the most of our resources and offer the best services to our customers.
Interpretation and extrapolation of data in transport and logistics.
This is the key to optimising the predictive supply chain. There is no point in implementing data collection and computerising all our services without knowing how to use them afterwards. They must be used correctly to achieve success. We will reduce costs by gaining fluidity in our transport, and greater adaptation to our customers and new markets.
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