Road transportation is constantly evolving in terms of internal planning, given an increase in urgent demand and the emergence of new technologies. Predictive logistics, based on Big Data, is playing an increasingly important role in this revolutionary shift.
Road transportation and prediction
With data analysis, predictive logistics can now identify behavioral patterns and transform them into a useful tool managing the entire supply chain, including road transportation. Predictive logistics improves the efficiency of the work carried out in each area of the supply chain in the following manner.
Anticipatory risk management
What does anticipatory risk management entail? Knowing at exactly what time of year the demand for a logistics service will drop sharply. Knowing the most likely spots for traffic hold-ups. Or knowing that goods require particular care during packaging because they are fragile. These are all examples of how anticipatory management, also known as predictive management, can reduce direct and indirect costs ensuing from undesirable situations.
Aligning logistical capacities with the exact demand makes it possible to predict an increase in capital, as well as adapt human and material resources to different situations so as to eliminate gaps. Predictive algorithms help to avoid financial tensions ensuing from undesirable situations.
Data analysis tells you when to carry out maintenance and when to maintain or switch teams, whether they are responsible for the warehouse or road transportation. In this way, action protocols are established, minimizing malfunctions and extending the useful life of machinery and vehicles. The regular maintenance of critical machinery is crucial to prevent costly operational shut-downs throughout the logistics department.
Scheduling deliveries long in advance improves road transportation, because it speeds up the entire process from the outset. Data analysis makes it possible to set up a more dynamic, efficient management model.
The origin of data
Road transportation generates a vast amount of data in the course of a day. After due analysis, geolocation, sensors, databases and integrated fleet management promote more efficient real-time decision-making. Ultimately, the beneficiary of each improved process is the end client, whether that is another business or an individual. The end client benefits from companies’ considerably improved productivity and reduced costs. Predictive logistics is also useful for the management of returns within the road transportation supply chain.
Interpretation, the major challenge
The stage on which logistics and road transportation are currently operating is one of transformation, in which Big Data is crucial for the predictive supply chain. The challenge that logistics departments currently face is distinguishing valuable data from data that is circumstantial or of little use for prediction. Only by resolving that challenge can market demand be met and vital profitability achieved.
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