At a strategic level, the topology and capacity of the
distribution network are adapted according to anticipated
future demand. The results from this stage of planning
usually drive investments with long requisition and
amortization cycles such as investments in warehouses,
distribution centers, and custom-built vehicles. More
precise capacity demand forecasts therefore increase
efficiency and lower the risks of investing in storage
and fleet capacity. Big Data techniques support network
planning and optimization by analyzing comprehensive
historical capacity and utilization data of transit points
and transportation routes. In addition, these techniques
consider seasonal factors and emerging freight flow
trends by learning algorithms that are fed with extensive
Big Data in Logistics
3 statistical series. External economic information (such
as industry-specific and regional growth forecasts) is
included for more accurate prediction of specific
transportation capacity demand.