Freight transport and logistics professionals must be fully prepared to anticipate and adapt to sudden spikes in demand, even amid challenging market conditions. Ontruck, a leading digital road freight platform, has developed a predictive tool that leverages Artificial Intelligence and machine learning to predict business behavior with an average margin of error of 10% in demand and 16% in load capacity by vehicle type.
Intrinsic to the success of these forecasts is the ability of the model to account for a complex range of real-time variables. These variables include the history of business loads, customers who placed orders, their sectors of activity, the recipients, origin and geographical destinations of the loads, the type of vehicle, and even the exact pallets used. With this data, the model can effectively analyze seasonality and the impact of key shopping holidays such as Christmas and Black Friday and how they affect specific locations. The Ontruck team also provides the system with official economic data sourced from the Office of National Statistics, such as percentages of businesses that open or close in a city or country, or extraordinary macroeconomic events, (limitations on mobility, a strike, a border closure), which are entered manually.
On top of seasonal peaks, transporters face the added pressure of working within the dynamic conditions created by COVID-19, which has showcased the increasing importance of flexibility, agility, and organizational planning in resilient supply chains. The fast-changing developments of the pandemic are factored into the prediction tool analysis, meaning that Ontruck can evaluate and best prepare for any potential impacts of the COVID-19 outbreak on business.
Ontruck's predictive model is split into two phases: the first offers a forecast report of up to two months in advance. Following this, a fortnightly review is conducted to take into account any updates that may have arisen. Using this closely audited approach, it is possible to make adjustments to account for any unexpected situations which may arise, such as a new confinement order or border closure, to allow for the most accurate predictions possible.