Demand Forecasting in Transportation

Transportation has always been a key component of the supply chain; customer satisfaction and loyalty depend largely on purchases arriving within estimated delivery times. While this importance has grown in recent years, the consolidation of online marketing channels and a fully globalized market have led to a significant growth in operations, which reached its peak during the health crisis caused by COVID-19.

It is no secret that there is a lot of money at stake in logistics operations, making fleet management and the optimization of routes and loads key elements in achieving business efficiency across all sectors. Traditionally, these tasks have been performed based on non-objective parameters, such as the results of previous campaigns, staff estimates, or activity history—leaving many factors to chance.

The multiplication of purchasing channels, the variability of references, and the increase in the number of inventory items require transportation management processes that minimize uncertainty to remain competitive in today’s challenging market. A great ally in this regard is found in demand forecasting systems, which allow us to know the future sales of a product in a given period in advance.

Efficient Fleet Planning

Delving deeper, thanks to advanced analytics, artificial intelligence, and algorithms, we can know ‘what will happen’ in the short and medium term. This simplifies decision-making and operational design, which will be based on objective facts, thereby gaining efficiency. The key to the process lies in the data or, rather, in the exploitation of all internal data (stock, orders, sales history, supplier lead times, etc.) and external data (economic indicators, weather, activity sectors, or even social media sentiment, for example). When compiled, processed, and linked under a data-driven philosophy, these offer a future snapshot of the entity’s product demand.

In addition to simplifying and streamlining stock management, purchasing processes, and production—ultimately every link in the supply chain—this enables optimal planning of transportation services. If we know that in month X, N units of a certain reference will be sold, Y of another, and Z of a third (extending the example to the entire inventory), and we know their estimated delivery areas, we will be able to carry out efficient fleet planning, adapt routes, establish schedules, determine product dispatch centers, estimate the personnel needed for each transport operation, and adjust delivery times for new sales, etc.

Any type of demand variation, whether an increase or decrease, affects transportation flows. Therefore, having a sales forecast integrated into the company’s TMS or planner simplifies the design of delivery operations and optimizes the process, resulting in cost savings and improved customer service.

Furthermore, if the demand forecasting system is accompanied by a load optimizer to take advantage of every millimeter of the fleet units, the process is complete: greater efficiency, increased sustainability, and improved customer service, while saving costs and increasing profit.

Conclusion

In an absolutely globalized world, transportation is becoming increasingly important every day. Logistics operations are becoming more complex due to the current consumption system and the rise of online buying and selling. Given this reality, technology is the best ally for the business fabric.

Advanced analytics, artificial intelligence, and big data provide businesses with tools to optimize all links in the supply chain, offering countless benefits to transportation flows. Specifically, the demand forecasting systems we have focused on simplify decision-making by providing estimates of future sales and, therefore, the loads to be moved.