Transport Cost Optimizer

Challenges in Transport Cost Optimization

In today’s competitive business environment, businesses are faced with low margins, pressure on volumes and volatile fuel prices. At the same time, they are constantly looking for the most efficient way to transport goods from a pick-up point to delivery location. Inefficient routing wastes minutes, miles and money, leaving on-time arrivals and important elements of customer satisfaction to chance.

In-house systems for commercial and logistical operations often lack dynamic optimization because of the complexity of calculations that are needed. Transport optimization is valuable for many organizations as it can increase productivity and improve efficiency.

Can a dynamic transport cost optimization solution address these challenges?

From a technical perspective, traditional routing and scheduling applications use batch planning algorithms to create an optimal set of routes for each day’s deliveries. Running groups of orders through a batch solver allows many alternatives to be considered and, in this way, provides better, more optimized solutions than a manually produced solution. The classical Travelling Salesman Problem (TSP) is a famous example in the study of route optimization in the field of computer science. Route planning has always been a major interest of study due to the nature of the topic which covers many industries. Transportation pathways, flight routes, travelers, delivery services and many more applies TSP which means there is a need for a pre-planned route schedule to ensure an optimized travelling has been performed.

Dynamic transport cost optimization improves the timing and routing over that of the regular batch model with real time adjustments and Demand-driven profitability. For transport this implies that the trend is moving from focusing on cost with enough capacity towards service in conditions of tight capacity. For businesses, this means both embracing adaptive planning and taking a forward-thinking approach