Data Science in Freight Negotiation & Truck Aggregation

Data Science in Freight Negotiation & Truck Aggregation

Freight negotiation is almost a straightforward process, one would think that initially. Although, whenever you look at it closely, it is far more art than the science required to acquire quotes at the best price. With no involvement of science, many companies in India rely on their proficient logistics managers to perform this function. For getting the best rates, logistics operators rely on their experience and their transporter system every day. However, the Indian transport industry is a messy sector, and it makes freight purchasing a strenuous process.


Data science to the rescue.


Technological advances in the field of data science and machine learning can help make this freight negotiation and truck acquiring more foreboding, attested, and compatible.


Techno logistics firms such as CtrlT have influenced machine learning & data science techniques and made inventory freight negotiations presentable, reliable, and sustainable.


A team of CtrlT developers and logisticians studied a large Steel manufacturer’s inventory and reached the daily estimated freight rate. The ML algorithm estimates the freight rate by lane (Hyderabad to Bangalore) and vehicle capacity-wise (7MT, 15MT). Fuel rates, seasons (off-season, regular season, peak season), regional holidays (Pongal, Holi, Diwali, etc.) are some of the factors considered.


With the freight analyzer, the Steel manufacturer has a good reference point to ratify against genuine rates and they also use it to determine the selling price. They are seeing huge upside with this process in predicting the right price and building a transparent system. For them, acquiring trucks is not an art, it is a science – Data science.