Forbes Insights’ research unraveled an eye-opening insight – 65% of logistics gurus reckon that currently, logistics is undergoing a profound transformation. Of the most eminent forces of change, the nexus of AI and ML (and their subset technologies) has been proved to catalyze the shift in the most remarkable way.
Logistics, as an industry, faces the onslaught of an incredible amount of data influx. Top that with the pre-existing truckloads of data collected over the years through telematics or electronic transport logs. For years, the industry has relied upon high-level decision theories to meaningfully use this data to optimize costs or reduce transit times. However, the exception today is not only more data but the extreme computing power that’s capable of performing massive calculations. Capable of detecting patterns and recognizing variables that make or break a supply link. This is where data science comes in.
We, at TruKKer, were never late to realize that these new-age technologies are now real-world forces. Let’s look at how data science is revolutionizing the logistics sector:
Pricing in logistics is very dynamic and doesn’t explicitly depend on just the miles covered or the travel time. Several intrinsic aspects need to be factored in which only further complicate the whole estimation process. Factors like the type of cargo, weight, loads on a particular lane, the time required for clearance and border formalities, and client requirements (if any) profoundly influence the overall cost estimation.
The only way out of this labyrinth of uncertainties is to harness the power of data science and drive efficiency and accuracy into the process. Price determination requires a malleable approach and real-time cost data. Data science powered algorithms are not explicitly programmed; instead, they use self-learning techniques to learn the pattern from any data. So, it indulges in a perpetual cycle of learning new trends or emerging demands while continuously integrating new information. We, at TruKKer, sift through heaps of historical data to glean insights for better price estimation. This enables us to provide instant quotes to our customers and become the first company in the Middle East to do so.
Web mapping services like Google Maps, Apple Maps have been deeply embedded in our daily lives as we set out for office, school, or anywhere. These have enabled us with the virtue of punctuality as they calculate the estimated time of arrival (ETA) and show us beforehand.
However, it’s a different ballgame altogether in the logistics world. Calculating ETA is not as straightforward as any ongoing travel is subject to change due to a plethora of issues. Time to load, time spent during border clearance, the weight of the cargo blurs the precision to propose a promising ETA. Hence, there is no industry-level standard or tool which can predict the ETA for a truck with pinpoint accuracy. That’s why TruKKer adds a layer of data analytics atop its data aggregated over the past years. This allows us to anticipate the ETA accurately while considering all the change factors.
Often, per emerging loading requests, the truck availability doesn’t scale up and consequently dents the reputation or profitability of a business. But what if the load can be predicted or the demand can be known beforehand?
TruKKer strives to respond to each client’s demand through 100% truck availability. By linking historical activity data with geo-localized market data and other indicators, we thrive on planning the fleet according to each demand.
Generally, logistics companies deal with a wider variety of clients with an unprecedented range of demands appearing inadvertently. Thus, the requirements don’t appear linearly. For example, in summers, the requirement for reefer trucks shoots up to transport cold chain items like ice-cream, desserts, etc. Parallelly, the need to transport live produce, fresh vegetables or ACs also comes in greater frequency. Seamlessly allocating trucks to meet these requirements might appear as a challenge.
But with a promising algorithm capable of capturing the patterns can allow for the automatic and optimized allocation of trucks. We, at TruKKer, blend historical behavioral data with consumer profiles and run it through our inference models to predict demand with increasing accuracy. This also empowers our sales team to deliver a targeted pitch to a particular industry type and readies our operations team to envisage the required operational preparations.
A healthy supply chain link extensively thrives on effective fleet management for uninterrupted flow of goods and services. Using data science powered models, companies can benefit from ingestion, analysis, and real-time action on data to make crucial decisions in seconds. And all this in real-time and simultaneously.
TruKKer leverages real-time data insights by processing personal, proprietary, and public data from a plethora of data sources – truck types, traffic patterns, load management, etc. Our enterprise-grade, scalable database empowers our operators to successfully manage the fleet while eliminating any overhead.