We can’t foresee the future events and analyze their outcomes, but what we can do is use all of our current digital capacities to gather and analyze past-data to draw insights into the future. In data science terminology, this process is termed as predictive analytics, and it is now widely adopted in sizeable heterogeneous business networks for greater transparency.

Predictive analytics is helping businesses to break away from passive legacy frameworks and introduce greater efficiencies across business cross functions. ‘Logistics’ is the new entrant in the list of predictive analytics benefactors. This blog highlights some of the prime predictive analytics advancements that are making logistics in the 21st century truly smart and efficient.

A logistics network is made of many complex moving parts. Freight service providers need to take care of myriad challenges ranging from on-time freight delivery, excess fuel consumption to reckless driving habits. In the past, finding a sustainable and cost-effective solution was not feasible but today with AI, IoT, GPS, and predictive analytics features, introducing efficiencies and transparency across supply chains networks is no longer incomprehensible.

Predictive Modelling

Data modeling is used as a blueprint to set the rules for particular organizations data interactions. It maps how a particular organization’s data will be stored and organized. Its principal emphasis is to ensure data security and subsequent compliances with data rules and policies.

But thanks to digitization, the organization today are generating new data at a breakneck speed. This new data lacks uniformity and structure to be of any substantial use. Data modeling helps to introduce structure and meaning in this new data. Through classified mapping models, this data can be used to draw insights depending on business needs. Additionally, the accuracy of these insights is proportional to the quality and quantity of data. So a significant advancement in predictive analytics has been the development in modeling tools that can handle a large amount of data sources.

Smarter Analytics For Smarter Logistics

Logistics play a primary role in business productivity and efficiency. Smarter freight forwarding solutions are necessities for modern businesses. Data mining and advanced predictive analytics tools help logistic service providers to introduce point blank efficiencies in key logistic parameters like:

• Smarter traffic predictions.
• Fastest route suggestions.
• Organized multiple pick-ups and drops.
• Freight safety and security.

As per a report, nearly 30% of businesses face freight transport issues from hostile weather conditions. Advance predictive analytics helps estimate weather conditions much more accurately than ever before. The predictive insights can be used not just cut down on freight delivery time but also prevent any undesirable business loss. An equally important impact of advanced predictive analytics can be seen in eliminating supply chain soft points.
Predictive modeling helps collect, cleanse, and refine data sourced from multiple supply chain touch points. An in-depth analysis of these interaction points offers a complete 360-degree view of the distribution networks, making it easier for service to identify and eliminate logistic pain points faster and better.

Today, as the digital age has entered its full spring, logistic service providers are considering data as the primary asset to drive fundamental, operational, and structural business decisions of the future. An important prerequisite for success in any data-driven strategy is ensuring that the data quality is uncompromised.

However, logistic services do not have the IT capacity to implement reliable data modeling and analytics solutions. Pratham, with years of expertise in data analytics services, has helped to introduce analytics-driven efficiencies across industry-specific supply chain systems. From data engineering to data visualization, Pratham offers tip-to-toe smart logistic solutions. Contact us today and let us help you adopt and adapt to the best data-driven logistic strategies.