Machine Learning, Predictive analytics and Data Integration in Logistics

Item: Addepto
Rating: 4.7
Author: 19 Clients

We helped a logistics company to make right business-critical decisions using full range of information and machine learning. We integrated data from multiple systems into Enterprise Data Warehouse (EDW) and implemented AI models on top of that.

THE CHALLENGE

Detailed analysis of costs and benefits

A lot of bad decision was made because of poor cost and profit analysis. Company want to have a flexible and business friendly tool for detailed analysis of costs and profits for making right business decisions in fast time.

Estimation of Bottlenecks and peaks

Not properly planned business bottlenecks and peak hour/days could resulted in losses and unsatisfied customers. Company want to better and more accurate plan resources and warehouses utilization in a more advanced way.

Operational capacity planning

It’s very hard to plan capacity in efficient way without integrated data from various transaction systems. Company was using few systems in separate business units management such as warehouses, transportation and customs services.

OUR SOLUTION

Embedded analytics

After solution deployment company was able to analyze large amounts of disparate data with quick response using internal application. Company was able to extract disseminate business insights in real time and share those insight using interactive dashboards with decision makers. Such analytics application became a real backbone for the future of the supply chain.

Machine Learning in Logistics – predictive modeling

Predictive analytics is improving supply chain and logistics industry by being able to accurately collect and analyze data that helps in management decisions. It can also help address issues like damaged inventory, stock errors, and supply and demand miscalculations. Predictive capabilities allow organizations to accurately address customer service and traffic patterns, labor unrest, and weather events that affect shipping and port behavior.

Data integration (ETL)

Data integration phase involved extraction, transformation and data loading into one information management system while adhering to particular business rules. Data was integrated from various sources so it gave a possibility to analyze business from various perpspectives.

Machine Learning in Logistics – route optimization

Created algorithm now ensures maximum utilization of assets with tight control on wastage. It helps to automate and optimize logistics routes. This results in reduced logistics cost, on-time deliveries and a delightful end user experience.

BENEFITS

The solution provides the organisation with full range of insights and the information about organization condition and help to make right critical decisions.