Fast-growing third-party logistics provider in the US, innovates for the future with Microsoft Azure

Intellint (Fortude’s data & AI arm) initiated the engagement with the US-based 3PL provider by conducting a comprehensive analytics health check. This involved a thorough review of the organization’s data estate and analytics landscape, including data management mechanisms and processes, data governance approaches, reporting methodologies, and the needs and expectations of the organizational hierarchy. Based on this assessment, Intellint formulated a detailed and customized strategy and roadmap for data and analytics transformation. This initiative aimed to enhance the company’s existing data architecture and realign it to effectively address the current and future needs of the business. Intellint worked with the business to build a data management platform based on Microsoft Azure data services infrastructure. It leverages Azure Data Lake Gen2, synapse pipelines, Databricks, and machine learning features, along with Power BI Premium for enterprise data models and dashboards.

The key characteristics and benefits of this solution are:

• Enterprise-wide business intelligence
  The solution has management/executive dashboarding capabilities, department-based analytics and group specific reviews, a self-service analysis function, and embedded reporting capabilities to enrich the existing ones.
  
  
• Flexibility and efficiency
  At its core, this solution comprises a framework that provides standardized KPIs/metrics and consistent taxonomies. The department- and team-specific analytics functions, along with the self-service business intelligence capabilities, contribute towards a faster data analysis process. Users can access and generate customized reports they require based on their business needs.
  
  
• Modern data analytics platform
  This platform consists of two main layers – the data and the analytics layers.
  • The data layer is composed of a staging layer that stores raw data in a curated and standardized form. The ingestion layer within the data layer collects raw data from various systems, cleanses, and standardizes. You can implement this layer as a data lake.
  • The analytics layer consists of the integration, data model, and business model layers. The data model layer stores data in an aggregated, analytical form to ensure it is relevant across the company. This layer functions as a typical data warehouse.

The first phase of Intellint’s solution focused on the company’s sales function and looked at enabling the sales team to do operational analysis for sales in one place. Previously, this 3PL provider had to get different people to build data sets and create reports. They also struggled to manage the vast volume of cluttered reports, so Intellint helped improve the structure of these existing reports. With the new architecture in place, all the data was housed in one location. This made it easy for the sales team to access data more easily and build reports simply via a drag and drop mechanism, without burdening other key resources.

The Intellint team also rolled out phase 2 and 3 of the project which focused on enabling operational analysis for the carrier sales (sales is done through trucking companies) and logistics functions, respectively. Intellint also conducted quarterly business reviews with the organization which were focused on identifying opportunities for the business to better harness the potential of its digital investments. This includes consolidating data from other sources such as the company’s financial data from their ERP, and the setup of machine learning models for cost predictions. Usually, logistics costs come from a third-party source and the prediction is not reflective of the actual costs. It tends to be considerably higher, so they tend to oversell to keep that margin. With ML, they are able to test out models fast so they can fix it and offer competitive pricing that reflects actual costs.