Logistics

Case Study – Logistics: On Time Delivery using Big Data

6 Jan 2015

This case study details Ingenious Qube’s understanding of designing the architecture and deploying for Logistics companies, which requires that their customers must receive the item deliveries on time.

The Problem

Here we are taking a case of a logistics company which is an amalgamation of multiple acquired companies. The parent company wanted to consolidate data across the company as the acquired companies were using different platforms to capture and store the data. In order to develop higher-level insights on customer behavior and identify failures & customer dissatisfaction patterns the company wanted all the data into one database.

The Solution

In order to consolidate the data efficiently, we designed a common standard under which all specific company-level data could be linked to customers who received the deliveries or purchased the services from this company.

The final architecture included several data tiers to stage and transform the data from each individual platform into the new data standard. A large Hadoop cluster managed the on-board process of the company’s high quality data and handled the batch analytics to identify customers who frequently purchased the services from the company.

The platform not only had to on-board and organize the data efficiently, but also had to provide real-time analytics on demand. A scalable NoSQL system was used for ad-hoc analytics and reporting using the Hadoop stack including PIG and HIVE.

Benefits for the Client

The new platform has allowed the company to:

  1. Search a single source for its child companies performance. All data is now located in one place, and on-demand analytics provide high-level customer insights.
  2. The data can now be used to generate a complete customer profile, helping the client tailor its services to meet specific needs, develop targeted marketing campaigns and increase overall sales.
  3. Enabling the company to have first hand information of any delays in deliveries which can be cascaded to the front desk customer care executives who can inform the end customer of any delays thereby reducing customer dissatisfaction.

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