3PL big data analytics solution

Discover how Yalantis helped a third-party logistics (3PL) company increase customer satisfaction, improve supply chain decision-making, and reduce operational costs by developing a complex big data analytics flow.

  • industry

    Logistics

  • Country

    USA

  • Team size

    5+

  • Implementation

    3 months

About the client

Our client is a US-based third-party logistics (3PL) company (name changed for NDA purposes) that provides their customers with order fulfillment, inventory management, warehousing and distribution, and freight forwarding services. 

Business context

The company needed an efficient data analytics solution to convert raw data from multiple sources such as customer logs, supply chain systems, RFID tags, and IoT devices into valuable information to support decision-making. In achieving this, they faced a number of challenges:

  • Incomplete data due to the lack of an efficiently configured process for real-time aggregation of data from multiple sources
  • Ineffective analysis of large amounts of unstructured data due to the absence of an advanced data analytics system
  • Poor accessibility to supply chain data siloed in separate departments

 

Solution overview

  • To efficiently tackle these challenges, our team started work on a custom big data analytics platform.

     

    Implementing core big data components for efficient data analysis

    The Yalantis team integrated the following big data analytics components:

    • A data lake to capture data from any sources, store structured and unstructured data at any scale, and ensure sufficient security and privacy
    • Real-time data extraction for tracking deliveries as well as optimizing time for refueling and vehicle maintenance based on GPS signals and data from IoT devices
    • Data visualization and analysis for tracking performance, consolidating route planning insights, and keeping track of vital financial indicators
    • Predictive analytics for predicting seasonal client demand, optimizing storage space with stock forecasts, and anticipating possible supply chain risks and exceptions to take proactive measures

  • Designing a flexible and scalable solution architecture for complex data analysis

    We designed a layered architecture to ensure separation of concerns, decoupling of tasks, and flexibility:

    • The ingestion layer is responsible for extracting data from internal and external sources to the big data analytics system. It’s built on AWS Data Exchange, which transfers data from third-party services into the system, and on Kinesis Data Firehose, which handles loading data streams from IoT devices directly into AWS products for processing.
    • The storage layer stores structured and unstructured data for easy use in all other layers. We used Amazon S3 to provide unlimited and low-cost scalability for the client’s serverless data lake.
    • The cataloging and search layer has a central data catalog for managing and storing ​​metadata for all datasets in the data lake through the AWS Lake Formation tool.
    • The processing layer makes data ready for consumption with the help of validation, cleanup, and normalization processes. We used AWS Glue to build and run ETL (extract, transform, load) jobs written in Python.
    • The consumption layer allows for data visualization, business intelligence (BI) analysis, and the use of ML algorithms. Amazon QuickSight provides a scalable and serverless BI service for data visualization, and Amazon SageMaker enables machine learning for predictive analytics on large-scale data.
    • The security and governance layer protects data in all other layers. We ensured data security and governance with the help of Amazon Identity and Access Management, AWS Key Management Service, and Amazon Virtual Private Cloud.

Value delivered

We developed a custom big data analytics platform for efficient business data analysis and helped the client achieve the following results:

  • Improved decision-making. The new digital solution allows company management to make informed decisions such as which new clients to target and which services to expand or improve.

  • Predictive stock planning. Real-time updates of sales figures, stock counts, and order frequency allow logistics managers to make accurate predictions on stock replenishment and help the company decrease warehouse operating costs.

  • Optimized route and load planning. Transportation analytics allows for mapping efficient shipping routes by analyzing such data as weather forecasts, traffic conditions, order frequency, and locations with the most and least orders.

  • Increased LTV. Customer managers now receive valuable insights about their clients (e.g. preferred delivery time, most frequent orders) to provide more personalized services.

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