AI-Powered Efficiency: Revolutionizing Supply Chain Management with an End-to-End ML Solution - Brillio

About the Client

The client is a US-based multinational corporation that develops medical devices, pharmaceuticals, and consumer packaged goods.

Customer Challenge

The client faces several challenges in managing inventory and optimizing supply chain costs across numerous SKUs and hundreds of locations:

  • Complex Inventory Management and SCM Cost Optimization: Thousands of Stock Keeping Units (SKUs) spread across multiple locations. Effectively managing inventory and optimizing supply chain costs to ensure efficient operations has become a daunting task.
  • Intermittent Time Series Data: The data received for hundreds of SKUs is often lumpy and intermittent, making it challenging to extract valuable insights and forecast demand accurately.
  • Establishing a Mature End-to-End Data Solution: The client wanted to establish a robust end-to-end data solution that goes beyond being just a Proof of Concept (PoC).
  • Scalable Sales Forecasting: The solution must be capable of handling the forecasting of sales for thousands of SKUs across various locations. Scalability is essential as the business continues to grow.
  • Low Maintenance with Automated and On-Demand Predictions: The client also needed a solution that is low maintenance, reducing the need for manual intervention. It should have the capability to automate processes and provide on-demand predictions to facilitate agile decision-making.

Brillio’s Solution

Our tailored solution addresses the challenges posed by our client’s organization’s inventory management and supply chain complexities through a comprehensive approach:

  • In-Depth Exploratory Data Analysis (EDA) and Statistical Testing: We begin by conducting a detailed EDA and applying rigorous statistical tests to assess the data’s quality and suitability for further modeling.
  • Custom Algorithms for Handling Intermittent Data: Brillio employs a unique blend of custom algorithms and well-established Machine Learning (ML) techniques to effectively handle the lumpy and intermittent nature of data associated with hundreds of SKUs.
  • End-to-End Azure-Based Pipeline: Our solution orchestrates an end-to-end data pipeline on Azure, encompassing all pre-processing to prediction steps.
  • Utilizing AzureML Compute Clusters: To handle large-scale training and ensure enterprise-level security and governance, we leverage AzureML-based compute clusters.
  • Flexibility for Easy Inference: Brillio’s solution allows effortless code modifications to facilitate easy inference, enabling you to adapt quickly to changing business requirements.

Business Outcomes:

Brillio’s tailored solution enabled significant benefits and empowered the client to achieve:

  • Rapid Forecasts: We have enabled the client to receive forecasts within an impressive execution time of 1.5 hours or less. This expedited process enables quick decision-making and response to market changes.
  • Identify Opportunities for Improvement: The client gained valuable insights into areas with potential for future improvements. This knowledge assists in optimizing operations, reducing costs, and increasing overall efficiency.
  • Swift Issue Resolution: The automated nature of our solution streamlines issue detection and resolution, minimizing downtime and maximizing productivity.
  • Highly Scalable Environment: Brillio’s AzureML-based solution is designed to be highly scalable, enabling the client to accommodate growing data volumes and expand business needs effortlessly.

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