ML-based demand forecasting solution
to improve forecast accuracy while reducing
costs by 5x

Built an ML-based demand forecasting solution enhance visibility in the supply chain based on their market share reducing the data run-time

Business Challenge

A leading alcoholic beverages company operating across 180+ countries was finding it challenging to estimate their market share in the industry. The customer had limited business datasets for market share estimation and relied on other sources such as Nielsen, IWSR, and more. The right estimation would help them predict demand and optimize supply chain to grow their business. The estimation required them to model around 200,000 time series, which is a sequence of data points, considering the micro and macro exogenous factors in a quick and coherent manner.

Sigmoid Solution

Sigmoid developed an agile demand forecasting solution by pulling data from Azure Data Lake storage, preprocessing it to remove outliers and running feature engineering on it. We trained several ML and deep learning models that ran parallely across all countries on Azure Kubernetes Service (AKS) to generate a trained model representing the behavioral aspects of the data to determine specific patterns. The trained model was used to create forecasts that were closely monitored through Grafana. To modularize the code, Sigmoid built the entire framework on Kedro that offers best practices for software engineering and makes the code easy to scale. In addition, we operationalized the final solution by parameterizing the end-to-end pipeline countrywise with auto-enabling features on AKS that scale up and down based upon the number of countries in the run.

Download the Complete
Case Study here

Business Impact

Sigmoid helped the customer predict nearly accurate demand forecasting to optimize their supply chain, improve inventory management, reduce overhead cost, enhance collaboration with suppliers, and improve risk mitigation.

20x

faster data run time

5x

reduction in forecasting cost

3x

reduction in compliance and overhead cost

Relevant Blogs

Microservices Architecture Blog Info thumbnail

Microservices-based Architecture: Key to Scaling Enterprise ML Models

ML model training blog thumbnail

Top 5 Model Training and Validation Challenges That Can be Addressed with MLOps

How to Detect and Overcome Model Drift in MLOps Banner image

How to Detect and Overcome Model Drift in MLOps