Demand and Supply Optimization for CPG (AWS, Spark, Python, Machine Learning, Tableau) – uat-website.datacrew.ai

Client

Africa-based CPG customer involved in the production and sale of Wheat flour-based products

Project Title

Driving Supply Chain Efficiency with Predictive Analytics for an Africa-based CPG leader

Client

Africa-based CPG customer involved in the production and sale of Wheat flour-based products

Problem

Significant challenges in demand forecasting and supply chain optimization.
The primary issues included frequent stockouts, Lost Sales, overstocking, high inventory costs, and suboptimal resource utilization.
These problems not only affected operational efficiency but also had a direct impact on the company’s revenue and profitability

Solution

Implemented a streamlined data and analytics solution using essential machine learning (ML) methods and a focused tech stack.
Time series Analysis models like ARIMA were employed to predict future demand based on historical sales data, accounting for seasonality and trends.
Regression Models: Employed to understand the impact of factors such as promotions and market conditions on demand.
Clustering: Applied to segment customers and markets for more targeted demand forecasting.

Value Proposition

The company achieved a 20% improvement in demand forecasting accuracy.
Accurate demand forecasting led to a 15% reduction in inventory levels
The improved inventory management resulted in a 10% increase in revenue due to better product availability and a 5% reduction in operational costs.
The overall supply chain became more agile and responsive to market changes