Sales Forecasting and
Operational Optimization Using ML

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Overview

A leading FMCG company in the UK aimed to optimize its sales forecasting and operational processes by leveraging machine learning (ML) technologies on AWS. The goal was to enhance prediction accuracy, streamline operations, and improve overall efficiency, resulting in better resource allocation and increased profitability.

Challenges

Inaccurate Sales Forecasting:

Existing sales forecasting methods were based on historical data and lacked predictive accuracy, leading to overstock or stockout situations.

Operational Inefficiencies:

Manual processes and legacy systems caused delays and errors in operations, impacting the supply chain and inventory management.

Data Silos:

Data was scattered across different departments and systems, making it difficult to obtain a unified view of sales and operations.

Real-Time Analysis:

The company needed real-time insights to respond swiftly to market changes and customer demands.

Scalability

The current infrastructure struggles to handle the increasing volume and complexity of data.

Solutions

Data Collection:

Use AWS Glue to automate ETL processes. Write Python scripts to extract data from various sources and load it into Amazon S3.

Data Processing:

Use AWS Lambda functions with Python scripts for data cleaning and preprocessing. Store the processed data in Amazon S3 or feed it directly into Amazon Redshift for further analysis.

Machine Learning:

Use Amazon SageMaker to develop and train ML models.

Data Analysis:

Run SQL queries or Python-based analysis scripts to derive insights from the processed data.

Data Visualization:

Use Power BI to create interactive dashboards and reports.

Benefits

Enhanced Forecast Accuracy:

The ML-driven sales forecasts significantly improved prediction accuracy, reducing stockout and overstock scenarios.

Operational Efficiency:

Automation and real-time insights streamlined operations, reducing manual errors and delays.

Data-Driven Decision Making:

Unified data and advanced analytics enabled data-driven decisions across the organization.

Cost Savings:

Optimized inventory management and resource allocation led to cost savings and improved profitability.

Scalability and Flexibility:

The scalable AWS infrastructure handled the growing data volume and complexity, ensuring robust performance.

Conclusion

By leveraging AWS Data Engineering and Machine Learning services, the FMCG company successfully transformed its sales forecasting and operational processes. The integration of ML-driven insights into everyday operations empowered the company to make informed decisions, optimize resources, and enhance overall efficiency, driving business growth and profitability.