- Manufacturing
AI-Driven Sales Forecasting on AWS for Manufacturing Inventory Optimization
JBS Dev built an AWS-based AI forecasting system for a manufacturer to improve forecast accuracy and reduce inventory risk and cost.
Product-level demand forecasts
85–90% forecast accuracy in the published project
Amazon SageMaker + APIs
- Sales history
- Demand forecasts
- Inventory planning
AT A GLANCE
The Project at a Glance
The Challenge
A manufacturer of high-volume, product-specific goods relied on traditional statistical forecasts that kept missing. Over-forecasting left excess inventory, and under-forecasting caused shortages, lost sales, and waste.
What We Built
JBS Dev built an automated forecasting platform on AWS. A generative AI time-series model on Amazon SageMaker reads sales history from Amazon S3, with AWS Lambda and Python, and serves forecasts through APIs.
The Result
The model reached 85 to 90% forecast accuracy, better than the statistical and machine learning methods tested before. The client now carries less excess inventory and makes inventory decisions faster.
THE CLIENT
Customer Overview
The client is a manufacturing organization that produces high-volume, product-specific goods for a diverse set of industries. Accurate sales forecasting is critical to their operations, as even small forecasting errors can result in significant excess inventory costs or missed revenue opportunities.
THE PROBLEM
Business Challenge
The organization needed a more accurate and reliable way to forecast product-level demand to determine the optimal inventory levels to purchase and produce. Their existing forecasting approaches relied on traditional statistical models that consistently fell short of the accuracy required to support real-world manufacturing and supply chain decisions.
As a result, the organization frequently faced two costly outcomes:
- Over-forecasting, which led to excess inventory and increased carrying costs
- Under-forecasting, which resulted in product shortages, lost sales, and material waste
Beyond accuracy, the business required a solution that could operate automatically as new data became available and integrate seamlessly with their internal software platforms through APIs.
CONSTRAINTS
Technical Constraints
Several constraints shaped our solution design:
- Existing forecasting methods were not adaptable enough to handle complex product-level demand patterns
- The solution needed to work with existing data pipelines and legacy systems
- Forecasting outputs had to be programmatically accessible via APIs for integration into in-house applications
- Manual intervention needed to be minimized to support scalability and operational efficiency
THE SOLUTION
Solution Overview
A modern, AI-driven forecasting platform was designed and deployed using AWS. The solution leverages a specialized generative AI time-series forecasting model trained and hosted on Amazon SageMaker.
Historical sales data for individual products is continuously ingested and stored on Amazon S3. The SageMaker-hosted model analyzes this data to generate highly accurate demand forecasts, which are exposed via secure APIs. These APIs allow our client’s internal systems to consume forecasts in near real time, enabling smarter inventory planning decisions across the organization.
The entire workflow is automated, ensuring forecasts remain up to date as new sales data is introduced.
- Amazon S3 Used as the central data repository for historical sales data and model inputs, providing scalable, durable storage optimized for analytics and machine learning workflows.
- Amazon SageMaker Used to train, host, and deploy the generative AI forecasting model. SageMaker model endpoints expose forecasts via APIs, enabling real-time access from internal systems.
- AWS Lambda Used to orchestrate data processing and automation tasks, triggering model interactions and supporting lightweight transformation logic without the need for dedicated infrastructure.
- Python was used for data processing, model training, and integration logic, enabling rapid development and flexibility throughout the project lifecycle.
THE OUTCOME
Results and Business Impact
The solution delivered a dramatic improvement in forecasting accuracy compared to all previous approaches. The new AWS-hosted generative AI model consistently achieved 85–90% forecast accuracy, significantly outperforming previously tested traditional statistical and machine-learning methods.
This improvement enabled our client to:
- Reduce excess inventory and associated carrying costs
- Minimize waste caused by under-forecasting
- Make faster, more confident inventory planning decisions
- Integrate forecasting intelligence directly into operational systems
LESSONS LEARNED
Lessons Learned
One of the key takeaways from this project is that generative AI models can be highly effective for time-series forecasting, often matching or exceeding the performance of traditional machine-learning and statistical techniques. When combined with AWS’s managed services, these models can be deployed quickly, scaled efficiently, and integrated seamlessly into existing enterprise environments.
KEEP READING
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