Unnamed Manufacturing Organization

The organization relied on traditional statistical forecasting models that consistently fell short of the accuracy needed for manufacturing and supply chain decisions. This led to frequent over-forecasting (excess inventory and carrying costs) and under-forecasting (product shortages, lost sales, and material waste). The solution also needed to operate automatically and integrate via APIs with internal platforms.

JBS Dev designed and deployed an AI-driven forecasting platform on AWS using a generative AI time-series model trained and hosted on Amazon SageMaker. Historical product-level sales data is continuously ingested into Amazon S3, and the model generates demand forecasts exposed via secure APIs. AWS Lambda orchestrates data processing and automation, keeping forecasts current as new data arrives.

The new model consistently achieved 85–90% forecast accuracy, significantly outperforming all previously tested statistical and machine-learning methods. This enabled the client to reduce excess inventory and carrying costs, minimize waste from under-forecasting, and integrate forecasting intelligence directly into operational systems for faster inventory planning decisions.

• Generative AI models can match or exceed traditional ML and statistical techniques for time-series forecasting tasks.
• AWS managed services (SageMaker, S3, Lambda) enable rapid deployment, efficient scaling, and seamless enterprise integration.
• Automating the full forecast pipeline eliminates manual intervention and ensures forecasts stay current as new data is introduced.