18182483. OUTLIER AND INLIER ANOMALY DETECTION AND CORRECTION FOR MATERIAL REQUIREMENTS PLANNING simplified abstract (Dell Products L.P.)
Contents
OUTLIER AND INLIER ANOMALY DETECTION AND CORRECTION FOR MATERIAL REQUIREMENTS PLANNING
Organization Name
Inventor(s)
Rohit Gosain of Bangalore (IN)
Shibi Panikkar of Bangalore (IN)
OUTLIER AND INLIER ANOMALY DETECTION AND CORRECTION FOR MATERIAL REQUIREMENTS PLANNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18182483 titled 'OUTLIER AND INLIER ANOMALY DETECTION AND CORRECTION FOR MATERIAL REQUIREMENTS PLANNING
Simplified Explanation: The patent application describes a methodology for correcting forecasts for component items based on outlier anomalies.
Key Features and Innovation:
- Receiving forecasts for a product and a component item
- Identifying outlier anomalies in the forecast for the component item
- Computing outlier corrected forecast quantities for the component item
- Correcting the forecast for the component item based on the computed outlier corrected forecast quantity
- Computing percentage deviations for total corrected forecast quantities
- Correcting inlier anomalies in the forecast for the component item
Potential Applications: This technology could be applied in supply chain management, inventory forecasting, and production planning.
Problems Solved: This technology addresses inaccuracies in forecasting component items, which can lead to inefficiencies in production and inventory management.
Benefits:
- Improved accuracy in forecasting component items
- Enhanced production planning and inventory management
- Reduction in wastage and excess inventory
Commercial Applications: The technology could be utilized by manufacturing companies, retailers, and distributors to optimize their supply chain operations and improve overall efficiency.
Prior Art: Readers can explore prior art related to forecasting methodologies, supply chain management, and inventory optimization.
Frequently Updated Research: Stay updated on the latest research in forecasting techniques, anomaly detection, and supply chain optimization to enhance the application of this technology.
Questions about the Technology: 1. How does this methodology improve forecasting accuracy? 2. What are the potential implications of using outlier correction in supply chain management?
Original Abstract Submitted
An example methodology includes receiving a forecast for a product and a forecast for a component item needed to make the product. The method also includes, responsive to a determination that the forecast for the component item is an outlier anomaly, computing an outlier corrected forecast quantity for the component item and correcting the forecast for the component item to be the computed outlier corrected forecast quantity for the component item. The method can further include computing a percentage deviation between a total corrected forecast quantity and a quantity forecasted for the component item and, responsive to a determination that the forecast for the component item is an inlier anomaly, computing an inlier corrected forecast quantity for the component item and correcting the forecast for the component item to be the computed inlier corrected forecast quantity for the component item.
