Automated Forecast Profile Selection for Supply Chain Demand
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Solution Overview
Problem
Conventional demand forecasting in managed supply chains often requires manual intervention to select suitable forecast models and parameters, especially when dealing with time-varying historical data, which is inefficient and labor-intensive.
Innovation Solution
A computerized method and system that automatically determines a forecast profile by performing tests to identify the significance of different forecast models and iteratively determining the optimal parameters, reducing the need for manual intervention and enhancing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used to select forecast models and parameters, then forecast accuracy can be maintained through expert judgment, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The system performs self-service by automatically selecting forecast models and determining optimal parameters through iterative testing and evaluation, eliminating the need for manual expert intervention while maintaining forecast accuracy through automated performance assessment
Solution Approach 2:
The system automatically adjusts and optimizes forecast parameters through iterative determination based on test outcomes, transforming the manual parameter selection process into an automated optimization routine that evaluates multiple parameter combinations to identify the most accurate configuration
2Productivity
If automated forecast processes are implemented, then productivity and efficiency are improved, but manual intervention is still required for model and parameter selection
Solution Approach 1:
The system achieves complete automation by performing self-service model selection and parameter optimization through automated testing and evaluation, eliminating all manual intervention requirements while maintaining high forecasting efficiency through systematic automated processes
Solution Approach 2:
The system implements feedback mechanisms where forecast test outcomes are automatically analyzed to determine the significance of models and guide iterative parameter determination, creating a closed-loop automated process that continuously refines model selection and parameter optimization without human input
3Measurement precision
If iterative parameter determination is performed, then forecast precision is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by conducting forecast tests to identify significant models before proceeding to iterative parameter determination, eliminating wasted computational effort on insignificant models and reducing overall processing time while maintaining parameter optimization accuracy
Solution Approach 2:
The system segments the parameter determination process by focusing iterative optimization only on parameters associated with statistically significant models identified through preliminary testing, dividing the computational task into manageable segments that reduce total processing time while preserving optimization precision
Data Source
AI summary
Systems and methods are disclosed for forecasting demand for objects, such as products, parts, etc. in a managed supply chain. In one embodiment, a method for forecasting demand is provided that comprises the step of determining a forecast profile including a forecast model and a forecast parameter to be assigned to a set of data forming the basis of the forecast. The determining step may include the steps of performing at least one forecast test on the set of data to identify the significance of a forecast model in the set of data, and determining iteratively the value of a forecast parameter, wherein the forecast parameter is determined based on the outcome of performing the at least one forecasting test. Further, the method may include the step of automatically assigning the determined forecast profile to the set of data.