Product Return Forecasting Using Computational Modeling
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Solution Overview
Problem
Companies face challenges in accurately forecasting whether high-tech products and their components will be returned, when they will be returned, and in determining the state and reusability of these returned components, due to the complexity of configurable, multi-generational products.
Innovation Solution
A computer-implemented method that maintains product profiles to track updated configurations of products and their components, builds and trains models to predict future returns, retention times, and reusable components based on quality indicators and reusability requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational models are used to predict product returns and reusability, then forecasting accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the product lifecycle management into distinct predictive modules: return prediction models, retention time prediction models, and reusability determination models. Each module handles a specific aspect of forecasting, breaking down the complex task into manageable components that can be developed and maintained independently while working together to provide comprehensive predictions.
Solution Approach 2:
The system performs preliminary actions by maintaining up-to-date product profiles that track configurations, quality indicators, and usage data throughout the product lifecycle. This preliminary data collection and modeling enables accurate predictions to be made before actual returns occur, allowing companies to prepare for remanufacturing and inventory management in advance.
2Measurement precision
If detailed product profiles are maintained to track configurations, then prediction accuracy improves, but data management complexity increases
Solution Approach 1:
The product profile serves multiple functions simultaneously: it tracks current configuration, maintains quality indicators, records usage data, and provides input for multiple different prediction models (return prediction, retention time, reusability). This multi-functional approach consolidates data management into a single system that supports various analytical needs without requiring separate tracking mechanisms for each function.
3Reliability
If companies accept more returned products for sustainability, then environmental benefit increases, but handling and processing complexity increases
Solution Approach 1:
The system performs preliminary determination of reusability and component quality before products are physically returned and handled. By predicting which components are reusable and their expected quality upon return, the system enables companies to prepare appropriate handling and processing procedures in advance, reducing the actual handling complexity when products are returned.
Solution Approach 2:
The predictive system enables companies to self-manage their return inventories more effectively by identifying which products to prioritize for remanufacturing, which components can be directly reused, and what quality standards to expect. This self-service capability reduces the need for extensive manual inspection and sorting of returned products.
Data Source
AI summary
Configurable product return determination includes maintaining product profiles indicating updated configurations of products and their components, building and training a set of models configured to identify products that are expected returns, predict retention times for such products, and determine, based on quality indicators of the products and on requirements for reusability of components, reusable components of the products, and, using the set of models and a product profile of a product in use, determining that the product in use is expected to be returned in the future and an expected timeframe of the return of the product in use, and determining one or more components, of a set of components of the product in use, that are reusable upon return of the product in the future.