Returned Product Repair Forecasting for Material Stock Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manufacturers face challenges in managing spare parts and materials due to varying return demands from clients across different countries, soaring logistics and repair costs, and the need to meet client agreements, which affect inventory management and client satisfaction.
Innovation Solution
A calculation system that utilizes deep learning to analyze historical data of returned products, evaluates repair benefits, and predicts material requirements using clustering algorithms and predictive models to optimize inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manufacturers maintain excessive stock of finished materials at maintenance centers, then repair readiness is improved, but storage costs and obsolete product value increase
Solution Approach 1:
The system performs preliminary actions by predicting future return product quantities and required materials before the actual repair demand occurs. The calculation unit analyzes historical data to forecast material requirements, and the determination unit pre-identifies which materials should be stocked at maintenance centers, enabling proactive inventory management rather than reactive stocking
Solution Approach 2:
The system enables self-service by automatically calculating and determining material requirements without manual intervention. The calculation unit autonomously processes historical return data to predict future needs, and the determination unit automatically identifies materials with repair benefits, eliminating the need for manual inventory assessment and enabling maintenance centers to manage their own material requirements efficiently
2Quantity of substance
If manufacturers maintain insufficient stock of finished materials at maintenance centers, then storage costs are reduced, but urgent transportation costs increase and client satisfaction decreases
Solution Approach 1:
The system implements feedback by continuously analyzing historical return product data to update predictions of future material requirements. The calculation unit processes past return patterns and feeds this information back into the prediction model, allowing the system to adapt to changing return demands and adjust material recommendations accordingly, ensuring optimal stock levels are maintained
Solution Approach 2:
The system performs preliminary actions by forecasting material requirements before actual repair demands arise. By analyzing historical data trends, the calculation unit predicts future return quantities and the determination unit identifies required materials in advance, enabling maintenance centers to have materials ready before urgent transportation situations occur
3Adaptability or versatility
If manufacturers do not filter returned products by repair benefits, then all returned products are considered for repair, but calculation accuracy of material requirements decreases
Solution Approach 1:
The determination unit segments returned products into distinct categories based on repair benefits - identifying which products have positive repair value and which do not. This segmentation allows the system to focus calculations only on products that should be repaired, separating the decision-making process into: (1) products with repair benefits that require material calculation, and (2) products without repair benefits that should be discarded or handled differently
Data Source
AI summary
A calculation system has a receiving unit, a calculation unit, a determination unit, and an output unit. The receiving unit of the calculation system is used to receive data of historically returned products. The calculation unit of the calculation system is used to calculate the number of each returned product in the next time period based on the data of the historically returned products. The determination unit of the calculation system is used to select the returned products with repair benefits in the next time period, and to calculate the quantity of each material required for repairing the returned products with repair benefits in the next time period, so as to generate data of the quantity of each material. The output unit of the calculation system is used to output the data of the quantity of each material.