Component Predeployment Using Failure and Order Probability Prediction
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Solution Overview
Problem
Conventional component advance deployment systems face challenges in accurately predicting machine malfunctions and customer needs, leading to issues like overstocking and stock shortages due to reliance on past abnormal state information and actual replacement rates.
Innovation Solution
A system that includes a server with a malfunction prediction section, area/customer characteristic estimation section, order reception probability calculation section, and advance deployment profit/loss calculation section, which uses operation information and customer data to predict malfunction probabilities and calculate the likelihood of component orders, thereby optimizing component deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems determine component names and quantities based on abnormal state information and past replacement rates, then the system can deploy components in advance, but this causes divergence between ordered and stocked components leading to overstocking and stock shortages
Solution Approach 1:
The system performs preliminary actions by predicting malfunctions before they occur and calculating order reception probabilities in advance. The malfunction prediction section analyzes operation information to forecast future component failures, and the order reception probability calculation section pre-calculates the likelihood of customers ordering specific components, enabling proactive inventory deployment rather than reactive restocking
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring operation information from machines and using this data to refine malfunction predictions and order probability calculations. The area/customer characteristic estimation section provides feedback loops that adjust predictions based on historical data and actual customer behavior patterns, improving inventory accuracy over time
2Productivity
If the system deploys components in advance based on past data, then it can improve machine productivity, but it cannot respond appropriately to changing situations causing needless advance deployment and delays
Solution Approach 1:
The system transitions from static, historical data-based deployment to dynamic, real-time adaptive deployment. The malfunction prediction section continuously updates predictions based on current operation information, and the order reception probability calculation section dynamically adjusts probabilities based on area and customer characteristics, allowing the system to adapt to changing conditions while maintaining productivity benefits
Solution Approach 2:
The system changes key parameters from fixed historical replacement rates to dynamic predictions based on real-time operation information. The malfunction prediction section generates time-varying failure probability estimates, and the order reception probability calculation section adjusts deployment quantities based on changing area and customer characteristics, enabling flexible response to varying situations
Data Source
AI summary
The present invention appropriately executes advance deployment of components. This component advance deployment assistance system includes a server. The server includes: a malfunction prediction section that predicts a malfunction of a machine based on operation information about the machine; an area/customer characteristic estimation section that estimates a characteristic of an area or a characteristic of a customer possessing or using the machine, based on data related to the machine possessed or used by a customer; an order reception probability calculation section that calculates an order reception probability that is a probability of receiving an order of a component associated with the machine, based on outputs from the malfunction prediction section and the area/customer characteristic estimation section; and an advance deployment profit/loss calculation section that calculates a profit/loss in a case of advance deployment of the component to a base near a location of the machine, based on the order reception probability.