AI Attach Rate Forecasting for Configure-to-Order Supply Planning
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Solution Overview
Problem
The challenge in supply planning for configure-to-order (CTO) systems lies in accurately forecasting customer-selected components, as there are no discernible ordering patterns, leading to low forecast accuracy and increased safety stock requirements, unlike the finished goods assembly (FGA) model which has relatively accurate demand planning due to pre-configured systems.
Innovation Solution
Implementing an AI-based supply planning management system that utilizes historical data and machine learning algorithms to predict component attachments, correlating forecasting results from non-customizable and customizable systems to generate accurate supply plans for CTO systems, thereby improving forecast accuracy from less than 40% to 75-85%.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a configure-to-order (CTO) model is used to enable customer customization, then customer flexibility and adaptability are improved, but forecast accuracy deteriorates due to no discernible ordering patterns
Solution Approach 1:
The patent segments the ordering process into two distinct models: Configure-to-Order (CTO) for customized components and Finished Goods Assembly (FGA) for pre-configured systems. By segmenting the data into these two categories, the system can apply appropriate forecasting methods to each, thereby maintaining customer flexibility while improving forecast accuracy for each segment separately.
Solution Approach 2:
The patent introduces a merge center as an intermediary between CTO and FGA models. The merge center consolidates customized components from CTO orders into pre-configured finished goods, enabling accurate demand planning at the aggregate level while preserving customer customization options at the component level.
2Adaptability or versatility
If a configure-to-order (CTO) model is used for component customization, then product versatility is improved, but safety stock requirements increase due to low forecast accuracy
Solution Approach 1:
The patent segments inventory management into CTO-specific components and FGA finished goods. By segmenting the inventory planning, the system can maintain lower safety stock for CTO components through accurate attach rate forecasting, while managing FGA inventory separately with its own demand patterns.
Solution Approach 2:
The patent performs preliminary attach rate analysis and forecasting before finalizing the supply plan. By predicting component attachment rates in advance using historical data and machine learning, the system can determine optimal safety stock levels for customized components, reducing excess inventory while maintaining service levels.
3Device complexity
If traditional forecasting methods are used for CTO systems, then simplicity of the forecasting process is maintained, but forecast accuracy deteriorates to less than 40%
Solution Approach 1:
The patent uses machine learning algorithms as intermediaries between historical data and forecast results. These algorithms automatically analyze patterns in historical CTO and FGA data, correlating results to generate accurate attach rate forecasts without requiring complex manual forecasting processes.
Solution Approach 2:
The patent implements an automated supply planning system that self-adjusts and self-optimizes forecasting accuracy. The system automatically ingests historical data, performs attach rate analysis, generates forecasts, and continuously improves accuracy through machine learning, eliminating the need for manual forecasting adjustments.
4Measurement precision
If an AI-based supply planning system is implemented to improve forecast accuracy, then forecast accuracy is improved to 75-85%, but system complexity increases
Solution Approach 1:
The patent implements a universal AI-based platform that handles multiple forecasting functions: CTO attach rate forecasting, FGA demand forecasting, and integrated supply planning. This multi-functional system consolidates various forecasting tasks into a single platform, improving accuracy while managing complexity through integration rather than proliferation of separate systems.
Solution Approach 2:
The AI-based supply planning system is designed to be self-service, automatically ingesting historical data, performing attach rate analysis, generating forecasts, and continuously improving accuracy through machine learning. This automation eliminates the need for complex manual processes and specialized expertise, making the sophisticated system easier to operate and maintain.
Data Source
AI summary
Techniques for automated supply planning management are disclosed. For example, a method obtains a first data set representing historical data associated with a non-customizable system, a second data set representing historical data associated with a customizable base system, and a third data set representing historical data associated with components used to customize the customizable base system. The method pre-processes at least portions of the first data set, the second data set and the third data set, and then performs forecasting processes respectively on the pre-processed portions of the first data set, the second data set and the third data set. Results of the forecasting processes are correlated and the forecasting results associated with the third data set are modified based on variations in one or more of the forecasting results associated with the first data and the forecasting results associated with the second data.