Data-Driven Model for Material Flow Delivery Prediction
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Solution Overview
Problem
Material flow systems face challenges in obtaining precise and complete data from suppliers, leading to quality losses and reduced effectiveness in material flow simulation, production planning, and order management.
Innovation Solution
A computer-aided procedure generates a data-driven model using machine learning to process digital data from material flow systems, enabling the creation of a production plan for time and/or quantitative control of technical systems by predicting expected delivery information based on historical data and events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual data collection from suppliers is used, then data completeness can be improved, but time consumption and processing complexity increase
Solution Approach 1:
The system automatically collects and processes delivery data from suppliers without requiring manual intervention. The machine learning model autonomously predicts expected delivery times by processing historical data and event information, eliminating the need for manual data collection while maintaining data completeness.
Solution Approach 2:
The patent replaces manual data collection processes with automated machine learning-based prediction systems. The machine learning model substitutes for manual analysis by automatically processing historical delivery data, supplier information, and event data to generate expected delivery time predictions.
2Productivity
If machine learning-based prediction is used, then data processing speed is improved, but model complexity and computational requirements increase
Solution Approach 1:
The system processes data by transforming it into appropriate formats and structures that machine learning models can effectively process. Historical delivery data, supplier information, and event data are converted into structured parameters that enable efficient machine learning prediction while managing computational complexity.
3Measurement precision
If historical data is used for training, then prediction accuracy is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The system extracts and processes only the necessary historical data elements required for accurate predictions. By selectively extracting relevant features from historical delivery data, supplier information, and event data, the system maintains prediction accuracy while reducing unnecessary data storage requirements.
Data Source
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AI summary
The invention describes a method for the computer-aided generation of a data-driven model (MO) for the computer-aided processing of digital data from a material flow system (MFS) for the temporal and/or quantitative control of a technical system (TS) by means of a production plan (PP). To generate the production plan (PP), provision information is processed from at least one supplier (L1, L2, L3) of a direct supplier network (LN). The generation of the data-driven model comprises, in step i), the provision of a training data set (TDS) that, for a multitude of past deliveries, includes at least one target provision information (SLD, SLM), at least one actual provision information (ILD, ILM), and at least one event (EVT) that causes or can cause a deviation of the actual provision information (ILD, ILM) from the target provision information (SLD, SLM).In step ii), a machine learning (ML) method is trained using the training data set (TDS), wherein the machine learning (ML) method includes an input for reading in at least one planned future delivery information (SLD, SLM) and at least one event (EVT) before or around the planned delivery time, and an output for outputting expected delivery information (ELD, ELM) as the delivery information, wherein the trained machine learning (ML) method represents the generated data-driven model (MO).