Arrival Time Prediction Using Historical Receipt Data
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Solution Overview
Problem
Inaccurate lead times provided by suppliers for component delivery result in components arriving either too early, occupying valuable inventory space, or too late, necessitating costly expedited shipping, leading to inefficiencies in manufacturing facilities.
Innovation Solution
A method that uses historical receipt data to predict arrival times by performing feature analysis and selecting relevant features for machine learning models, which are then used to modify schedules and ensure components arrive at the expected delivery time, thereby optimizing inventory management and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lead times provided by suppliers are used for scheduling, then components may arrive early, but this occupies valuable inventory space
Solution Approach 1:
The system performs preliminary analysis of historical receipt data and performs feature engineering to generate predictive models before scheduling components. This allows the system to predict actual arrival times in advance, enabling better scheduling decisions that prevent both early and late arrivals, thereby optimizing inventory space utilization.
Solution Approach 2:
The system continuously collects historical receipt data on actual component arrival times and uses this feedback to train and improve machine learning models. The models learn from past patterns to increasingly accurately predict future arrival times, enabling dynamic adjustment of scheduling decisions to minimize inventory space occupation while maintaining reliable delivery.
2Reliability
If lead times provided by suppliers are used for scheduling, then components may arrive late, but this requires costly expedited shipping
Solution Approach 1:
The system performs preliminary predictive analysis using trained machine learning models before components are scheduled for delivery. By predicting actual arrival times in advance based on historical patterns, the system can proactively adjust schedules to prevent late arrivals, thereby avoiding the need for costly expedited shipping interventions.
Solution Approach 2:
The system uses historical receipt data as feedback to continuously improve prediction accuracy. By learning from past delivery patterns, the models become increasingly reliable at predicting arrival times, enabling the system to make accurate scheduling decisions that prevent late arrivals and eliminate the need for expensive corrective expedited shipping actions.
3Measurement precision
If machine learning models with feature analysis are implemented, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the complex prediction task into distinct phases: data preprocessing, feature engineering to generate additional features from historical receipt data, feature selection to identify the most relevant features, and model training. This segmentation allows each phase to be optimized independently, managing computational complexity while maintaining high prediction accuracy.
Solution Approach 2:
The system extracts and selects only the most relevant features from the comprehensive set of historical receipt data. By performing feature selection, the system removes redundant and less important features, reducing the dimensionality of the input data and thereby decreasing computational complexity while preserving the essential information needed for accurate arrival time prediction.
Data Source
AI summary
Methods, systems, and non-transitory computer readable medium for predicting arrival time of components based on historical receipt data. A method includes receiving historical receipt data corresponding to features. The method further includes performing feature analysis to generate additional features for the historical receipt data. The method further includes selecting a first set of features including at least one of the additional features. The method further includes predicting, based on the first set of features, an arrival time for one or more components of a manufacturing facility. The method further includes causing, based on the predicted arrival time, modification of a schedule in a file associated with the one or more components of the manufacturing facility.