View source for 17939984. SYSTEM AND METHOD FOR SUPPLIER RISK PREDICTION AND INTERACTIVE RISK MITIGATION IN AUTOMOTIVE MANUFACTURING simplified abstract (Honda Motor Co., Ltd.)

=SYSTEM AND METHOD FOR SUPPLIER RISK PREDICTION AND INTERACTIVE RISK MITIGATION IN AUTOMOTIVE MANUFACTURING=


==Organization Name==

[[:Category:Honda Motor Co., Ltd.|Honda Motor Co., Ltd.]]


[[Category:Honda Motor Co., Ltd.]]

==Inventor(s)==

[[:Category:Patricia Wollstadt of Offenbach (DE)|Patricia Wollstadt of Offenbach (DE)]][[Category:Patricia Wollstadt of Offenbach (DE)]]

[[:Category:May Markusic of Marysville OH (US)|May Markusic of Marysville OH (US)]][[Category:May Markusic of Marysville OH (US)]]

[[:Category:Lydia Fischer of Offenbach (DE)|Lydia Fischer of Offenbach (DE)]][[Category:Lydia Fischer of Offenbach (DE)]]

[[:Category:Stefan Menzel of Offenbach (DE)|Stefan Menzel of Offenbach (DE)]][[Category:Stefan Menzel of Offenbach (DE)]]

==SYSTEM AND METHOD FOR SUPPLIER RISK PREDICTION AND INTERACTIVE RISK MITIGATION IN AUTOMOTIVE MANUFACTURING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17939984 titled 'SYSTEM AND METHOD FOR SUPPLIER RISK PREDICTION AND INTERACTIVE RISK MITIGATION IN AUTOMOTIVE MANUFACTURING

==Simplified Explanation==

The method described in the patent application aims to mitigate the effects of shipment time delays of intermediate products and materials in a manufacturing process by using historical supplier data, process parameters, and external data to predict time delay risks.

* Machine learning algorithm is applied to generate a model for predicting time delay risks based on historical supplier data, process parameters, and external data.
* The model is recorded in a database and used in the application phase to predict actual time delay risks based on current process parameters and external data.
* An output signal including the predicted actual time delay risk is generated and outputted to a user via a human machine interface.

== Potential Applications ==
This technology could be applied in various manufacturing industries where shipment time delays of intermediate products and materials can impact production schedules and efficiency.

== Problems Solved ==
This technology helps in identifying and predicting potential time delay risks in the manufacturing process, allowing for proactive measures to be taken to mitigate the impact of such delays.

== Benefits ==
The use of machine learning algorithms and predictive models can help manufacturers optimize their production processes, reduce downtime, and improve overall efficiency by addressing potential time delay risks in advance.

== Potential Commercial Applications ==
Potential commercial applications of this technology include manufacturing plants, supply chain management companies, and logistics firms looking to improve their operations by better managing time delay risks in their processes.

== Possible Prior Art ==
One possible prior art for this technology could be predictive maintenance systems used in manufacturing to anticipate equipment failures and prevent downtime. These systems also utilize historical data and machine learning algorithms to predict potential issues before they occur.

=== What are the specific process parameters used in the machine learning algorithm for predicting time delay risks?
The specific process parameters used in the machine learning algorithm are observable for the manufacturing process and are obtained during the training phase of the method.

=== How does the method handle changes in external data during the application phase?
The method obtains current external data during the application phase and uses it along with the recorded model to predict actual time delay risks, allowing for real-time adjustments based on the most up-to-date information available.


==Original Abstract Submitted==

A method for mitigating effects of a shipment time delay of intermediate products and materials in a manufacturing process comprises, in a training phase, obtaining historical supplier data of the intermediate products and materials, obtaining process parameters of the manufacturing process that are observable for the manufacturing process, and obtaining external data that are independent from the manufacturing process. The method applies a machine learning algorithm to generate a model for predicting a time delay risk in the manufacturing process based on the historical supplier data, the process parameters of the manufacturing process and the external data, and records the generated model in a database. In an application phase, the method obtains current process parameters of the manufacturing process and current external data, and proceeds by predicting an actual time delay risk based on the recorded model for predicting a time delay risk and the current process parameters of the manufacturing process and the current external data. The method generates an output signal including the predicted actual time delay risk, and outputs, via a human machine interface, the generated output signal to a user.



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