Supply Chain Prediction Platform for On-Time Delivery
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Solution Overview
Problem
Conventional manufacturing systems face challenges in ensuring on-time delivery (OTD) due to complex supply chains with system incompatibilities, poor communication, and lack of cohesive interactions between suppliers and buyers, leading to delays and disruptions.
Innovation Solution
A processor-implemented method and system that generates a 'part persona' using data models, predicts initial and successive on-time delivery intentions, and ensures OTD by collecting and analyzing supplier, fulfillment, and buyer data, employing AI and Q-Learning models to manage supply chain interactions and provide proactive alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional supply chain management platforms are used with isolated suppliers, then supplier independence is maintained, but system compatibility and communication efficiency deteriorate
Solution Approach 1:
The patent introduces a centralized supply chain management platform that acts as an intermediary between isolated suppliers and the manufacturing company. This platform collects, standardizes, and integrates status information from multiple suppliers using different IT systems, enabling cohesive interaction without requiring suppliers to change their existing systems. The platform mediates data exchange between incompatible systems through standardized interfaces and protocols.
2Adaptability or versatility
If multiple IT enterprise systems are involved in part sourcing, then supplier flexibility is maintained, but system compatibility and communication efficiency deteriorate
Solution Approach 1:
The patent implements a universal supply chain management platform that can interface with multiple different IT enterprise systems simultaneously. The platform provides multi-functional capabilities to handle various data formats, communication protocols, and system architectures through standardized integration layers, enabling seamless interaction between diverse supplier systems without requiring system-specific customization for each supplier.
3Device complexity
If manual data sharing and offline conversations are used, then system simplicity is maintained, but communication efficiency and tracking capability deteriorate
Solution Approach 1:
The patent replaces manual data sharing mechanisms (mechanical/system) with automated digital data collection and processing systems. The platform automatically gathers status information from supplier systems, standardizes data formats, and makes information accessible to all stakeholders through centralized access points, eliminating the need for manual data entry, offline conversations, and physical document exchange while significantly improving communication efficiency and tracking capability.
4Device complexity
If conventional supply chain monitoring is used, then implementation simplicity is maintained, but ability to predict and prevent delays deteriorates
Solution Approach 1:
The patent implements predictive analytics capabilities that analyze historical data, current status information, and external factors to forecast potential delays before they occur. The system performs preliminary assessments of supply chain risks, identifies bottlenecks in advance, and enables proactive decision-making to prevent delays. This allows the manufacturing company to take corrective actions ahead of time rather than reacting to problems after they occur.
Data Source
AI summary
The disclosure generally relates to methods and systems for ensuring the on-time delivery (OTD) of a product by a manufacturing company. According to the present disclosure, a part persona for each part is generated using a data model, by placing the part as an central entity and the one or more influencing factors of each part that affect the OTD of the product are captured in the part persona. A trained intent and OTD prediction model is built and used to predict an initial intent and an initial OTD for each part, based on the corresponding part persona. Further, the trained intent and OTD prediction model is used to predict a successive intent and a successive OTD for each part, based on the conversations and the events. Hence the OTD of the product is accurately predicted and which is useful to ensuring the OTD of the product.