Production Resource Scheduling With Value-at-Risk Control
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Solution Overview
Problem
Current supply chain planning systems in dynamic business environments lack the ability to provide planners with systematic intelligence to quantify the financial impact of actions, leading to inadequate prioritization and potential counterproductive decisions due to insufficient insight into causal factors and financial metrics.
Innovation Solution
A machine-learning application trained with supply chain data predicts operational metrics, infers causal factors, and generates action recommendations, providing contextual data through intuitive user interfaces to support decision-making across the supply chain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional supply chain planning systems are used, then planners have access to basic operational data, but they lack systematic intelligence to quantify financial impact and prioritize actions effectively
Solution Approach 1:
The patent introduces machine learning models as intermediary components that process raw supply chain data and transform it into actionable financial insights. These models act as mediators between the complex supply chain operations and the planners, quantifying the financial impact of various actions without requiring planners to directly analyze complex operational data.
Solution Approach 2:
The patent replaces traditional manual analysis methods and basic reporting systems with advanced machine learning algorithms. Instead of relying on planners to manually assess financial impacts using basic tools, the system automatically computes financial metrics using ML models, substituting mechanical manual processes with intelligent automated systems.
2Measurement precision
If planners manually analyze voluminous supply chain data, then they can identify issues, but processing time increases significantly reducing ability to act promptly
Solution Approach 1:
The patent enables the supply chain system to self-analyze its own data through machine learning models. The system automatically processes voluminous supply chain data, identifies issues, and generates prioritized action recommendations without requiring manual planner intervention for data analysis, thereby reducing processing time while maintaining identification accuracy.
Solution Approach 2:
The patent replaces manual data analysis processes with automated machine learning systems. The ML models rapidly process voluminous supply chain data and identify issues much faster than manual analysis, substituting the time-consuming mechanical process of manual review with intelligent automated processing that maintains or improves identification accuracy.
3Productivity
If planners focus on global objectives within their operational silos, then they can optimize their specific functions, but they fail to achieve overall enterprise objectives
Solution Approach 1:
The patent creates a unified machine learning system that serves multiple supply chain functions simultaneously. The same ML infrastructure supports demand planning, inventory management, production scheduling, and logistics optimization, enabling planners at different levels to access consistent, enterprise-wide financial insights that align local decisions with global objectives.
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning system continuously monitors supply chain performance across all functions and provides actionable recommendations that align with enterprise-wide objectives. The system learns from outcomes and adjusts its recommendations to ensure that local optimization actions contribute to overall enterprise success rather than creating suboptimal siloed outcomes.
Data Source
AI summary
Methods and systems for controlling production resources in a supply chain are described. The system automatically generates predicted supply chain operational metrics across a nodes of a supply chain. The system automatically infers causal factors that impact the predicted supply chain operational metrics. The causal factors include a change to a utilization of the production resource. The system communicates a user interface including production runs being scheduled on the production resource including a user interface element representing the scheduling of the production run associated with a value at risk. The system receives input causing a change to the utilization of the production resource. The change to the utilization of the production resource impacts the predicted supply chain operational metrics including the value at risk associated with the scheduling of the production run.