Supply Chain Inventory Control Using Value-at-Risk Predictions
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Solution Overview
Problem
Current supply chain planning systems lack the ability to provide supply chain planners with systematic intelligence to quantify the financial impact of actions, leading to inadequate prioritization and decision-making, especially in fast-moving and complex environments, due to insufficient insight into root causes, noisy data, and lack of objective metrics for prioritization and impact analysis.
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 electronic user interfaces that include alert metrics, value at risk, inventory days of supply, and capacity metrics, allowing for intuitive decision-making in a global supply chain system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional supply chain planning systems are used, then planners have access to basic information and tools, but the information is voluminous and noisy leading to long processing time and insufficient time to act or prioritize
Solution Approach 1:
The system extracts only the most relevant and actionable information from the voluminous supply chain data using machine learning algorithms. It identifies and surfaces key insights such as root causes of issues, predicted operational metrics, and high-impact actions, filtering out noisy data to provide planners with concentrated, high-value information that requires minimal processing time
Solution Approach 2:
The patent replaces manual information processing and analysis with automated machine learning systems. The ML application automatically processes supply chain data, generates predictions, identifies causal factors, and prioritizes actions, substituting the mechanical manual analysis process with an intelligent automated system that delivers insights rapidly without human intervention in the data processing stage
2Measurement precision
If planners rely on intuition and experience, then they have operational insight, but they lack systematic intelligence to quantify the financial impact of actions
Solution Approach 1:
The machine learning application serves as an intermediary between raw supply chain data and planner decision-making. It systematically processes data through trained models to generate quantified financial impact predictions for various actions, translating complex data patterns into actionable financial insights that complement planner intuition without requiring them to understand the underlying complexity
Solution Approach 2:
The system transforms qualitative planner intuition into quantitative financial metrics by using machine learning models that predict specific operational outcomes (delivery dates, inventory levels, capacity utilization) and their financial impacts. This parameter transformation allows planners to evaluate actions based on objective financial measurements rather than relying solely on subjective experience
3Adaptability or versatility
If planners focus on global objectives within their operational silos, then they can optimize their specific function, but they cannot understand the impact of actions on the overall enterprise
Solution Approach 1:
The machine learning application provides a universal platform that serves multiple supply chain functions simultaneously - demand planning, inventory management, production scheduling, and logistics. It generates enterprise-wide predictions and action recommendations that are relevant across all operational silos, allowing planners to see how their specific actions impact overall enterprise objectives while maintaining the ability to optimize their specific functions
Solution Approach 2:
The system adds an enterprise-wide dimension to siloed planner views by incorporating cross-functional data and predictions. It shows how actions in one area (e.g., production scheduling) impact other areas (e.g., inventory levels, delivery dates, capacity utilization), creating a multi-dimensional view that connects operational silos to overall enterprise performance without requiring complex manual integration
4Ease of operation
If planners use existing information and tools, then they have access to current data, but the tools do not facilitate prioritizing actions based on financial metrics
Solution Approach 1:
The machine learning application performs preliminary analysis and prioritization of actions before planners need to make decisions. It pre-calculates financial impacts, identifies high-value actions, and presents prioritized recommendations, allowing planners to immediately focus on the most impactful actions without having to analyze and prioritize from scratch
Solution Approach 2:
The system replaces manual action prioritization based on planner judgment with automated machine learning-based prioritization based on quantified financial metrics. The ML model objectively ranks actions by their predicted financial impact, substituting subjective prioritization with data-driven objective prioritization that is consistently applied across all decisions
Data Source
AI summary
Methods and systems for controlling inventory in a supply chain are described. The system receives supply chain data including input signals comprising operational plans and observed supply chain operational metrics. The system automatically generates predicted supply chain operational metrics including a value at risk that is predicted for a product. The system automatically infers causal factors including a shipment of the product. The causal factors impact the predicted supply chain operational metrics. The system communicates a user interface for shipments of the product and the system receives input causing a change to a shipment of the product impacting the predicted supply chain operational metrics including the value at risk for the first product.