Machine Learning Resource Shortage Prediction Using KPI Signals

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Solution Overview

Problem

Existing systems fail to accurately and efficiently predict resource shortages, particularly in critical areas like healthcare, leading to inefficiencies and potential harm due to unforeseen supply disruptions.

Innovation Solution

A system utilizing machine learning to analyze key performance indicators and generate likelihoods of future shortages by training a supply shortage labeling operation and model, enabling proactive alerts and resource management.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional methods are used to monitor resource availability, then system complexity is low, but prediction accuracy of resource shortages is insufficient

Engineering Contradiction:

Improveprediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical monitoring systems with machine learning models that analyze key performance indicators and transaction datasets. The system uses algorithms to predict resource shortages by processing multiple data sources including inventory levels, order patterns, and supply chain metrics, achieving high prediction accuracy without physical intervention.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces key performance indicators as intermediary variables that bridge raw transaction data and shortage predictions. These KPIs serve as mediators that transform complex datasets into meaningful signals for the machine learning model, improving prediction accuracy while maintaining manageable system complexity through structured data transformation.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If comprehensive data analysis is performed to predict shortages, then prediction accuracy improves, but computational time and processing speed increase

Engineering Contradiction:

Improveshortage prediction accuracyVSAvoidcomputational processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary actions by continuously collecting and pre-processing transaction data, maintaining ready-to-analyze datasets of key performance indicators. This pre-processing work is done in advance so that when shortage prediction is needed, the machine learning model can quickly process pre-organized data without extensive real-time computation, reducing processing time while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent extracts only the most relevant key performance indicators from comprehensive transaction datasets for prediction analysis. By selecting and focusing on critical variables such as inventory turnover rates, order fulfillment patterns, and supply lead times, the system achieves high prediction accuracy using a subset of data rather than processing entire datasets, thereby reducing computational time.

Inventive Principle:

Principle #2Taking out (Extraction)

3Difficulty of detecting and measuring

If real-time monitoring of all resources is implemented, then detection capability improves, but system resource consumption increases

Engineering Contradiction:

Improvedetection capabilityVSAvoidsystem resource consumption

Core Design Contradiction:

Difficulty of detecting and measuringVSUse of energy by moving object

Solution Approach 1:

The patent applies local quality by focusing monitoring efforts on specific high-risk resources identified through the machine learning model rather than uniformly monitoring all resources. The system detects shortages by analyzing key performance indicators for prioritized resources where prediction accuracy is most critical, reducing overall system resource consumption while maintaining high detection capability for important resources.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The machine learning model performs self-service by automatically identifying which resources require monitoring based on learned patterns from historical data. The system autonomously determines detection priorities and allocates computational resources accordingly, eliminating the need for manual configuration and reducing overall system resource consumption while maintaining high detection capability.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12598146B2Systems, methods, and apparatuses for predicting resource shortages using machine learning

Publication Date: 2026.04.07 PREMIER HEALTHCARE SOLUTIONS

AI summary

Systems, methods, and apparatuses are described herein for predicting resource shortages using machine learning. The present invention is configured to identify a resource identifier(s) from a resource database; receive a resource transaction dataset associated with the resource identifier(s); identify a key performance indicator(s) from the resource transaction dataset; generate a key performance signal(s), wherein the key performance signal(s) comprises a plurality of values of the key performance indicator(s) over a predetermined period; apply the key performance signal(s) to a supply shortage labeling operation; identify a resource shortage event(s) associated with the resource identifier(s); apply the key performance signal(s) and the resource shortage event(s) to train a supply shortage machine learning model; and generate, by the supply shortage machine learning model, a likelihood of a future-shortage for the resource identifier(s) which is based on the at least one key performance signal for at least one point in time.