Enterprise Performance Measurement With Process-Output Alignment
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Solution Overview
Problem
Traditional systems for monitoring and reporting in large enterprises lack the ability to effectively measure and track process-output-response data, leading to inconclusive performance metrics and potential disasters, as they focus on fixed data sets and individual datum-point values without considering the impact of process inputs on key performance metrics.
Innovation Solution
A system that collects historical and real-time data using a statistical model to analyze enterprise-specific metrics, providing actionable feedback and recommending process improvements through an Integrated Enterprise Excellence (IEE) value chain, which aligns process output responses with their creation processes, and uses machine learning for strategic recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional systems collect and analyze fixed data sets and individual datum-point values, then data collection is simple and straightforward, but the performance metrics become inconclusive and fail to provide actionable insights
Solution Approach 1:
The system segments performance measurement into multiple dimensions including process inputs, process outputs, and process responses. By dividing the measurement approach into these distinct segments, the system can analyze each aspect separately and comprehensively, leading to more conclusive performance metrics that capture the full complexity of enterprise operations.
Solution Approach 2:
The patent transitions from traditional single-point data collection to multi-dimensional performance measurement by incorporating process inputs, outputs, and responses across multiple levels. This dimensional expansion allows the system to capture relationships and patterns that single-point measurements miss, providing actionable insights while managing complexity through structured organization.
2Loss of information
If traditional systems track data in silos and as individual datum-point values, then data collection is straightforward, but the systems fail to evaluate process output responses and their relationship to process inputs
Solution Approach 1:
The system merges previously siloed data collection efforts into an integrated measurement framework that captures process inputs, outputs, and responses together. By combining these elements into a unified system, the patent preserves critical process relationship information that would be lost in separate tracking approaches, while managing complexity through standardized data structures and relationships.
Solution Approach 2:
The patent implements feedback mechanisms that connect process outputs back to process inputs through measured relationships. This feedback loop allows the system to evaluate how process inputs influence outputs and responses, preserving information about causal relationships while using structured feedback pathways to manage the complexity of tracking these interconnections.
3Measurement precision
If traditional reporting systems do not numerically quantify organizational desires and customer specification requirements, then reporting is simpler, but the achievement of business and customer needs cannot be effectively measured
Solution Approach 1:
The system transforms qualitative organizational desires and customer requirements into quantifiable performance parameters. By changing the state of these metrics from descriptive to numerical, the patent enables precise measurement of achievement while maintaining ease of operation through automated data collection and standardized parameter definitions that simplify reporting.
Data Source
AI summary
Systems and methods for measuring, reporting, controlling, and improving enterprise performance are described. A system receives objective statements for an enterprise, which can be an IEE value chain of the enterprise, and enterprise-specific measurable metrics through an interactive user interface with their associated processes. The system collects historical data and real-time data (e.g., daily) associated with enterprise-specific measurable metrics from a plurality of infield resources and analyses the historical data and the real-time data using a statistical model to provide information that teams can use to determine the strengths and shortcomings of the enterprise. Based on the determined strength and shortcomings, the system provides, using a knowledge database, information so that leadership and teams can determine where to focus process-output metric improvement efforts via process improvement efforts so that the enterprise-as-a-whole financially benefits. Process output response measurements are reported, so there is alignment to the processes that created the output response. The system automatically assesses process-output responses in the IEE value chain for stability, using a statistical technique. If a process-output response is stable, a prediction statement is provided for the metric in the chart's report. If a prediction statement is undesirable, there is a “pull” for a process improvement effort that is to enhance the metric's response.