Process-Driven Analysis Engine for Business Operations Optimization
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Solution Overview
Problem
Current business operations monitoring and management systems lack the ability to easily define and analyze abstract business processes, predict future metric values, and automate the optimization of these processes, requiring significant manual coding and labor.
Innovation Solution
A method and platform for process-driven analysis that defines abstract processes, computes metric values, builds analysis and prediction models, and optimizes processes automatically, using a metric definer, metric computation engine, analysis and prediction engine, and process improvement engine to provide comprehensive monitoring and optimization capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual coding and programming is used to define metrics and analyze business processes, then analysis precision and customization can be achieved, but the effort, time, and complexity increase significantly
Solution Approach 1:
The system enables business analysts to define metrics and perform analysis without requiring programming expertise. The metric definer and analysis engine allow users to create custom metrics through intuitive interfaces, automatically generating the necessary code and executing analyses without manual programming intervention.
Solution Approach 2:
Manual coding and programming tasks are replaced by automated computational engines. The metric computation engine, analysis engine, and prediction engine automatically execute complex calculations and analyses that would otherwise require extensive manual programming, substituting mechanical coding work with automated processing.
2Reliability
If comprehensive analysis and prediction capabilities are implemented, then business insight and optimization improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The system divides comprehensive analysis capabilities into separate functional modules: metric definer, metric computation engine, analysis engine, and prediction engine. Each module handles a specific aspect of analysis independently, making the overall complex system more manageable and easier to implement through modular architecture.
Solution Approach 2:
The analysis engine and prediction engine serve multiple functions within a unified system. They can perform various types of analyses (descriptive, diagnostic, predictive) and work with different metrics and processes, providing versatile capabilities that reduce the need for separate specialized tools for each analysis type.
3Productivity
If automated process optimization is implemented, then productivity and metric improvement accelerate, but the initial setup and configuration effort increases
Solution Approach 1:
The system performs preliminary configuration by allowing users to define abstract processes, metrics, and optimization goals in advance through the metric definer and process simulator. Once configured, the optimization engine can automatically execute optimization routines without requiring repeated manual setup, reducing initial configuration time through reusable templates and predefined optimization strategies.
Data Source
AI summary
A method of process-driven analysis of operations includes defining an abstract process, defining at least one metric over the abstract process using a metric definer and computing metric values using a metric computation engine. The method further includes building an analysis model and a prediction model using an analysis and prediction engine to provide analysis on the computed metric values and optimizing the abstract process based on the computed metric values.