Compensation Modeling Staging Entity and Guideline Table
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Solution Overview
Problem
The complexity of determining employee compensation across various factors such as job location, performance levels, and position ranges in enterprises, along with the need for flexible and efficient compensation modeling, is not adequately addressed by existing methods.
Innovation Solution
An employee compensation modeling approach that generates a model from a stored template, populating it with HR data to calculate per-employee allocations, allowing for flexible updates and recommendations based on user inputs, utilizing an in-memory database for efficient processing and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compensation modeling methods are used, then compensation planning can be conducted, but the process becomes complex and time-consuming due to multiple factors including job location, performance levels, and position ranges
Solution Approach 1:
The compensation model is segmented into distinct components: a staging entity storing employee data and attributes, a guideline table containing compensation formulas, and a modeling engine that processes these segments. This segmentation allows independent management and optimization of each component, reducing overall system complexity while improving planning efficiency.
Solution Approach 2:
The staging entity acts as an intermediary between the HR database and the compensation modeling engine. It pre-processes and structures employee data, attributes, and base compensation values before they enter the modeling engine, simplifying the processing workload and reducing the time required for compensation planning.
2Measurement precision
If detailed per-employee compensation calculations are performed, then accurate compensation allocations are achieved, but processing time increases significantly
Solution Approach 1:
The staging entity performs preliminary actions by pre-calculating and storing base compensation values, employee attributes, and guideline associations before the actual modeling process. This pre-processing ensures that when the modeling engine executes, it works with already-structured data, maintaining accuracy while significantly reducing processing time.
Solution Approach 2:
The system changes parameters by storing compensation formulas as configurable guidelines in the guideline table rather than hardcoding calculations. This allows the modeling engine to efficiently apply different compensation scenarios by simply changing which guidelines are active, rather than reprocessing entire compensation structures, thus maintaining precision while reducing computational overhead.
3Adaptability or versatility
If the compensation model structure is made flexible to accommodate different organizational methods, then adaptability improves, but system complexity increases
Solution Approach 1:
The compensation model is made dynamic through the staging entity, which allows flexible assignment and reassignment of guideline formulas to different employee groups. The system can dynamically adjust compensation parameters, attributes, and calculations without requiring structural changes to the underlying database or model framework, thus improving adaptability while maintaining system simplicity.
Solution Approach 2:
The staging entity serves multiple functions: storing employee data, managing attributes, linking to guideline formulas, and facilitating various compensation calculation methods. This multi-functionality allows a single unified structure to support different organizational compensation approaches without requiring separate specialized systems, thereby improving versatility without proportionally increasing complexity.
Data Source
AI summary
An employee compensation modeling approach generates a model from a stored template. The model template may include fields for eligibility rules, performance ratings, guidelines, exchange rates, pay ranges, and other information. A modeling engine then populates that model with specific data from a human resources (HR) database, according to the structure of the template. Examples of HR data stored in an underlying database can include employee names, job codes, pay grades, physical job locations, base salaries, and other compensation-relevant information. The resulting compensation model includes a name, a description, target(s), and other information including allocation(s). The compensation model is displayed on an interface configured to receive further user inputs for purposes of model exploration and/or update. In certain embodiments, the HR database may be an in-memory database, with the template and/or engine also being present within the in-memory database in order to leverage its processing power and storage capacity.