Forecast Drilldown UI Using Mode Chaining for Granular Accuracy
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Solution Overview
Problem
Current forecasting systems are limited by data from a single data source or preconfigured entities, lacking flexibility and accuracy in providing granular level guidance for users.
Innovation Solution
A system that allows users to specify forecast values through customizable entity levels in a user interface drilldown, utilizing mode chaining information to query and fetch forecasts for sub-objects, enabling deeper entity relationships and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If forecasting systems use preconfigured entities and single data source, then system complexity is reduced, but forecast accuracy and granularity are limited
Solution Approach 1:
The system segments the forecasting functionality into configurable entity levels, allowing users to divide the forecast into hierarchical segments (e.g., regional, departmental, project levels). This enables granular forecasting accuracy without requiring a completely complex system redesign, as each segment can be configured independently based on user needs.
Solution Approach 2:
The forecasting system transitions from static preconfigured entities to dynamic configurable entity levels. Users can dynamically adjust the granularity and structure of entities according to different forecasting scenarios, allowing the system to adapt its complexity level based on the specific forecasting task rather than being fixed in structure.
2Adaptability or versatility
If forecasting systems provide granular level guidance, then forecast accuracy improves, but system complexity and configuration requirements increase
Solution Approach 1:
The system implements a universal entity level configuration framework that can serve multiple forecasting scenarios and granularities through a single configurable structure. This multi-functional approach allows the same system to provide both high-level and detailed forecasts by simply adjusting the entity level configurations, rather than requiring separate systems for each granularity level.
Solution Approach 2:
The forecasting system uses a nested hierarchical structure where entity levels are organized in parent-child relationships (e.g., region containing departments containing projects). This nesting allows users to drill down from high-level summaries to detailed granular forecasts systematically, making the complexity manageable through organized layers rather than presenting all granularity levels simultaneously.
3Measurement precision
If users can specify forecast values at multiple entity levels, then forecast precision increases, but data processing complexity increases
Solution Approach 1:
The system adds the entity level dimension to the forecasting data structure, organizing forecast data not only by time and category but also by hierarchical entity levels. This dimensional organization allows precise forecasts at multiple granularities without proportionally increasing processing complexity, as the hierarchical structure enables efficient aggregation and drill-down operations.
Data Source
AI summary
A system queries for entity data of a user from a first data source. The system displays a display panel in a user interface (UI), the display panel including measurement metrics for an account entity of the user from the entity data of the first data source. The system determines mode chaining information that specifies a chain of entity relationships between a number of entities in the entity data, where the relationships include one-to-one or one-to-many relationships. The system determines forecast information corresponding to the measurement metrics for the entities of the user. The system displays the forecast information as entity-based forecast values in the UI based on the mode chaining information, where the entity-based forecast values include a drill down of forecast values for the entities of the user.