Dynamic Data Relationship Management for Business Planning
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Solution Overview
Problem
Current business planning tools, such as spreadsheets and the Cube/Dimension/Link paradigm, are inflexible, difficult to maintain, and require specialist knowledge, leading to high setup and maintenance costs, making it challenging for small and medium-sized enterprises to implement comprehensive business planning models.
Innovation Solution
A data-processing apparatus that allows users to create and dynamically maintain relationships between operational and output data using line items with specified attributes, enabling easy customization and aggregation, reducing the need for specialist consultants and simplifying the adaptation of models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If spreadsheets are used as business planning tools, then they are inexpensive and easy to use for simple applications, but they are difficult to adapt for complex commercial enterprises and require significant time and effort to maintain
Solution Approach 1:
The system segments business planning functionality into modular components: operational data models, output data models, and configurable relationships between them. This allows the system to handle complex commercial enterprises through composition of simple, reusable building blocks while maintaining ease of use through standardized interfaces.
Solution Approach 2:
The system implements dynamic adaptability through configurable data models and relationships that can be modified without restructuring the entire system. Users can add, remove, or modify line items and their relationships dynamically, allowing the system to adapt to changing business requirements while maintaining simplicity.
2Adaptability or versatility
If the Cube/Dimension/Link paradigm is used to model complex commercial activity, then comprehensive business planning models can be built, but maintenance is difficult and requires specialist knowledge
Solution Approach 1:
The system uses templates and reusable data model patterns that can be copied and adapted for different business scenarios. Standard operational data models and output data models can be replicated across multiple projects, reducing the need for specialist knowledge while maintaining comprehensive modeling capability.
Solution Approach 2:
The system allows flexible configuration of data models through parameters and attributes rather than requiring structural changes to the underlying framework. Users can modify line item properties, relationships, and calculations through parameter adjustments, making maintenance accessible to non-specialists while preserving complex modeling capabilities.
3Adaptability or versatility
If models are built from scratch using the Cube/Dimension/Link paradigm, then custom business planning models can be created, but it is not possible to adapt existing models for different customers due to complexity
Solution Approach 1:
The system provides pre-built operational data models and output data models that can be configured for different customer scenarios. These templates contain common business planning patterns already established, allowing rapid deployment without building from scratch while maintaining full customization capability through configuration.
Solution Approach 2:
The system implements universal data models that can serve multiple business scenarios and customers. The operational data model and output data model frameworks are designed to be multi-functional, accommodating various business types and requirements through configuration rather than requiring separate custom-built models for each customer.
4Reliability
If specialist consultants are employed to develop and maintain models, then comprehensive business planning models can be created, but set-up and running costs become very high
Solution Approach 1:
The system enables end-users to independently create, configure, and maintain their own business planning models using intuitive tools and pre-built templates. This self-service capability eliminates the need for expensive specialist consultants while maintaining model quality through guided configuration processes and validation mechanisms.
Solution Approach 2:
The system uses lightweight, configurable data models that can be quickly created and modified without requiring expensive, specialized development resources. The modular architecture allows rapid prototyping and iteration using simple, adaptable components rather than complex, hard-to-modify structures.
Data Source
AI summary
A data-processing apparatus is provided. The data-processing apparatus creates and dynamically maintains relationships between operational data and output data. The data-processing apparatus has first line items for storing operational data. The first line items have operational attributes and at least one treatment attribute. The treatment attribute specifies qualifiers used to create qualified operational data. Impact attributes specify destinations for the qualified operational data in a set of second line items which is arranged to hold the output data. The data-processing apparatus is particularly suited to use as a business planning tool.