Quote to Cash Code Generation via Universal Data Model
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Solution Overview
Problem
Current approaches to generating quote-to-cash (Q2C) solutions are time-consuming and require significant manual effort, especially when dealing with multiple vendors and clients, leading to scalability issues and lengthy onboarding processes.
Innovation Solution
A system and method that automates the generation of Q2C solutions by creating a data model from a universal template, mapping external data, and generating code based on business rules, allowing for rapid customization and deployment across various environments, including blockchain and cloud platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual approaches are used to generate Q2C solutions for multiple vendors and clients, then customization and accuracy can be maintained, but development time and complexity increase significantly
Solution Approach 1:
The patent uses code templates as reusable copies that can be instantiated multiple times for different vendors and clients. These templates contain pre-defined business logic and data structures that can be customized through configuration rather than rewriting code, thus maintaining accuracy while reducing development time from weeks to hours.
Solution Approach 2:
The patent creates a universal data model and code templates that serve multiple functions across different vendors and clients. The same template framework handles pricing, quoting, invoicing, and cash management for various clients, eliminating the need for separate manual development for each client while maintaining customization through configurable parameters.
2Adaptability or versatility
If manual customization is performed for each client in the quote-to-cash process, then specific client requirements can be met, but onboarding time and operational complexity increase
Solution Approach 1:
The patent performs preliminary action by pre-defining code templates, data models, and business logic structures before client onboarding. These templates are prepared in advance with common Q2C functionalities already implemented, so when a new client joins, the system can quickly instantiate and configure the solution rather than building from scratch, thus simplifying the onboarding process while maintaining adaptability.
Solution Approach 2:
The patent segments the Q2C solution into modular components represented by code templates and data models. Each template represents a separable functional unit that can be independently configured and combined. This segmentation allows the system to maintain high adaptability by selecting and configuring only the necessary modules for each client while reducing operational complexity through standardized interfaces and assembly processes.
3Reliability
If traditional development methods are used for Q2C solutions, then thorough testing and validation can be performed, but the onboarding process becomes lengthy and costly
Solution Approach 1:
The patent uses validated code templates that have been pre-tested and verified for reliability. These templates encapsulate proven business logic and data handling routines that have already undergone thorough validation. By copying these validated templates rather than writing new code for each client, the system maintains reliability while dramatically increasing onboarding speed and reducing costs.
Data Source
AI summary
Systems, computer-implemented methods and/or computer program products that facilitate generating code for template specification in a quote to cash (Q2C) solution are provided. In one embodiment, a computer-implemented method comprises: generating, by a system operatively coupled to a processor, a data model by specifying from a universal template which fields and attributes to include in the data model; mapping, by the system, external data into the data model; and generating, by the system, a code for template specification based on the data model, one or more business rules and a first code template.