Decision Tree Configuration Engine for Solar Power System Pricing
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Solution Overview
Problem
Conventional pricing methodologies for solar power systems are limited in exploring viable pricing solutions due to the combinations of pricing parameters, making it difficult for sales experts to present optimal configurations to customers, especially when multiple parties are involved in financing arrangements.
Innovation Solution
A computer-implemented method using a decision tree to generate and display pricing solutions for solar power systems, allowing users to select pricing parameters and explore multiple configuration options through a graphical user interface, thereby expanding the number of available configurations and pricing solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional pricing methodologies are used by sales experts to manually explore pricing solutions, then the process is simple to operate, but the number of pricing solutions that can be explored is limited
Solution Approach 1:
A computer-based pricing system acts as an intermediary between the sales expert and the customer, automatically generating and evaluating multiple pricing solutions based on configured parameters. The system includes a pricing parameter configuration module, a pricing solution generation module, and an evaluation module that work together to explore millions of pricing combinations without requiring manual analysis by the sales expert.
Solution Approach 2:
The system systematically varies pricing parameters (down payment, lease term, annual rate adjustment, etc.) to generate different pricing solutions. By changing these parameters in combination with solar power system configurations, the system can explore a vast number of pricing scenarios automatically, presenting multiple viable options to the customer rather than relying on a single manual assessment.
2Adaptability or versatility
If the number of pricing parameters and configurations is increased to provide more options, then customer choice is improved, but the difficulty of selecting viable solutions increases
Solution Approach 1:
The pricing system incorporates an evaluation module that automatically assesses each generated pricing solution against predefined criteria and constraints. The system provides feedback on the viability of each pricing option, allowing the sales expert and customer to quickly identify suitable solutions without manually analyzing each combination. This feedback mechanism filters out non-viable options and highlights the most promising pricing configurations.
3Productivity
If manual pricing exploration is used, then the system is easier to operate, but the time required to present optimal configurations to customers increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring pricing parameters and constraints before the customer interaction. The pricing parameter configuration module sets up the evaluation criteria and acceptable ranges in advance, so that when pricing solutions are needed, the system can rapidly generate and evaluate options based on pre-established parameters, significantly reducing the time required during actual customer presentations.
Data Source
AI summary
A configuration engine traverses sequential levels of a decision tree in order to iteratively refine a configuration for a solar power system. At each level of the decision tree, the configuration engine determines the outcome of a design decision based on computing the result of a value function. The configuration engine explores configurations that optimize the value function result compared to other configurations, and may also discard less optimal configurations. When a current configuration is considered less optimal than a previous configuration generated at a previous level, the configuration engine discards the current configuration and re-traverses the decision tree starting with the previous configuration.