Cloud Computing Analytics for Category-Based Rate Forecasting
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Solution Overview
Problem
Existing systems lack the ability to intelligently determine rate values for different categories of goods and services based on historical transaction data, optimization levels, and user-defined aggressiveness, leading to inefficiencies in forecasting future expenditures and savings.
Innovation Solution
A computing system that utilizes historical transaction data to calculate average savings rates and standard deviation values, combined with optimization levels and user-defined aggressiveness, to determine intelligent rate values and forecast future savings, providing a graphical user interface for visualization and prioritization of categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical transaction data is analyzed to determine rate values, then forecasting accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the analysis by dividing transactions into different categories (e.g., procurement categories, service categories) and analyzing each category separately. This allows the system to handle complexity in a structured manner while maintaining forecasting accuracy for each segment.
Solution Approach 2:
The system changes parameters by incorporating multiple variables such as historical transaction amounts, frequency of transactions, seasonality factors, and user-defined aggressiveness levels. These parameter changes enable accurate rate value determination while managing system complexity through defined computational parameters.
2Reliability
If multiple categories are analyzed with optimization levels, then decision-making quality is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating statistics such as average transaction amounts, standard deviations, and optimization levels for each category. These pre-computed values are stored and reused, reducing computational requirements during actual decision-making while maintaining high decision-making quality.
Solution Approach 2:
The system implements self-service through automated analysis of historical data, automatic determination of optimization levels, and generation of rate values without requiring extensive manual intervention. This automation reduces computational overhead by efficiently processing multiple categories simultaneously.
3Adaptability or versatility
If user-defined aggressiveness parameters are incorporated, then customization capability is improved, but system complexity increases
Solution Approach 1:
The system implements dynamics by allowing user-defined aggressiveness parameters that can be adjusted based on organizational risk tolerance and strategic goals. The system dynamically adapts rate value calculations based on these user inputs, providing customization capability while managing complexity through parameter-based control.
Data Source
AI summary
Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives, from a client device, a request for information associated with a category. In response to the request, the program further accesses a storage to retrieve a first value associated with the category. The program also determines a set of values associated with the category based on a plurality of transactions. The program further determines an optimization level value associated with the category. The program also determines a second value associated with the category based on the first value, the set of values, and the optimization level value. The program further provides, by an application operating on the device, a graphical user interface (GUI) to the client device, the GUI comprising the second value.