Intelligent Forecasting with Peer Benchmark Data
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Solution Overview
Problem
Current workforce management and forecasting solutions are limited to individual organizations' historical data, failing to provide accurate and informed forecasts due to data limitations and inaccuracies when analyzing dissimilar data, which restricts the ability to meet service goals and manage resources effectively.
Innovation Solution
The implementation of an intelligent forecaster system that incorporates de-identified and anonymized benchmark data from similar organizations to generate more informed forecasts and resource plans, utilizing a cloud-computing platform with APIs to compile, anonymize, and analyze data from peer organizations, enabling the development of benchmark-plans that can be compared to entity plans for improved resource allocation and service goal optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current workforce management solutions use only individual organization's historical data, then the system complexity is low, but the forecasting accuracy and resource management effectiveness deteriorate
Solution Approach 1:
The patent introduces an intermediary benchmarking system that aggregates and anonymizes data from multiple organizations. This intermediary layer enables organizations to access peer performance data without direct competition or data sharing conflicts, thereby improving forecasting accuracy while managing system complexity through structured data aggregation and anonymization processes
Solution Approach 2:
The system creates a universal benchmarking platform that serves multiple organizations simultaneously. By building a shared database of anonymized performance data across industries and organizations, the system enables each organization to benefit from collective data while maintaining their own distinct forecasting models, thus improving overall accuracy without requiring each organization to develop complex standalone systems
2Quantity of substance
If the system analyzes data from dissimilar organizations, then the quantity of available data increases, but the measurement precision and reliability of forecasts deteriorate
Solution Approach 1:
The patent applies local quality by segmenting the benchmarking data into relevant categories and subsets based on organizational characteristics, industry sectors, and performance parameters. This allows the system to aggregate data from many organizations (increasing quantity) while maintaining precision by analyzing only comparable, relevant data subsets for each specific forecasting scenario
Solution Approach 2:
The system segments the large dataset into meaningful categories and groups organizations based on similarities in size, industry, and operational characteristics. This segmentation enables the system to process and analyze data from numerous organizations while maintaining forecast accuracy by focusing on relevant peer comparisons rather than treating all data uniformly
3Reliability
If the system incorporates benchmark data from peer organizations, then the forecasting accuracy improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The system implements self-service by enabling organizations to automatically access and utilize benchmark data through standardized interfaces. Organizations can retrieve relevant peer performance data, compare it with their own metrics, and adjust their forecasts without requiring complex manual data processing or analysis, thus improving reliability while managing processing complexity through automated queries and comparisons
Data Source
AI summary
A method for providing benchmark-plans to a customer based on benchmark information, comprising receiving a customer-defined service goal and a demand forecast for the customer; generating, with a planner, a plan for achieving the customer-defined service goal based on the demand forecast; determining a benchmark category from a plurality of benchmark categories that the customer belongs to based on at least an industry of the customer, wherein the benchmark category defines a plurality of other customer-defined service goals for other customers participating in at least the industry as the customer; determining benchmark service goals based on the determined benchmark category; executing the planner for each of the benchmark service goals thereby generating benchmark-plans for the demand forecast for the customer; and outputting, to the customer, the plan and the benchmark-plans, wherein the benchmark-plans are different from the plan.