Predictive Capacity Optimizer for Computing Resource Allocation
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Solution Overview
Problem
Current predictive analytics systems lack the ability to effectively optimize computing capacity utilization across business entities by predicting future opportunities and assigning resources accordingly, leading to potential overcommitment and suboptimal use of computing resources.
Innovation Solution
A computer-implemented method that predicts future business opportunities and their signatures, ranks entities based on their ability to fulfill these opportunities, and optimally assigns them to maximize computing capacity utilization, avoiding overcommitment by accounting for predicted demands and current opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predictive analytics systems assign resources based on current opportunities only, then resource allocation is simple and quick, but computing capacity utilization becomes suboptimal and overcommitment occurs
Solution Approach 1:
The system performs preliminary actions by predicting future opportunities before they occur and pre-allocating computing resources accordingly. The predictive analytics engine forecasts upcoming opportunities and the system proactively reserves or releases computing capacity in advance, preventing both overcommitment and underutilization before they happen.
Solution Approach 2:
The system dynamically adjusts resource allocation based on predicted future conditions rather than static current states. Computing capacity assignments are continuously optimized as new predictions arrive, allowing the system to adapt resource distribution in real-time to match anticipated demand patterns.
2Productivity
If the system predicts and assigns entities to future opportunities, then computing capacity utilization improves, but the complexity of resource management increases
Solution Approach 1:
The system introduces an intermediary predictive analytics layer between current resource states and future opportunity requirements. This intermediary engine translates complex future demand patterns into actionable current resource allocation decisions, simplifying the management interface while improving outcomes.
Solution Approach 2:
The system implements feedback loops where actual opportunity outcomes are compared against predictions, and this information feeds back into refining the predictive model. This continuous learning mechanism improves accuracy over time, making the increasingly complex system more efficient at resource allocation.
3Reliability
If the system accounts for predicted demands in entity assignment, then overcommitment is prevented, but the time required for assignment decisions increases
Solution Approach 1:
The system performs preliminary predictions and preliminary resource reservations in advance, so that when actual assignment decisions are needed, much of the analytical work is already complete. This reduces real-time decision complexity and speed up the final assignment process.
Solution Approach 2:
The system dynamically balances prediction depth against decision speed by adjusting the level of forecasting detail based on time constraints and opportunity urgency. For time-critical assignments, the system uses streamlined prediction models that provide sufficient accuracy without the full computational overhead.
Data Source
AI summary
Embodiments of the invention are directed to techniques that include predicting, by a computer system, a number of predicted opportunities and signatures of the predicted opportunities expected in a time window. Based on the signatures of the predicted opportunities, the computer system generates a listing of entities ranked according to signatures of the predicted opportunities. The computer system selects the entities to be assigned to the predicted opportunities based, at least in part, on computing capacity related to sales while accounting for any current opportunities having been assigned to the entities.