Reinforcement Learning Capacity Planning for Purchase Orders
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Solution Overview
Problem
Organizations face inefficiencies and increased costs due to understaffing in purchase order processing, as manual capacity planning is inadequate in handling fluctuations in purchase orders and user agent working hours, especially in large organizations with complex environments.
Innovation Solution
A capacity planning manager utilizing artificial intelligence and reinforcement learning to automatically adjust user agent working hours by learning an optimal action selection policy, minimizing understaffing through an actor-critic architecture that updates models over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual capacity planning is used to adjust user agent working hours, then the organization can handle changes in purchase orders and staffing, but the process becomes inefficient and costly in large organizations with complex environments
Solution Approach 1:
The patent replaces manual capacity planning (mechanical human operation) with an automated machine learning system that uses reinforcement learning to optimize user agent working hours. The system automatically processes complex capacity planning decisions by substituting human manual analysis with algorithmic decision-making based on historical and real-time data.
Solution Approach 2:
The capacity planning system performs self-service by automatically adjusting user agent working hours without requiring manual intervention. The machine learning model continuously learns from data and autonomously generates capacity planning recommendations, enabling the system to serve itself rather than relying on external manual planning processes.
2Productivity
If the amount of user agents and working hours is increased to handle more purchase orders, then processing capacity improves, but staffing costs increase
Solution Approach 1:
The patent implements dynamic adjustment of user agent working hours based on real-time demand fluctuations. Instead of maintaining fixed or constantly increased staffing levels, the system dynamically optimizes working hours to match actual purchase order volumes, allowing the organization to scale capacity up or down as needed without permanently increasing headcount.
Solution Approach 2:
The system changes the parameter of working hours allocation based on learned patterns and current state conditions. By adjusting working hours as a variable parameter rather than a fixed quantity, the organization can optimize processing capacity while controlling staffing costs through data-driven parameter optimization rather than linear increases in headcount.
3Adaptability or versatility
If manual capacity planning methods are used, then implementation is simple, but the system cannot adapt to fluctuations in purchase orders and working hours in complex environments
Solution Approach 1:
The patent implements a feedback-driven capacity planning system that continuously monitors purchase order volumes, user agent performance, and working hours data. The reinforcement learning model uses this feedback to continuously improve its predictions and adjustments, enabling the system to adapt to fluctuations while managing complexity through iterative learning rather than complex manual rules.
Solution Approach 2:
The system performs preliminary actions by proactively adjusting user agent working hours before demand fluctuations fully impact processing capacity. The machine learning model predicts future demand patterns and takes preventive capacity adjustment actions, allowing the organization to adapt to changes more effectively while maintaining manageable system complexity through anticipatory rather than reactive planning.
Data Source
AI summary
Techniques described herein relate to a method for performing capacity planning services. The method includes obtaining a current CP state from a client; in response to obtaining the current state: selecting an action based on the current CP state; providing the action to the client, wherein the client performs the action; in response to providing the action: obtaining a new CP state and a headcount associated with the action; calculating a reward based on the headcount and a reward formula; storing the current CP state, the action, the new CP state, and the reward as a learning set in storage comprising a plurality of learning sets; and performing a learning update using a portion of the plurality of learning sets to generate an updated actor, an updated critic, an updated target actor, and an updated target critic.