Value Chain Planning via Agent Recruitment and Reward Simulation
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Solution Overview
Problem
Existing value chain planning systems lack the ability to automatically configure high-efficient plans for tasks across multiple services and business entities, particularly in environments requiring resilience and ESG considerations.
Innovation Solution
A value chain planning apparatus and system that recruits, registers, and simulates execution modules as agents, measuring KPIs and rewarding based on execution results to dynamically link participating business entities from material procurement to sales, incorporating ESG evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual planning methods are used for value chain tasks, then flexibility and adaptability are maintained, but automation extent and productivity are insufficient
Solution Approach 1:
The patent introduces an intermediary system comprising agent recruitment units, agent registration units, and reward payment processing units that mediate between the planning objective and the execution of value chain tasks. These intermediary components automatically manage the recruitment, registration, and coordination of execution modules (agents), thereby achieving automatic plan configuration without requiring direct complex manual intervention for each task assignment.
Solution Approach 2:
Execution modules (agents) are designed to autonomously perform value chain tasks once recruited and registered. The system enables self-service by allowing execution modules to independently execute tasks and receive rewards based on their performance, reducing the need for continuous manual oversight and complex coordination mechanisms.
2Productivity
If existing mediation systems are used for service fees distribution, then multi-layer company participation is supported, but automated value chain task planning across multiple services is not achieved
Solution Approach 1:
The system performs preliminary actions by recruiting and registering execution modules (agents) in advance before actual value chain tasks are executed. The agent recruitment unit and agent registration unit prepare the pool of available agents beforehand, and the plan preparation unit conducts simulations to determine optimal task assignments, thereby reducing configuration time when actual tasks need to be executed.
Solution Approach 2:
The reward payment processing unit implements a feedback mechanism where execution modules receive rewards based on their task execution results. This feedback loop incentivizes efficient task completion and provides performance data that can be used to improve future task assignments and plan configurations, thereby increasing productivity over time.
Data Source
AI summary
There is provided a value chain planning apparatus for a value chain from material procurement to sales of a product in which at least two or more business entities participate. The value chain planning apparatus includes an agent recruitment unit that recruits an execution module that is an agent that realizes a task included in the value chain, an agent registration unit that registers the execution module as the agent when receiving an application including the execution module, a plan preparation unit that executes the execution module for two or more tasks included in the value chain, performs a simulation of executing the task, and measures a KPI, and a reward payment processing unit that pays a reward corresponding to an execution result to the executed execution module.