AI Reverse Logistics Sub-Process Sequencing
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Solution Overview
Problem
Current reverse logistics systems are inefficient due to unnecessary human checks and resource utilization, requiring additional resources and lacking complete information, leading to siloed processes that hinder optimal decision-making.
Innovation Solution
A computer system and method utilizing an artificial intelligence algorithm engine to dynamically determine and prioritize sub-processes in reverse logistics, improving efficiency by removing human discretion and sequencing eligible sub-processes based on received data and contextual milestones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional reverse logistics systems use human checks and manual processes, then decision-making can be made with human discretion, but the system becomes inefficient and requires additional resources
Solution Approach 1:
The system enables self-service through automated AI algorithms that independently determine the sequence and prioritization of sub-processes without human intervention. The AI engine autonomously analyzes return data, evaluates variations, and sequences sub-processes based on pre-stored databases, eliminating the need for human checks while maintaining operational effectiveness.
Solution Approach 2:
Manual human decision-making processes are replaced with an automated AI-based mechanical system. The AI algorithm engine substitutes human discretion with computational analysis, using pre-stored databases and automated prioritization logic to determine sub-process sequences, thereby improving efficiency while removing the bottleneck of manual intervention.
2Loss of information
If traditional reverse logistics systems perform comprehensive checks and analysis, then complete information can be obtained, but unnecessary resource utilization occurs
Solution Approach 1:
The system performs preliminary action by pre-storing databases of variations and sub-process information before they are needed. The AI engine has pre-access to comprehensive data about potential return scenarios, product variations, and sub-process sequences, allowing it to quickly retrieve and apply relevant information without performing exhaustive real-time analysis, thus reducing resource consumption while maintaining information completeness.
Solution Approach 2:
The system applies partial action by selectively analyzing only the specific variations and sub-processes relevant to each return case. Instead of performing comprehensive checks on all possible scenarios, the AI engine focuses on evaluating only the applicable sub-processes based on the received data and pre-stored databases, obtaining complete relevant information without the resource cost of unnecessary comprehensive analysis.
3Device complexity
If traditional reverse logistics systems use fixed process sequences, then implementation is simple, but the system cannot adapt to different return scenarios
Solution Approach 1:
The system implements dynamic process sequencing where the sequence of sub-processes is not fixed but adapts based on the specific return scenario. The AI algorithm engine dynamically determines the optimal sequence by analyzing received data against pre-stored databases of variations, allowing the process flow to change automatically according to product type, return reason, and other relevant factors, thereby achieving high adaptability while managing complexity through automation.
Solution Approach 2:
The system changes parameters dynamically by adjusting the sequence and prioritization of sub-processes based on varying input conditions. The AI engine modifies process parameters such as sub-process order, prioritization levels, and evaluation criteria according to the specific return scenario, enabling the system to adapt to different return variations while maintaining operational simplicity through automated parameter adjustment.
4Ease of operation
If traditional reverse logistics systems rely on siloed processes, then each department can operate independently, but optimal decision-making is hindered
Solution Approach 1:
The system merges previously siloed departmental processes into a unified AI-driven workflow. The AI algorithm engine consolidates data from multiple sources and coordinates sub-processes across different functional areas, ensuring that decision-making considers all relevant factors simultaneously. This integration maintains operational independence at the execution level while achieving optimal decision-making through centralized intelligent coordination.
Data Source
AI summary
Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises receiving data associated with an initiation of a process; dynamically determining a sequence of a plurality of sub-processes associated with the received data based the initiated process; dynamically prioritizing the determined sequence of the plurality of sub-processes associated with the received data based on an analysis of eligible variations and a pre-stored database of variations associated with each respective sub-process; dynamically performing the process based the prioritized sequence of the plurality of sub-processes associated with the received data; and transmitting a result of the dynamic prioritization of the determined sequence of a plurality of variations to a user interface of another computing device.