Supply Chain Task-Network Resequencing After Process Anomalies
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Solution Overview
Problem
Conventional process monitoring systems fail to minimize the negative impact of anomalies on subsequent processes and tasks, leading to disruptions in sequential workflows.
Innovation Solution
A sequencing module dynamically adjusts the sequence order of processes based on detected anomalies using process mining techniques and discrete sequence-based methods, optimizing target parameters and adhering to policies, to complete tasks while minimizing disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional process monitoring systems are used to detect anomalies, then anomaly detection capability is provided, but the negative impact of anomalies on subsequent processes cannot be minimized
Solution Approach 1:
The system dynamically adjusts the sequence order of processes based on detected anomalies. The sequencing module generates alternative process flows in real-time, changing the execution order from static to dynamic adaptation, allowing the system to respond to anomalies by reordering subsequent processes to minimize disruption while maintaining productivity.
2Device complexity
If the original process sequence is maintained after anomaly detection, then process simplicity is preserved, but task completion efficiency deteriorates due to disruptions
Solution Approach 1:
The system segments the process flow into individual processes that can be independently reordered. When an anomaly is detected in one process, only the subsequent processes are resegmented and resequenced, while earlier processes remain unchanged. This selective segmentation maintains overall process simplicity while improving task completion efficiency through targeted reordering.
Solution Approach 2:
The system changes the parameter of process sequence order dynamically. By modifying the execution order parameter of subsequent processes based on anomaly detection, the system optimizes task completion efficiency without fundamentally changing the process structure or adding complex transformations, maintaining relative simplicity while improving productivity.
3Productivity
If alternative process flows are generated to minimize anomaly impact, then task completion efficiency is improved, but system complexity increases
Solution Approach 1:
The sequencing module implements dynamic sequence generation that adapts to anomalies in real-time. Rather than pre-defining multiple static alternative flows, the system dynamically computes optimal sequences based on current anomaly conditions, reducing the need to store and manage numerous predefined alternative process flows, thus controlling system complexity while improving task completion efficiency.
Data Source
AI summary
A system and method are provided including a memory storing processor-executable program code; and a processing unit to execute the processor-executable program code to: receive a first task map including a plurality of nodes, each node representing an executable process for completion of a task; identify a first node of the plurality of nodes as a starting node representing the first process for completion of the task; identify a second node of the plurality of nodes as the ending node representing the last process for completion of the task; generate a first sequence order of node execution for completion of the task based on a target parameter; identify an anomaly; generate a second sequence order of node execution for completing the task based on the target parameter; and execute the nodes in the generated second sequence order. Numerous other aspects are provided.