Dynamic Enterprise Planning Model for Unplanned Event Response
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Solution Overview
Problem
Conventional enterprise planning systems (EPS) are suboptimal in accounting for and simulating unplanned events such as natural disasters, civil unrest, and other disruptions, which can significantly impact supply chain operations and profitability.
Innovation Solution
An unplanned event system that includes solver logic and a planning model, allowing for the representation and simulation of unplanned events, enabling the generation of optimal or immediate response plans by modifying entity attributes and freezing existing plans to prevent pre-anticipation of disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional EPS uses linear programming and heuristics to build plans, then steady state operations can be optimized, but the system becomes suboptimal when accounting for unplanned events
Solution Approach 1:
The system transitions from static conventional EPS to a dynamic planning system that can adapt to unplanned events. The solver logic dynamically adjusts the planning model by applying event data to modify entity attributes, allowing the system to respond to changing conditions while maintaining operational efficiency.
Solution Approach 2:
The system changes parameters by applying unplanned event data to modify entity attributes in the planning model. This allows the plan to adapt to new conditions by adjusting parameters such as supply availability, demand patterns, or operational constraints based on the unplanned event scenario.
2Reliability
If the system applies unplanned event data to modify entity attributes, then response plans can be optimized, but the system may pre-anticipate disruptions which is incorrect
Solution Approach 1:
The system performs preliminary actions by preparing the planning model with the capability to accept and process unplanned event data. However, the actual modification of entity attributes only occurs when event data is applied, not in advance, preventing pre-anticipation while maintaining readiness to respond.
Solution Approach 2:
The system uses feedback by applying unplanned event data to the planning model and generating updated response plans based on the event outcome. This closed-loop approach ensures plans are optimized based on actual event data rather than speculation, improving accuracy without pre-anticipation.
3Device complexity
If conventional EPS models supply chain entities with fixed attributes, then planning is computationally efficient, but the system cannot account for entity attribute changes due to unplanned events
Solution Approach 1:
The system segments the planning model into a base model with fixed attributes and an event-driven modification layer. This allows the core model to remain computationally efficient while the event data application layer provides flexibility to adjust entity attributes when unplanned events occur.
Solution Approach 2:
The planning model serves multiple functions: it operates as a conventional efficient planner for steady state operations and simultaneously acts as a dynamic response system when unplanned event data is applied. This multi-functionality allows the same model structure to handle both fixed and flexible planning scenarios.
Data Source
AI summary
Systems, methodologies, media, and other embodiments associated with planning a response to an unplanned event are described. One example computer implemented method includes solving a model with no unplanned events applied, modifying the model by applying an unplanned event to the model, freezing a plan up to the unplanned event effective date, and solving the modified model. A plan associated with the modified model may then be provided as an output.