Redeployable Resource Forecasting via State Segmentation
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Solution Overview
Problem
Current forecasting methods fail to accurately model and predict the usage and redeployment of resources that can be reused across different time periods, leading to inefficiencies in supply chain management.
Innovation Solution
A method and system that determine the probability of resource usage and assignment by modeling the state of redeployable resources, including their assignment to jobs, and generating reports on probable utilization, enabling better forecasting and resource allocation over a given time horizon.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current forecasting methods are used for consumable products, then the interaction between supply and demand is easy to model, but the methods fail to accurately model and predict the usage and redeployment of resources that can be reused across different time periods
Solution Approach 1:
The patent segments the resource forecasting problem by introducing distinct state variables (assigned, available, retired) to represent different phases in the resource lifecycle. This segmentation allows the model to track resources through their various states across time periods, enabling accurate prediction of redeployable resource availability while maintaining manageable model complexity through structured state transitions.
Solution Approach 2:
The patent applies dynamics by modeling resource states as time-dependent variables that transition between different conditions. The forecast dynamically updates resource availability based on probability distributions for state transitions, allowing the model to adapt to changing resource conditions across multiple time periods rather than treating resources as static or consumable.
2Productivity
If a resource is assigned to a job, then it satisfies demand for that job, but the resource cannot be used again in other time periods unless it is redeployable
Solution Approach 1:
The patent implements feedback by continuously tracking the state of each resource and using this information to update forecasts for future time periods. The model incorporates feedback loops where resource assignment outcomes in one period inform availability predictions in subsequent periods, enabling optimized resource allocation that maximizes utilization while minimizing idle time through adaptive reassignment strategies.
Solution Approach 2:
The patent applies preliminary action by forecasting resource availability in advance across multiple time periods. The model predicts future resource states before assignments are made, allowing planners to proactively optimize resource allocation schedules. This advance forecasting enables resources to be strategically reassigned before idle periods occur, maximizing productivity and reducing unnecessary downtime.
Data Source
AI summary
A method, computer program product and a computer system for forecasting resource usage is provided. A processor determines a job forecast. The processor determines a probability of a future usage for a first resource, wherein the first resource is currently assigned to a first job. The processor determines an assignment of a second resource to a forecasted job of the plurality of jobs, wherein the second resource is available for assignment to the forecasted job. The processor determines a probable utilization of the second resource, wherein the probable utilization of the second resource indicates the probability that the second resource will be deployed during assignment to the forecasted job. A processor, in response to the probable utilization of the second resource being below a predetermined value, generates a report including the assignment state of the second resource and probable utilization of the second resource by the forecasted job.