Adaptive Inventory Control via Opportunity Cost Simulation
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Solution Overview
Problem
Network-based service providers face challenges in predicting consumer demand and supplier capacity, leading to stochastic inventory planning, resulting in under or overuse of inventory capacities, especially during peak demand periods like holidays, where manual and ad-hoc processes fail to optimize capacity use over a time horizon.
Innovation Solution
Implementing an adaptive capacity control tool and a multi-period ordering model that simulate supply and demand across a time horizon, generating opportunity costs to optimize inventory decisions, allowing for early and late purchasing to balance capacity constraints, and considering future constraints to make informed purchase decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual and ad-hoc inventory planning processes are used, then simplicity of operation is maintained, but inventory capacity optimization deteriorates during peak demand periods
Solution Approach 1:
The system enables automated inventory planning where the computing system independently simulates supply and demand, generates opportunity costs, and determines optimal inventory decisions without requiring manual intervention. The system serves itself by automatically adjusting inventory plans based on simulated outcomes and capacity constraints.
Solution Approach 2:
The patent replaces manual mechanical inventory planning processes with an automated computational system that uses simulation and optimization algorithms. The mechanical process of manual calculation and adjustment is substituted with electronic computing that automatically processes demand forecasts, capacity constraints, and opportunity cost calculations.
2Productivity
If adaptive capacity control with simulation is implemented, then inventory capacity optimization improves, but device complexity increases
Solution Approach 1:
The inventory planning system is divided into distinct modular components: demand forecasting module, supply simulation module, opportunity cost calculation module, and optimization module. Each module performs a specific function and can be independently developed, tested, and maintained, reducing overall system complexity while achieving sophisticated optimization.
Solution Approach 2:
The patent introduces an intermediary computing system that acts as a mediator between raw input data (demand forecasts, capacity constraints) and inventory decisions. This intermediary layer processes information through simulation and optimization, transforming complex data into actionable inventory plans without requiring direct complex interactions between all system components.
3Measurement precision
If multi-period ordering model is used, then purchase decision quality improves, but computational requirements increase
Solution Approach 1:
The system performs preliminary simulation and optimization calculations before finalizing inventory decisions. By pre-simulating multiple scenarios and calculating opportunity costs in advance, the system reduces the need for complex real-time computations when actual purchase decisions must be made, thereby lowering immediate computational requirements while maintaining high decision quality.
Solution Approach 2:
The ordering model dynamically adjusts its computational depth based on decision criticality and time availability. For routine items, simpler heuristics are applied, while for high-value or high-risk items, full multi-period simulation is executed. This dynamic approach optimizes computational resource usage while maintaining purchase decision quality where it matters most.
Data Source
AI summary
Techniques for determining a decision to acquire units of an item to be inventoried may be provided. For example, a demand for an item may be simulated to determine a consumption of a capacity for inventorying the item. A discrepancy between the consumption of the capacity and the capacity may be determined. An opportunity cost associated with the capacity may be updated based at least in part on determining that the discrepancy fails a convergence criterion. The opportunity cost may indicate a value associated with using the capacity. The consumption of the capacity may be simulated based at least in part on the updated opportunity cost. A resulting discrepancy may be determined. If the resulting discrepancy meets the convergence criterion, the decision to acquire the units of the item may be generated based at least in part on the updated opportunity cost.