Inventory Optimization Tool Using Probability Distributions
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Solution Overview
Problem
Current inventory optimization systems primarily operate from a local perspective, failing to effectively predict and manage inventory needs in global distributed logistical networks, which leads to congestion, delays, and inefficiencies due to the complexity of factors like lead times, demand, and decentralized control.
Innovation Solution
A computer-implemented method and system that simulates inventory needs by determining probability distributions for lead time and demand using Akaike Information Criteria and Monte Carlo Simulation, allowing for predictive modeling and automatic adjustments to inventory parameters such as capacity and transportation schedules without user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If local inventory optimization is used to manage single warehouses, then single warehouse costs and demands are optimized, but global inventory needs cannot be predicted and network congestion occurs
Solution Approach 1:
The system segments the global inventory network into multiple distributed warehouses, each with its own simulation model. The overall system divides the complex global optimization problem into smaller local simulation modules that can be independently executed and aggregated, allowing global prediction while managing complexity through modular architecture.
Solution Approach 2:
The system transitions from traditional single-dimension local optimization to multi-dimensional simulation by incorporating time (historical data), space (distributed network locations), and probability (stochastic modeling of lead times and demands). This dimensional expansion enables global perspective while managing complexity through structured multi-parameter analysis.
2Measurement precision
If probability distributions and Monte Carlo Simulation are used to model lead time and demand, then predictive accuracy improves, but computational complexity increases
Solution Approach 1:
The system creates simplified probabilistic models that copy the essential characteristics of complex real-world inventory systems. Instead of simulating every detail of actual inventory operations, it uses probability distributions to replicate the statistical behavior of lead times and demands, achieving accurate predictions with reduced computational complexity.
Solution Approach 2:
The system transforms complex inventory dynamics into manageable parameters by fitting probability distributions to historical data. By changing the representation from detailed operational data to statistical parameters (mean, variance, distribution shapes), the system achieves high predictive accuracy while significantly reducing computational complexity.
3Productivity
If automatic adjustments are made to inventory capacity and transportation schedules, then response time to random events improves, but loss of human control increases
Solution Approach 1:
The simulation system enables automatic self-adjustment of inventory parameters by using predicted outcomes to automatically modify inventory capacity and transportation schedules. The system serves itself by generating predictions and implementing adjustments without continuous human intervention, improving productivity while maintaining ease of operation through automated decision-making based on probabilistic forecasts.
Data Source
AI summary
This disclosure generally relates to devices, systems, and computer-implemented methods for simulating one or more inventories in a distribution network. Specifically, methods are described that comprise the operations of receiving inventory data for one or more inventories to be simulated, wherein the inventory data includes lead time data and demand data; determining, based on the inventory data, probability distributions for each of lead time and demand for the one or more inventories to be simulated; determining a lead time demand probability distribution of the one or more inventories to be simulated based on the determined probability distributions for the lead time and the demand; determining a predictive state of the one or more inventories to be simulated based on the lead time demand probability distribution; and outputting one or more evaluation parameters associated with the predictive state of the one or more inventories to be simulated.