Monte Carlo Forecasting for Low-Selling SKU Inventory
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Solution Overview
Problem
Retailers face challenges in accurately forecasting sales for low-selling stock keeping units (SKUs), as existing methods like Kalman filters are inadequate for non-linear demand patterns and Poisson distributions, leading to sub-optimal inventory management and lost sales opportunities due to overstocking or understocking.
Innovation Solution
A system utilizing Monte Carlo methods with an unscented Kalman filter to generate dynamic linear models for clusters of low-selling SKUs, fitting these models with random data points to produce more accurate sales forecasts, and ordering inventory based on these forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting methods (e.g., Kalman filters) are used for low-selling SKUs, then the forecasting process is simple and quick, but the forecast accuracy is insufficient for non-linear demand patterns and Poisson distributions
Solution Approach 1:
The patent transforms the forecasting approach by changing the mathematical parameters and distributions used. Instead of linear models, it employs Poisson distributions for demand modeling and Monte Carlo simulations with unscented Kalman filters to handle non-linear patterns. This parameter transformation enables accurate forecasting for low-selling SKUs with irregular demand while maintaining computational feasibility through efficient sampling methods.
Solution Approach 2:
The patent replaces traditional mechanical forecasting systems (deterministic linear models) with a probabilistic simulation system. By substituting fixed mathematical models with Monte Carlo simulations that incorporate random sampling and unscented Kalman filtering, the system adapts to non-linear demand patterns and Poisson-distributed data, significantly improving forecast accuracy for low-selling items.
2Reliability
If inventory levels are increased to prevent stockouts, then the risk of understocking is reduced, but the cost of storing excess inventory increases and space is wasted on items that do not sell
Solution Approach 1:
The patent implements a feedback mechanism where forecast accuracy continuously improves through iterative Monte Carlo simulations. By using unscented Kalman filters to process actual sales data and update demand predictions, the system learns from past performance and adjusts inventory recommendations dynamically. This feedback loop enables precise inventory level optimization that prevents stockouts while minimizing excess inventory for low-selling SKUs.
Solution Approach 2:
The patent transforms static inventory planning into a dynamic system that adapts to changing demand patterns. By employing Monte Carlo simulations with time-varying parameters and unscented Kalman filtering, the forecasting system continuously adjusts predictions based on new sales data. This dynamic approach allows inventory levels to be optimized in real-time, ensuring adequate stock for potential demand surges while reducing excess inventory for consistently low-selling items.
Data Source
AI summary
A system and method for calculating demand forecasts is presented. Sales data for a set of SKUs is received. The sales data is filtered to contain only data for low-selling SKUs. A set of clusters of SKUs is created. A generalized dynamic linear model for use with each cluster in the set of clusters is generated. A set of random data points is generated. The dynamic linear model is fitted at each data point in the set of random data points using a Monte Carlo method. This fitting can be performed using an unscented Kalman filter method. Calculating a forecast for sales based on the fitting at each data point. Using the forecast for sales, inventory is ordered. Other embodiments are also disclosed herein.