Distributional Demand Forecasting for Slow-Moving Inventory
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Solution Overview
Problem
Existing inventory management systems struggle with accurately predicting demand for slow-moving products, which leads to inadequate service levels and inefficient resource allocation due to the high intermittence and low lumpiness of demand.
Innovation Solution
A system and method for generating distributional demand forecasts for slow-moving products, using a zero-inflated negative binomial distribution model that accounts for excess zeros and overdispersion, and incorporating explanatory variables such as promotions, seasonality, and weather to improve forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical inventory replenishment rules are used for slow-moving products, then the inventory management system remains simple and easy to operate, but service levels become inadequate due to high demand intermittence
Solution Approach 1:
The patent transforms the demand forecasting approach by changing the parameter representation from point forecasts to distributional forecasts. This involves modeling demand as a zero-inflated negative binomial distribution with parameters μ (mean) and σ (standard deviation), allowing the system to capture the high intermittence and lumpiness characteristics of slow-moving products while maintaining operational simplicity through automated parameter estimation.
Solution Approach 2:
The patent introduces distributional forecast parameters (μ and σ) as intermediaries between the raw intermittent demand data and the inventory replenishment decisions. These parameters serve as mediators that encapsulate the complex demand patterns, enabling classical replenishment rules to function effectively without requiring complex system modifications.
2Measurement precision
If point forecast models like Croston's exponential smoothing are used, then the forecasting system remains simple, but the dispersion surrounding the forecast is not captured, leading to inadequate inventory control
Solution Approach 1:
The patent adds a new dimension to forecasting by transitioning from one-dimensional point forecasts to two-dimensional distributional forecasts that include both the mean (μ) and standard deviation (σ). This dimensional expansion captures the dispersion and uncertainty inherent in slow-moving demand patterns without significantly increasing model complexity, as the zero-inflated negative binomial framework provides closed-form estimation methods.
Solution Approach 2:
The patent changes the forecast output parameters from a single point estimate to a pair of parameters (μ and σ) that fully characterize the demand distribution. This parameter transformation enables the forecasting model to represent both the central tendency and the variability of demand, providing more precise measurement while maintaining computational simplicity through maximum likelihood estimation.
3Reliability
If traditional forecasting approaches are used for slow-moving items, then the system remains computationally efficient, but the high intermittence and low lumpiness of demand cannot be accurately summarized
Solution Approach 1:
The patent employs parameter changes by selecting the zero-inflated negative binomial distribution, which is specifically suited for capturing high intermittence and low lumpiness patterns. The distribution's parameters (μ and σ) can be efficiently estimated using maximum likelihood methods, maintaining computational efficiency while dramatically improving forecast accuracy for slow-moving items with these characteristic demand patterns.
Data Source
AI summary
A system and method are disclosed for a supply chain planner to generate a distributional demand forecast for slow-moving inventory in a supply chain. The distributional demand forecast model takes into account explanatory variables and historical sales data to address seasonality and special events and permits sharing of demand information across different stores and stock-keeping units. The supply chain planner performs inference on the explanatory variables and historical sales data to generate process parameters and latent variables. Other embodiments are also disclosed.