Composite latent state models for intermittent demand forecasting
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Solution Overview
Problem
Existing forecasting methods struggle to accurately predict intermittent demand for infrequently sold items due to their sporadic sales patterns, leading to challenges in maintaining optimal inventory levels, as they often result in either stockouts or overstocking, which can lead to customer dissatisfaction and increased costs.
Innovation Solution
The implementation of composite latent state models that combine deterministic and random process components, such as linear functions and neural networks, to generate probabilistic forecasts for intermittent demand data sets, incorporating feature metadata like holidays and price changes, and utilizing approximate Bayesian inference and Kalman smoothing for parameter fitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting methods are used for intermittent demand items, then computational simplicity is maintained, but forecast accuracy deteriorates due to sporadic sales patterns and zero demand values
Solution Approach 1:
The patent segments the forecasting problem by separating deterministic components (trend, seasonality) from random process components (intermittent demand variability). This is achieved through decomposition of the demand signal into distinct elements that can be modeled separately, allowing each component to be handled with appropriate methods while improving overall forecast accuracy for intermittent items
Solution Approach 2:
The patent creates a composite forecasting model that combines deterministic modeling techniques with stochastic process models. The composite model integrates linear deterministic functions with random process components to capture both the structured patterns and the intermittent variability in demand, resolving the contradiction between model complexity and forecast accuracy
2Reliability
If inventory levels are increased to prevent stockouts, then customer satisfaction is improved, but holding costs increase due to excessive stock of infrequently-purchased items
Solution Approach 1:
The patent implements feedback mechanisms by using probabilistic forecast outputs to dynamically adjust inventory policies. The model provides probability distributions of future demand that feed into inventory optimization systems, enabling data-driven decisions about reorder points and order quantities that balance service levels with inventory costs for intermittent items
Solution Approach 2:
The patent changes the parameter representation from deterministic point forecasts to probabilistic forecast distributions. This allows inventory systems to work with confidence intervals and probability thresholds, enabling more nuanced inventory decisions that account for demand uncertainty without requiring excessive safety stock
3Measurement precision
If lead time is extended to allow accurate forecasting, then forecast precision improves, but responsiveness to demand changes deteriorates
Solution Approach 1:
The patent introduces dynamics by allowing the model to adapt to changing demand patterns over time. The probabilistic framework enables the forecast to dynamically adjust to new information and changing conditions, providing precise forecasts even for items with evolving intermittent patterns without requiring excessively long lead times
Data Source
AI summary
With respect to an input data set which contains observation records of a time series, a statistical model which utilizes a likelihood function comprising a latent function is generated. The latent function comprises a combination of a deterministic component and a random process. Parameters of the model are fitted using approximate Bayesian inference, and the model is used to generate probabilistic forecasts corresponding to the input data set.