Multi-Model Demand Forecasting for Retail Inventory
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Solution Overview
Problem
Current retail inventory systems struggle to accurately forecast product demand, especially when faced with uncharacteristic factors such as weather, social events, or economic changes, leading to inefficiencies in inventory management and customer experience.
Innovation Solution
A product forecasting system that applies multiple models to historic data, using a variable database to identify variables with uncharacteristic effects on demand, and adjusts forecasts based on actual sales, allowing for dynamic inventory adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single forecast model is used, then the system complexity is low, but the forecasting accuracy deteriorates when faced with uncharacteristic factors
Solution Approach 1:
The forecasting system is segmented into multiple independent forecast models (e.g., time series model, regression model, machine learning model), each capable of handling different patterns in demand data. This segmentation allows the system to maintain low complexity within each model while achieving high overall accuracy through model diversity.
Solution Approach 2:
The plurality of forecast models serves multiple functions: capturing trend patterns, seasonal variations, and uncharacteristic factors simultaneously. Each model contributes differently to the overall forecast, making the system universally applicable to various demand scenarios without requiring separate systems for each pattern.
2Measurement precision
If multiple forecast models are applied, then the forecasting accuracy improves, but the computational time and processing complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing historical data and pre-training multiple forecast models before actual forecasting is needed. This allows the models to be ready for rapid execution during demand forecasting, reducing computational time when accuracy is most critical.
Solution Approach 2:
The system skips unnecessary computational steps by selecting only the most relevant models based on data characteristics and forecast horizons. This allows the system to rush through the forecasting process using a subset of models when full accuracy is not required, reducing processing time while maintaining adequate accuracy.
3Adaptability or versatility
If variables with uncharacteristic effects are incorporated, then the adaptability to changing conditions improves, but the data processing complexity increases
Solution Approach 1:
The system applies local quality by incorporating variables with uncharacteristic effects only in specific models where they are most relevant, rather than uniformly across all models. This selective incorporation maintains adaptability to changing conditions while reducing overall data processing complexity by focusing computational resources where they provide maximum benefit.
Data Source
AI summary
In some embodiments, apparatuses and methods are provided herein useful to forecasting product demand. In some embodiments, a system comprise a forecasting control circuit to: apply each of a plurality of different models to forecast demand of a first product over a first historic period generating historic forecasted demands of the first product, wherein at least a first model uses selected one or more variables that are predicted to have an uncharacteristic effect on predicted demand; select one of the models and apply the model in generating a forecasted future demand, wherein the selection of the model is based on a difference between each of the generated historic forecasted demands and actual sales; and identify actions to modify inventory of the first product at the first shopping facility based on the forecasted future demand.