Inventory Stocking Forecasts for Unforecastable Retail SKUs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to accurately forecast demand for low-volume and irregular-volume stock-keeping units (SKUs), leading to inefficiencies such as dead stock and stockouts, as manual static minimums are unreliable and infrequently updated.
Innovation Solution
A machine learning infrastructure that integrates with retailer devices to analyze data, employing algorithms like LightGBM to predict zero-demand probability, high-demand classification, and raw-demand scores, dynamically calculating optimal stocking levels for unforecastable items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual static minimums are used for low-volume SKUs, then implementation simplicity is maintained, but forecast accuracy deteriorates leading to dead stock and stockouts
Solution Approach 1:
The system transitions from static manual minimums to dynamic machine learning-based forecasts that automatically adapt to changing demand patterns. The ML models continuously learn from historical data and update predictions, enabling the system to respond to seasonal variations, trends, and anomalies without manual intervention.
Solution Approach 2:
The patent replaces the mechanical manual process of setting static minimums with an automated machine learning system. The ML infrastructure ingests historical data, processes it through trained models, and generates dynamic forecasts automatically, eliminating the need for manual analysis and updates while significantly improving accuracy.
2Device complexity
If manual updates of static minimums are performed infrequently, then operational complexity is reduced, but reliability deteriorates as SKUs become unreliable and unusable
Solution Approach 1:
The machine learning system operates autonomously, automatically ingesting data, training models, generating forecasts, and updating recommendations without human intervention. The system serves itself by continuously learning from new data and adapting to changing patterns, eliminating the need for manual updates while maintaining high reliability.
Solution Approach 2:
The ML system provides continuous forecasting and automatic updates rather than periodic manual revisions. The infrastructure continuously processes incoming data, retrains models as needed, and generates up-to-date recommendations, ensuring SKU reliability is maintained constantly without operational interruptions.
3Measurement precision
If dynamic machine learning forecasts are implemented, then forecast accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the forecasting system into distinct modular components: data ingestion module, data processing module, model training module, forecast generation module, and recommendation module. Each component has a specific function and can be independently developed, tested, and maintained, reducing overall system complexity despite the advanced capabilities.
Solution Approach 2:
The system introduces an intermediary machine learning infrastructure that sits between the historical data and the forecasting needs. This intermediary layer handles the complexity of data processing, model training, and prediction generation, presenting a simplified interface to end users while managing the sophisticated ML operations in the background.
4Adaptability or versatility
If machine learning models continuously learn from historical data, then adaptability improves for changing demand patterns, but computational resources increase
Solution Approach 1:
The system implements periodic model training and updating rather than continuous real-time retraining. The ML models are trained at scheduled intervals or triggered by significant data accumulations, allowing the system to adapt to changing demand patterns while managing computational resources efficiently through batch processing rather than constant computation.
Data Source
AI summary
The present system and methods generally relate to managing stock of unforecastable supply items and various goods sold by a retailer and/or wholesaler. The present system and methods can employ various techniques to integrate with retailers computing infrastructure and receive data to determine unforecastable supply items of the retailers and/or wholesalers. Using novel techniques, the present system and methods can employ machine learning techniques to predict minimum and maximum stocking requirements for goods sold by the retailers and/or the wholesalers. The present system methods can employ other machine learning model to generate reports, data analyses, predictions, and recommendations associated with the goods sold by the retailer and/or wholesaler. The present system and methods can produce stocking outcomes based on the analyses performed. For example, the present system can update internal stocking data, generate reports associated with various goods, and/or generate orders for identified low stock items.