Consumption-Driven Forecasting Using Heterogeneous Point-of-Sale Data
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Solution Overview
Problem
Legacy forecasting methods fail to accurately account for point-of-sale data, leading to the 'bullwhip effect' and inventory volatility in distribution chains, as they do not combine point-of-sale data with distribution-tier ordering data effectively.
Innovation Solution
A method and system for dynamic time-phased consumption-driven forecasting and replenishment planning that combines point-of-sale data with distribution-level order data, using a computer-implemented engine to generate accurate replenishment plans by reconciling data from various sources and formats, thereby reducing inventory volatility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If legacy forecasting methods use only distribution-tier order data, then the forecasting process is simple, but forecast accuracy deteriorates due to the bullwhip effect and lack of visibility to point-of-sale demand
Solution Approach 1:
The patent merges point-of-sale data with distribution-tier order data into a unified forecasting model. The system combines heterogeneous data from multiple sources (retail sales data, wholesale orders, distribution center inventory) to generate integrated forecasts that reduce the bullwhip effect by providing visibility into actual consumer demand while maintaining manageable complexity through standardized data processing protocols
Solution Approach 2:
The patent introduces an intermediary data processing layer that acts as a mediator between point-of-sale systems and supplier forecasting systems. This intermediary layer standardizes and reconciles data from different formats and sources, enabling accurate forecasting without requiring direct complex integration between all system components
2Reliability
If suppliers use ad hoc techniques with multiple ad hoc data formats, then data processing is flexible, but forecast reliability deteriorates due to inconsistency and inability to account for point-of-sale data
Solution Approach 1:
The patent implements a universal data processing framework that can handle multiple data formats and sources through a single standardized interface. The system is designed to process point-of-sale data, distribution orders, and inventory data through common reconciliation protocols, ensuring consistent and reliable forecasting across diverse data inputs without requiring separate processing systems for each data type
3Stability of the object's composition
If distribution entities place orders without visibility to point-of-sale demand, then ordering decisions are independent and simple, but inventory volatility worsens due to amplified demand variations propagating upstream
Solution Approach 1:
The patent implements feedback mechanisms where point-of-sale demand information flows upstream through the distribution chain to influence ordering decisions at each tier. The system provides real-time or near-real-time visibility of actual consumer demand to distribution entities, enabling them to adjust orders based on actual sales patterns rather than amplified forecast variations, thereby stabilizing inventory levels throughout the supply chain
Data Source
AI summary
A method, system, and computer program product for generating forecasts and replenishment plans. Some embodiments commence upon receiving point-of-sale data, then receiving distribution-level order data in a second data format. The first point-of-sale data comprises an item identifier and a first date or first date range, and the distribution-level order data comprises the item identifier and a second date or second date range. The originators of the order data are determined using address identifiers (e.g., network location identifiers). The received data is combined wherein at least a portion of the point-of-sale data is combined with at least a portion of the distribution-level order data to generate a combined forecast for the item. Further processing includes receiving an inventory model parameter and combining at least a portion of the first point-of-sale consumption data with at least a portion of the distribution-level order data to generate a replenishment plan for the item.