Promotion Demand Forecasting Using Distribution Center Data Aggregation
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Solution Overview
Problem
Current supply chain management systems face challenges in accurately forecasting product demand during promotions, especially when insufficient point of sale (POS) data is available for certain distribution centers, and they fail to differentiate between distribution centers based on their data availability.
Innovation Solution
A computer-implemented method that uses historical POS data to calculate product demand for stores associated with distribution centers, differentiating between centers with sufficient data and those without by correlating with data-rich centers or relying on historical data, and takes electronic actions based on the calculated demand, such as inventory repositioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If POS data is collected for a short period during a promotion, then the system can quickly generate demand forecasts, but the accuracy of the forecast deteriorates due to insufficient data
Solution Approach 1:
The patent introduces distribution center level POS data as an intermediary to bridge the gap when store level data is insufficient. The system aggregates POS data from multiple stores at a distribution center to compensate for limited data at individual stores, enabling accurate forecasting even with short collection periods.
Solution Approach 2:
The system combines multiple data sources including store level POS data, distribution center level POS data, and historical promotion data to create a comprehensive forecast. This merging of data sources ensures sufficient statistical basis for accurate demand prediction during promotions.
2Ease of operation
If the system treats all distribution centers uniformly, then the process is simple and consistent, but forecast accuracy deteriorates for centers with insufficient data
Solution Approach 1:
The patent implements differentiated processing for different distribution centers based on their data availability. Centers with sufficient POS data undergo one type of analysis, while centers with insufficient data receive alternative processing using distribution center level aggregation or historical data, optimizing forecast accuracy for each local context.
Solution Approach 2:
The system dynamically adjusts its forecasting approach based on data availability conditions. It automatically selects appropriate data sources and methods for each distribution center, transitioning between different forecasting strategies based on the quality and quantity of available POS data.
3Measurement precision
If the system waits for sufficient POS data before forecasting, then forecast accuracy improves, but the response time to promotion demands deteriorates
Solution Approach 1:
The system performs preliminary aggregation of POS data at the distribution center level before detailed store level analysis. This preliminary action ensures that sufficient data is available to start forecasting immediately when a promotion begins, rather than waiting for individual store data to accumulate.
Solution Approach 2:
The forecasting system operates continuously throughout the promotion period, constantly updating forecasts as new POS data becomes available. This continuous operation allows the system to generate initial forecasts quickly using available data and refine them over time, maintaining both speed and accuracy.
Data Source
AI summary
A computer implemented method of product ordering and inventory repositioning for a promotion in a supply chain management system utilizing a network is provided. The method comprises obtaining via the network from a plurality of stores in a first region, each store associated with a respective distribution center within the first region, point of sale data for a short period of time relative to a length of time allotted for a current product sales promotion. A computer calculation is caused of a product demand level for stores associated with at least one distribution center for the current product sales promotion based on an outlook model and the point of sale data. A computer calculation is caused of a product amount for the at least one distribution center based at least in part on the product demand level for stores associated with the at least one distribution center. An electronic action is taken based on the product amount for the at least one distribution center.