Sales Forecast Adjustment System Using Trend Analysis
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Solution Overview
Problem
Current sales forecasting methods fail to adequately update forecasts in response to changes and trends, leading to inefficient inventory and resource management in enterprises.
Innovation Solution
A computer program product and method that periodically receives sales forecasts and actual sales information, identifies anomalies and trends using mathematical expressions, and adjusts forecasts based on these factors to provide an updated sales forecast.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sales forecasting methods are used, then the forecasting process is simple, but the forecast accuracy and timeliness are insufficient to adapt to changes and trends
Solution Approach 1:
The patent segments the forecasting system into multiple independent modules: data collection module, anomaly detection module, trend analysis module, and forecast adjustment module. Each module performs a specific function, allowing the system to achieve high accuracy through specialized processing while maintaining manageable complexity through modular design. The segmentation enables parallel processing of different data aspects without requiring a monolithic complex system.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and analyzing sales data, anomalies, and trends before final forecast generation. The methodology pre-processes data to identify patterns, deviations, and emerging trends in advance, so that when forecast updates are needed, the system can quickly generate accurate predictions without performing complex real-time analysis from scratch.
2Loss of time
If sales forecasts are updated frequently to adapt to changes, then the forecast timeliness improves, but the resource consumption and system complexity increase
Solution Approach 1:
The system implements periodic action by updating forecasts at scheduled intervals based on predetermined triggers such as time periods, data availability, or significant threshold deviations. Rather than continuously recalculating forecasts in response to every data change, the system periodically processes updates when meaningful changes occur, maintaining timeliness while avoiding unnecessary computational resource consumption from constant recalculations.
Solution Approach 2:
The methodology incorporates feedback mechanisms that monitor data changes and only trigger forecast updates when deviations exceed predetermined thresholds or when significant trends are detected. This feedback-based approach ensures timely updates when needed while conserving resources by avoiding updates when data changes are minor or insignificant, thus resolving the contradiction between timeliness and resource consumption.
3Measurement precision
If comprehensive data analysis is performed to identify all trends and anomalies, then the forecast accuracy improves, but the processing time and computational complexity increase
Solution Approach 1:
The system applies local quality by focusing analysis efforts on specific critical areas rather than uniformly analyzing all data. The methodology identifies and prioritizes analysis of significant anomalies, emerging trends, and key performance indicators that have the greatest impact on forecast accuracy. Less critical data receives minimal or no analysis, allowing the system to achieve high precision on important forecast aspects without the time cost of comprehensive analysis of every data point.
Solution Approach 2:
The methodology employs parameter changes by dynamically adjusting analysis depth and computational parameters based on data characteristics, time constraints, and forecast criticality. When time is limited or data changes are minor, the system reduces analysis parameters to maintain acceptable accuracy with faster processing. When time permits and significant changes are detected, the system increases analysis depth to improve precision, thus balancing accuracy and processing time through adaptive parameter adjustment.
Data Source
AI summary
A computer program product and method for sales forecasting and adjusting a sales forecast for an enterprise in a configurable region having one or more clusters of stores. The method includes periodically receiving a sales forecast for an enterprise over a configurable period of time, periodically receiving actual sales information, sales anomalies and anticipated events within the at least one of the clusters of stores over a computer network, determining positive and negative deviations from the anticipated sales of the sales forecast based on the sales information, determining whether one or more trends are occurring or have occurred using a pre-defined mathematical expression based on the sales information, the positive and negative deviations, and the sales anomalies, adjusting the anticipated sales of the sales forecast based on the sales anomalies, the trends and the anticipated events, and outputting the adjusted sales forecast to a user. The sales forecast includes anticipated sales for a plurality of items within at least one of the clusters of stores.