Gross Margin Forecasting via Supervised Machine Learning
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Solution Overview
Problem
Business organizations face challenges in accurately forecasting gross margin and aligning it with Annual Operating Plan targets due to delays and inaccuracies in price adjustments, which affect overall profitability and business performance.
Innovation Solution
A machine learning-based system that predicts gross margin by combining internal product attributes and external customer attributes using various forecasting models, such as linear regression, ARIMA, Holt-Winters, and Prophet, to generate accurate demand forecasts and propensity scores, thereby improving forecasting accuracy and alignment with business targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional price planning methods are used, then the system is simple to operate, but the forecasting accuracy of gross margin is insufficient
Solution Approach 1:
The gross margin forecasting system is segmented into multiple independent modules: demand forecasting module, pricing module, cost module, and gross margin calculation module. Each module processes specific aspects independently, then results are integrated to produce the final forecast. This segmentation allows the system to achieve high forecasting accuracy through specialized algorithms while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary analytical layer that sits between raw business data and gross margin outcomes. This intermediary layer uses machine learning models to process pricing, cost, and demand data, transforming them into predictive insights. The intermediary handles the computational complexity, allowing the overall system to achieve high accuracy without requiring end users to understand or manage the underlying complexity.
2Reliability
If price adjustments are made frequently to align with AOP targets, then the alignment with profitability targets improves, but the stability of pricing and business operations deteriorates
Solution Approach 1:
The system performs preliminary forecasting of gross margin outcomes before actual pricing decisions are finalized. By using machine learning models to predict how different pricing scenarios will affect gross margin and AOP alignment, the system allows businesses to evaluate multiple options in advance. This preliminary action enables target alignment without requiring frequent reactive price adjustments, thereby maintaining pricing stability while achieving reliability in meeting profitability targets.
3Measurement precision
If more business information is collected to improve forecasting accuracy, then the measurement precision improves, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary data processing and feature engineering in advance, preparing business information for forecasting before it is needed for decision-making. Historical data is pre-processed, patterns are pre-identified, and baseline forecasts are generated ahead of time. This preliminary action reduces the time required for actual forecasting operations while maintaining high accuracy, as the heavy lifting of data preparation is completed beforehand rather than during the critical forecasting window.
Data Source
AI summary
A mechanism is provided to proactively forecast gross margin for a business unit of an organization utilizing machine learning techniques. Embodiments provide a cascading-architecture machine-learning model to predict gross margin for a period (e.g., an upcoming quarter), utilizing metrics both internal and external to the organization. Internal metrics can include list price change, discounting change, cost impact, and the like. External metrics can include customer information such as propensity to purchase and purchase consumption.