Promotion Plan Optimization System Margin Budget Constraints
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Solution Overview
Problem
Existing methods struggle to efficiently generate optimal promotion plans that align with enterprise objectives, particularly in handling a large number of products and variations in pricing across stores, and fail to accurately forecast the effectiveness of promotional tools and price adjustments.
Innovation Solution
A system and method that includes receiving base data for products, establishing a margin budget, and using a scenario generator and optimization engine to create and optimize a promotion plan, constrained by the margin budget, which considers allowable offers and price rules to achieve enterprise objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional manual methods are used to create promotion plans, then flexibility in decision-making is maintained, but the ability to handle astronomical number of products and combinations is lost
Solution Approach 1:
The system segments the complex promotion planning problem into manageable components: product data, pricing rules, promotion strategies, and optimization algorithms. Each component is processed independently and integrated to form the complete promotion plan, enabling handling of large product catalogs while maintaining decision flexibility.
Solution Approach 2:
The patent introduces a computerized optimization system as an intermediary between manual decision-making and the complex promotion planning task. This intermediary processes astronomical combinations of products and pricing scenarios, presenting optimized recommendations to human decision-makers who retain final flexibility in approval and adjustment.
2Measurement precision
If comprehensive promotion analysis is performed across all products and stores, then optimal promotion plans can be identified, but processing time becomes excessive
Solution Approach 1:
The system performs preliminary actions by pre-processing product data, pricing rules, and historical sales information before the actual optimization run. Demand forecasting models are pre-configured and validated, so when promotion scenarios are evaluated, the analysis can proceed efficiently without excessive processing time while maintaining comprehensive accuracy.
Solution Approach 2:
The patent applies partial action by focusing optimization efforts on the most critical products, stores, and promotion scenarios rather than uniformly analyzing every possible combination. The system identifies and prioritizes high-impact opportunities, achieving near-optimal results with reduced processing time by concentrating computational resources where they yield the greatest value.
3Device complexity
If uniform pricing strategy is applied across all stores, then implementation complexity is reduced, but variations in product pricing across stores cannot be accommodated
Solution Approach 1:
The system implements local quality by allowing different pricing strategies and promotion parameters to be applied to different stores based on their specific characteristics, product mix, and market conditions. Each store can have customized pricing rules and promotion configurations within the overall enterprise framework, accommodating regional variations without requiring completely separate systems for each location.
4Measurement precision
If detailed promotion scenarios are optimized for each product, then precision in meeting enterprise objectives is improved, but the complexity of managing promotions increases
Solution Approach 1:
The patent applies universality by creating a unified optimization platform that handles multiple products, stores, and promotion types through a single integrated system. The same optimization engine and algorithmic framework manage diverse promotion scenarios across the entire enterprise, reducing management complexity despite the detailed precision applied to each individual product and scenario.
Data Source
AI summary
A method (400) and system (100) for providing a promotion plan (128) for merchandising products (600) receives base data (142) for the products (600) that includes allowable offers (204) and price rules (206) that affect the offers (204). A margin budget (146) is established for the promotion plan (128) that defines an amount of margin an enterprise is willing to forgo for a promotion event implementing the promotion plan (128). A scenario (406) is generated in response to the base data (142). The scenario (406) is optimized to obtain decisions (154) for the promotion plan (128) that are constrained by the margin budget (146). The promotion plan (128), indicating the obtained decisions (154), is presented for implementation by the enterprise during the promotion event. The obtained decisions (154) include a list of the products (600), each of which is associated with one offer (204) and one price rule (206).