Digital Purchase Platform Linear Optimization Algorithm
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current e-purchasing systems between companies (B2B) lack a method to mathematically optimize the negotiation and purchasing of multiple products while adhering to extensive constraints such as price, quantity, and supplier ratings, including incremental and staggered discounts, which are not effectively addressed by existing digital systems.
Innovation Solution
A computer-implemented process using a digital purchase platform that applies a linear optimization algorithm with mixed integer programming to determine the optimal mix of product quantities from multiple suppliers, minimizing total acquisition cost, while allowing for negotiation and evaluation based on qualitative and quantitative criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a digital purchase platform uses traditional e-purchasing systems to connect buyers and sellers, then the platform can facilitate transactions between companies, but it cannot mathematically optimize the negotiation and purchasing of multiple products while adhering to extensive constraints such as price, quantity, and supplier ratings
Solution Approach 1:
The patent transforms the purchasing system from a simple transaction platform to an optimization system by changing the parameters considered. It introduces multiple constraints (price, quantity, supplier ratings, incremental discounts, staggered discounts) as mathematical parameters that the linear optimization algorithm can process. This allows the system to handle complex B2B purchasing scenarios with multiple products and suppliers while finding optimal solutions that minimize total acquisition cost.
2Quantity of substance
If the system purchases from the supplier with the lowest unit price, then the immediate product cost is reduced, but the total acquisition cost may increase due to ignoring incremental quantity discounts and staggered discounts on total purchase value
Solution Approach 1:
The system performs preliminary calculations and optimizations before the actual purchasing decision. The linear optimization algorithm pre-computes the optimal distribution of quantities across multiple suppliers, taking into account incremental quantity discounts and staggered discounts on total purchase value. This preliminary optimization ensures that the final purchasing decision minimizes the total acquisition cost rather than just the unit price, while the platform handles the complexity automatically.
Solution Approach 2:
The digital purchase platform acts as an intermediary between the purchasing company and multiple suppliers. It collects pricing information, discount structures, and supplier ratings from various suppliers, then uses the linear optimization algorithm to determine the optimal purchasing strategy. The platform mediates the complexity by presenting a single optimized recommendation to the user, eliminating the need for manual analysis of multiple discount structures and supplier options.
3Quantity of substance
If the system manually negotiates with multiple suppliers for optimal pricing, then the total acquisition cost can be minimized, but the time and resources required for negotiation increase significantly
Solution Approach 1:
The system enables self-service optimization where the digital purchase platform automatically performs the negotiation and optimization process without requiring manual intervention. The linear optimization algorithm automatically analyzes pricing data, discount structures, and supplier capabilities to determine the optimal purchasing strategy. This self-service approach minimizes the total acquisition cost while eliminating the time-consuming manual negotiation process, as the system independently finds and implements the optimal solution.
Solution Approach 2:
The patent replaces the mechanical process of manual negotiation with an automated computational system. Instead of human purchasers spending time negotiating with multiple suppliers, the system uses a linear optimization algorithm to automatically calculate the optimal purchasing strategy. This substitution of mechanical negotiation with computational optimization dramatically reduces the time required while maintaining or improving the quality of the purchasing decision.
4Productivity
If the system considers multiple suppliers and products for optimization, then the total acquisition cost can be minimized through optimal quantity distribution, but the computational complexity and data processing requirements increase
Solution Approach 1:
The digital purchase platform is designed as a universal system that can handle multiple products, multiple suppliers, and various constraint types simultaneously. The linear optimization algorithm is formulated to process diverse data types including pricing, quantities, incremental quantity discounts, staggered discounts on total purchase value, and supplier ratings in a unified mathematical framework. This multi-functionality allows the system to optimize complex B2B purchasing scenarios across multiple products and suppliers while maintaining a single coherent optimization process.
Data Source
AI summary
The present invention refers to a digital method of centralizing, optimizing and negotiating the purchase of goods and services implemented by computer, wherein at least one authorised user (101) by the company or purchase centre (108), identifies the products (102) to be acquired and proceeds to consult with at least one potential supplier (103) through a digital purchase platform (109), hosted in a dedicated server, locally or in a cloud. The company or purchase centre (109) proceeds to validate the proposals (104) from the suppliers (103), and can create different, economically more advantageous choice scenarios from the proposals (104) that satisfy all the restrictions imposed by the company or purchase centre (109) and by the suppliers (103).