Predictive Model for Supply Management
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Solution Overview
Problem
Manufacturing industries face challenges in balancing supply and demand, leading to inefficiencies, excess storage costs, and potential losses due to inaccurate forecasting and substitution complexities, particularly in industries with long lead times like wine production.
Innovation Solution
A predictive model utilizing two-stage stochastic programming with recourse is employed to manage building block supplies, considering demand distributions, substitution costs, and constraints, to optimize production quantities and minimize costs and risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional supply management methods are used, then simplicity of operation is maintained, but supply-demand balance deteriorates leading to excess supply or undersupply
Solution Approach 1:
The patent introduces a predictive model as an intermediary between demand forecasting and production planning. This model processes demand data and substitution data to generate optimal production quantities, acting as a mediator that translates complex inputs into actionable production decisions while accounting for building block substitutability
Solution Approach 2:
The patent replaces traditional mechanical supply management approaches with a data-driven predictive model. Instead of relying on manual planning and simple forecasting methods, the system uses computational models that process demand data and substitution data to automatically determine optimal production quantities
2Reliability
If production quantities are increased to meet potential demand, then customer service level is improved, but storage capacity requirements increase
Solution Approach 1:
The patent changes the parameter of production quantity from fixed or overestimated levels to dynamically optimized quantities. The predictive model adjusts production parameters based on demand data and substitution possibilities, producing only the optimal quantity needed while maintaining customer service levels
Solution Approach 2:
The patent applies partial action by producing only the optimal quantity needed rather than excessive amounts. The predictive model calculates precise production quantities based on demand forecasts and substitution data, avoiding overproduction and excess storage requirements
3Adaptability or versatility
If building block substitutions are allowed, then supply flexibility is improved, but substitution costs increase
Solution Approach 1:
The patent introduces dynamics into supply management by allowing flexible substitutions between building blocks based on real-time conditions. The predictive model dynamically determines optimal production quantities considering substitution possibilities, enabling the system to adapt to demand variations while managing substitution costs
Solution Approach 2:
The predictive model incorporates feedback by processing substitution data and demand data to continuously optimize production decisions. The model learns from historical patterns and adjusts substitution strategies to minimize costs while maintaining supply flexibility
4Productivity
If accurate demand forecasting is implemented, then production optimization is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex forecasting problem into manageable components by processing demand data and substitution data separately through the predictive model. The model divides the optimization task into analyzing demand patterns and evaluating substitution options, making the overall process more tractable
Data Source
AI summary
Systems and methods are described for managing a supply of building blocks for one or more products. An example method includes: obtaining demand data for each building block from a plurality of building blocks; obtaining substitution data for the plurality of building blocks; providing the demand data and the substitution data as input to a predictive model; receiving as output from the predictive model a predicted optimal production quantity for each building block; and facilitating a production of the plurality of building blocks according to the predicted optimal production quantities.