Automotive Demand Planning for Option Package Constraints
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Solution Overview
Problem
The complexity of determining automobile production plans is exacerbated by the numerous configurations and options available, making it difficult to accurately forecast demand and manage production efficiently.
Innovation Solution
A demand planning system that models option packages as a hierarchy, considering interdependencies, capacity constraints, and supply chain rules to generate a supply chain plan that adjusts to forecasted demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of automobile configurations and options is increased to meet diverse customer demands, then product versatility and customer satisfaction are improved, but the complexity of determining production plans and forecasting demand increases significantly
Solution Approach 1:
The patent segments the complex automobile configuration problem into hierarchical levels: base models, optional features, and package bundles. This segmentation allows the system to manage thousands of possible configurations by breaking them down into manageable components that can be independently planned and controlled, thereby reducing production planning complexity while maintaining product versatility.
Solution Approach 2:
The patent introduces an intermediary computing system that acts as a mediator between demand forecasts and production planning. This intermediary system processes configuration data, applies constraints, and generates optimized production plans, thereby decoupling the complexity of configuration management from direct production control and enabling efficient handling of diverse product options.
2Adaptability or versatility
If more configuration options and packages are offered to satisfy customer preferences, then market competitiveness and sales potential are improved, but the difficulty of accurately forecasting demand and managing production increases
Solution Approach 1:
The patent applies preliminary action by pre-defining configuration packages and option bundles before the forecasting period. These pre-configured packages group related options together with known demand patterns, allowing the forecasting system to predict demand at the package level rather than evaluating every individual option combination, thereby improving forecast accuracy while maintaining configuration diversity.
Solution Approach 2:
The patent merges individual configuration options into bundled packages that are sold together. By combining multiple options into unified packages with correlated demand patterns, the system reduces the dimensionality of the forecasting problem and improves prediction accuracy, as customers typically select packages rather than individual options in isolation.
3Reliability
If the system accounts for all constraints including capacity, supply chain rules, and interdependencies among options, then production plan feasibility and reliability are improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent implements dynamics by using iterative optimization that adjusts production plans based on constraint satisfaction. The system dynamically modifies configuration mix recommendations to account for capacity constraints and supply chain rules, re-running forecasts with adjusted parameters until an feasible solution is found. This dynamic approach ensures plan feasibility while managing computational time through targeted iterations rather than exhaustive analysis.
Solution Approach 2:
The patent incorporates feedback loops where the system evaluates produced plans against all constraints (capacity, supply chain rules, option interdependencies) and uses this feedback to refine subsequent planning iterations. The feedback mechanism identifies infeasible configurations and adjusts the optimization parameters, ensuring that final plans satisfy all requirements while reducing processing time by learning from previous iteration results.
Data Source
AI summary
A system and method are disclosed including a demand planner that receives a demand for two or more options that are needed to produce at least one automobile. The demand planner also models the two or more options as a network of arcs and nodes and generates one or more valid configurations of the two or more options. The demand planner further determines the demand for the one or more valid configurations and causes at least one manufacturer to manufacture, the at least one automobile based on the determined demand for the one or more valid configurations.