Automobile Demand Planning for Configuration and Capacity Constraints
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Solution Overview
Problem
The complexity of determining automobile production plans is exacerbated by the numerous configurations and options, making it difficult to accurately forecast demand and manage production capacity effectively.
Innovation Solution
A demand planning system that models option packages as a hierarchy, incorporating forecasted demand, supply chain rules, and constraints to generate a supply chain plan, adjusting to demand fluctuations and capacity limitations.
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 is improved, but production planning complexity increases
Solution Approach 1:
The patent segments the complex production planning problem into hierarchical levels (global demand planning, regional demand planning, and local production planning). Each level handles specific aspects of the planning process, breaking down the overwhelming complexity of managing thousands of configuration combinations into manageable segments that can be processed systematically through optimization algorithms.
Solution Approach 2:
The system changes parameters by transforming the planning approach from managing individual configuration details to managing aggregated demand parameters and constraints. By using mathematical optimization models that work with summary statistics and constraint parameters rather than exhaustive configuration lists, the system handles product versatility without proportional increases in planning complexity.
2Measurement precision
If detailed configuration options are tracked to improve demand forecasting accuracy, then measurement precision is improved, but information processing complexity increases
Solution Approach 1:
The patent extracts only the essential demand signals and constraints needed for planning from the full set of configuration data. Rather than processing all detailed configuration information, the system identifies and extracts key demand patterns, constraints, and parameters that drive production decisions, filtering out unnecessary complexity while maintaining forecasting accuracy.
Solution Approach 2:
The system creates simplified representative models (copies) of the complex configuration data that capture the essential demand characteristics. These models use aggregated parameters and statistical representations that preserve forecasting accuracy while being computationally tractable, allowing the system to work with manageable data structures that mirror the essential features of the full configuration space.
3Productivity
If production capacity is increased to meet higher demand, then productivity is improved, but capacity constraint management complexity increases
Solution Approach 1:
The patent implements dynamic capacity management where production plans are continuously adjusted based on real-time demand signals and capacity utilization. The system treats capacity constraints as dynamic parameters that can be flexibly allocated across different configurations and time periods, rather than fixed limitations. This dynamic approach allows the system to maximize productivity by continuously optimizing the use of available capacity across the product portfolio.
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.