Demand Forecast Sourcing with Material Constraints
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Solution Overview
Problem
Current supply chain management systems lack coordination between demand planning and other components, failing to account for material and resource constraints when sourcing demand forecasts, leading to inefficient decision-making and inadequate handling of subsequent sales orders.
Innovation Solution
A system and method that calculates demand forecasts, identifies current sales orders, generates an open forecast, and sources it based on supply chain material and resource constraints, using optimization techniques and 'dummy' forecast orders to protect sourced forecasts from existing sales orders, and intelligently sources new sales orders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If demand forecast is propagated through supply chain based on empirical rules, then demand forecast can be generated, but it does not account for material and resource constraints leading to inefficient decision-making
Solution Approach 1:
The system implements feedback by continuously monitoring material availability and resource constraints, then using this information to adjust and optimize demand forecast sourcing decisions. The SNP module receives feedback from material requirements planning and resource constraint data to intelligently allocate demand forecasts to appropriate supply sources.
Solution Approach 2:
The demand forecast sourcing system performs self-service by automatically identifying and allocating demand forecasts to suitable supply sources based on predefined criteria and real-time constraint data, eliminating the need for manual intervention while adapting to changing material and resource conditions.
2Ease of operation
If demand forecast is sourced without coordination with other SCM components, then sourcing can be performed independently, but coordination with material and resource constraints is lost
Solution Approach 1:
The system merges the demand forecast sourcing function with material requirements planning and resource constraint management by integrating the SNP module with other SCM components. This allows coordinated decision-making where demand forecasts are allocated considering material availability and resource constraints simultaneously.
Solution Approach 2:
The SNP module serves multiple functions: it receives demand forecasts from DP, checks material availability through MRP, evaluates resource constraints, and allocates forecasts to supply sources. This multi-functionality enables coordinated operation across different SCM components while maintaining ease of use.
3Measurement precision
If empirical demand planning is used, then demand forecast can be calculated, but it cannot intelligently source the forecast considering supply chain constraints
Solution Approach 1:
The system performs preliminary action by pre-configuring sourcing criteria, supply source priorities, and constraint thresholds in the SNP module. When demand forecasts are generated by DP, the SNP module has already prepared the framework for intelligent allocation, enabling rapid and accurate sourcing decisions based on material and resource constraints.
Solution Approach 2:
The demand forecast sourcing system is dynamic, automatically adjusting allocation decisions based on real-time changes in material availability and resource constraints. The SNP module continuously evaluates current constraints and reallocates demand forecasts to optimal sources, maintaining adaptability while preserving forecast calculation accuracy.
Data Source
AI summary
A system and method are described for intelligently sourcing demand forecasts within a supply chain management (“SCM”) system based on a constrained supply chain model, in light of material and resource constraints. For example, a computer implemented method according to one embodiment of the invention comprises: calculating a demand forecast identifying anticipated demand for a product over a specified time period; identifying current sales orders for the product with delivery dates scheduled during the specified time period; generating an open forecast for the product based on the current sales orders and the anticipated demand; and sourcing different percentages of the open forecast from different plants, the percentages selected based on supply chain material and/or resource constraints. In addition, after the demand forecast is sourced, one embodiment of the invention employs additional techniques for intelligently sourcing new sales orders entering the SCM system.