Dynamic Risk-Based Scheduling for Supply Chain Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current supply chain planning and scheduling systems fail to effectively manage inherent randomness and risk, leading to inefficient inventory management, poor customer service, and high costs due to conservative order sizing and long lead times, which are exacerbated by the inability to find optimal schedules and the need for frequent rescheduling.
Innovation Solution
A dynamic risk-based scheduling system that generates evolving schedules by balancing inventory levels, equipment utilization, and on-time delivery, using probability distributions to determine shortage risks and capacity adjustments, thereby minimizing inventory and optimizing resource utilization without the need for frequent rescheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conservative order sizing and long lead times are used to manage supply chain risk, then reliability of delivery is improved, but inventory levels increase and productivity decreases
Solution Approach 1:
The patent applies dynamics by transitioning from static, conservative scheduling parameters to dynamic parameters that adapt in real-time based on actual system conditions. The scheduling system continuously updates order sizes and lead times based on current inventory levels, demand patterns, and capacity availability, allowing the system to maintain reliability while optimizing productivity through data-driven adjustments rather than fixed conservative defaults
Solution Approach 2:
The invention changes key parameters from fixed conservative values to variable parameters optimized through machine learning algorithms. Order sizing parameters and lead time parameters are dynamically adjusted based on learned patterns from historical data and real-time conditions, enabling the system to achieve reliable delivery with optimized inventory levels and improved supply chain efficiency
2Adaptability or versatility
If frequent rescheduling is performed to accommodate changes in demand and capacity, then adaptability is improved, but loss of time and operational disruption increase
Solution Approach 1:
The system performs preliminary action by pre-calculating and pre-positioning inventory and capacity buffers based on predicted demand scenarios. Machine learning models forecast potential demand variations and capacity constraints in advance, allowing the system to prepare alternative scheduling options beforehand. This reduces the need for reactive rescheduling and minimizes time loss when changes occur
Solution Approach 2:
The invention implements feedback mechanisms where the scheduling system continuously monitors actual demand and capacity deviations from the plan. Machine learning algorithms learn from these deviations and automatically adjust future schedules, reducing the frequency and impact of rescheduling events. The feedback loop enables adaptive optimization without requiring frequent manual or system-wide rescheduling operations
3Reliability
If pessimistic lead times are used to avoid late deliveries, then reliability is improved, but loss of time and responsiveness decrease
Solution Approach 1:
The system transforms lead time from a fixed pessimistic parameter to a dynamic parameter optimized by machine learning models. The models analyze historical lead time data, capacity constraints, and demand patterns to determine optimal lead times for different product-family-resource combinations. This enables the system to achieve reliable on-time delivery with minimized lead times tailored to specific conditions rather than applying uniform conservative estimates
Solution Approach 2:
The invention applies local quality by customizing lead time parameters for different product families, resources, and routing scenarios rather than using a single global lead time value. Each product-family-resource combination receives a tailored lead time estimate based on its specific characteristics and historical performance, improving responsiveness while maintaining reliability for each local context
4Reliability
If large safety stock is maintained to buffer against demand variability, then reliability is improved, but inventory levels increase and cost increases
Solution Approach 1:
The system applies dynamics by transitioning from static safety stock levels to dynamic safety stock that adjusts in real-time based on actual demand variability and supply chain conditions. Machine learning models continuously update safety stock requirements based on learned patterns from historical data, enabling the system to maintain reliable service levels with optimized inventory levels that respond to changing conditions rather than relying on fixed conservative buffers
Solution Approach 2:
The invention changes safety stock from a fixed parameter to a variable parameter optimized through machine learning algorithms. The system dynamically adjusts safety stock parameters based on product demand characteristics, supply lead times, and service level targets, enabling precise inventory optimization that achieves reliable service levels with minimized inventory investment
Data Source
AI summary
A production and inventory control for a manufacturing facility is provided that facilitates and coordinates improved planning and execution of such facility in a supply chain with a focus on providing an improved and robust planning, production and inventory control, even in the presence of uncertainty. This may include Optimal Planning that can balance the need for low inventory, low cost (i.e., high utilization of equipment and labor), and efficient on-time delivery. The result of such planning is not a schedule per se but a set of parameters that form a dynamic policy that generates an evolving schedule as conditions (demand, production) materialize. An Optimal Execution applies the dynamic policy resulting in a manufacturing system that is robust enough to accommodate moderate changes in demand and/or capacity without the need to reschedule. Optimal Execution may also involve a “Capacity Trigger” that detects when the assumptions regarding demand and capacity used to determine the dynamic policy are no longer valid. The Capacity Trigger also may provide a Trigger Signal to the planner indicating the need for either more or less capacity.