The time-series-based supply planning finite heuristic creates a finite demand and supply plan based on prioritized demand, taking into account certain supply and resource constraints.
It provides a prioritized, constrained feasible solution, with priorities derived from the costs of demand and sources of supply (higher costs mean higher priority for demands, lower costs means higher priority for sources of supply). It's the only heuristic that enables you to create a supply plan based on priorities.
The Supply Planning Finite Heuristic is a powerful solver with many key capabilities to model a variety of scenarios and product lines.
Handling Multi-Level Supply Chains:
Manages complex supply chains with multiple levels of production and distribution.
Finite Capacity Planning:
Considers capacity constraints for resources, ensuring realistic and achievable plans.
Stock Level Management:
Optimizes stock levels across different locations to minimize shortages and overstock situations.
Lead Time Management:
Accounts for lead times in production and transportation, ensuring timely deliveries.
Prioritization of Demands over costs:
Uses prioritization rules to allocate limited resources and meet most critical demands first.
Production Sequencing:
Determines the optimal order of production activities to maximize efficiency.
Lot Size Management:
Handles lot-sizing constraints to balance production frequency and inventory levels.
Availability Checks:
Ensures materials and components are available before scheduling production activities.
Cross-Plant Planning:
Supports planning across multiple plants, optimizing the utilization of distributed resources.
Scenario Planning:
Allows for the creation and comparison of different planning scenarios for better decision-making.
Alerts and Notifications:
Generates alerts for exceptions and issues, enabling proactive resolution.
Integration with Other Modules:
Seamlessly integrates with other SAP IBP modules such as Demand Planning and Inventory Optimization.
Each of these capabilities contributes to creating a robust and adaptable supply planning process, allowing organizations to address various challenges and improve their supply chains effectively.
The goals of the Supply Planning Finite Heuristic in SAP Integrated Business Planning (IBP) aim to improve the efficiency and effectiveness of the supply chain by ensuring realistic, achievable, and balanced plans.
Goals of the Supply Planning Finite Heuristic in SAP Integrated Business Planning (IBP)
Achieving Feasible Plans:
Ensure that production and distribution plans are executable within the given constraints, such as capacity, lead times, and resource availability.
Improve Service Level:
Meet customer demands on time while minimizing stock-outs and backorders.
Optimizing Resource Utilization:
Efficiently use production and transportation resources to avoid bottlenecks and under-utilization.
Balancing Inventory Levels:
Maintain optimal inventory levels to prevent overstocking and under-stocking.
Facilitating Scenario Analysis:
Enable planners to analyze different scenarios to make informed decisions.
In SAP Integrated Business Planning (IBP), both the Supply Planning Infinite Heuristic and Supply Planning Finite Heuristic algorithms are used to generate supply plans, but they differ significantly in how they handle constraints and provide planning results.
The main differences between the Supply Planning Infinite Heuristic and Supply Planning Finite Heuristic algorithms are the following:
Constraint Handling:
- Supply Planning Infinite Heuristic:
- No Capacity Constraints: This algorithm does not consider resource capacity constraints. It assumes that infinite resources are available for production and transportation.
- No Material Constraints: Typically does not strictly consider material availability; it plans as if materials are always available..
- Supply Planning Finite Heuristic:
- Capacity Constraints: Considers finite capacity constraints for production resources, storage, and transportation. Plans are generated based on the actual available resources.
- Material Constraints: Takes into account the availability of materials and components, making sure that supplies do not exceed what's available.
Planning Results:
- Supply Planning Infinite Heuristic:
- Optimistic Outputs: The results are often more optimistic, reflecting ideal scenarios without considering limitations
- Potential Overcommitment: Resources may appear overcommitted, and plans may be unrealistic due to ignoring capacity constraints.
- Simpler and Faster: The calculations are simpler and faster because they omit complex constraint checks.
- Supply Planning Finite Heuristic:
- Realistic Outputs: Generates more realistic plans by respecting the finite capacities of resources and material availability.
- Feasible Plans: Ensures that plans are executable and aligned with actual resource and material constraints, preventing overcommitment.
- Complex and Slower: More complex and potentially slower computations due to the inclusion of several constraints.
Use Cases:
- Supply Planning Infinite Heuristic:
- Full Demand Propagation: This enables the planner to have the visibility of full demands in the supply chain on all BoM levels.
- High-Level Planning: Suitable for high-level strategic planning where detailed constraints are less critical.
- Long-Term Planning: Can be used for long-term capacity planning where the focus is on overall trends rather than immediate executability.
- Scenario Analysis: Useful for "what-if" scenarios to quickly analyze the impact of different demand or supply changes.
- Supply Planning Finite Heuristic:
- Constrained Demand Propagation: This enables the planner to have the visibility of constrained demands in the supply chain on all BoM levels as a feasible plan.
- Operational Planning: Ideal for operational and tactical planning where realistic and executable plans are required.
- Short to Mid-Term Planning: More appropriate for short to mid-term planning horizons where resource and material constraints are critical.
- Execution Feasibility: Ensures that the generated plans can be executed without overloading resources or creating infeasible supply scenarios.
Complexity and Data Requirements:
- Supply Planning Infinite Heuristic:
- Simpler Data Requirements: Requires less detailed data inputs since it does not account for capacity constraints.
- Easier to Configure: Generally easier to set up due to fewer parameters and constraints needing configuration.
- Supply Planning Finite Heuristic:
- Detailed Data Requirements: Requires detailed information on resource capacities, material availability, shift patterns, lead times, etc.
- Complex Configuration: More intricate to configure due to the necessity to input and manage comprehensive constraint data.
Outputs and KPIs:
- Supply Planning Infinite Heuristic:
- Higher-Level KPIs: Typically generates higher-level KPIs that may look favorable but might not be immediately actionable.
- Idealistic Plans: Tends to produce plans that are idealistic and assume perfect conditions.
- Supply Planning Finite Heuristic:
- Actionable KPIs: Generates KPIs that are actionable and reflect the reality of resource constraints and material availability.
- Realistic Plans: Produces plans that reflect real-world conditions, making them more useful for execution purposes.
By understanding these differences, planners can choose the appropriate algorithm based on the specific requirements of their planning process.
Some companies use both the infinite heuristic for demand propagation and the finite heuristic to determine the constrained demand. This enables them to have the visibility of full demands in the supply chain on all BoM levels in addition to the feasible plan. This information is useful as decision support to mitigate bottlenecks in future.