Evaluating the available planning algorithms in SAP IBP for respo

The figure outlines four different aspects of supply chain planning and optimization: 1. Unconstrained Planning (Infinite Without Shortage), 2. Prioritized Demand Fulfillment, 3. Production and Distribution Optimization, and 4. Deployment.

SAP Integrated Business Planning for response and supply provides planning operators to support both tactical and operational planning.

The Infinite Heuristic is an unconstrained planning process, used primarily to propagate demand and understand dependent requirements and projected capacity needs.

The Finite Heuristics can provide feasible plans by considering one order or demand element at a time. The Finite Heuristic is a pull-based model (pull to demand) and can be used for production planning and deployment planning.

On the other hand, the Supply Optimizer provides feasible planning results using the key constraints modeled at network level with a cost model. The optimizer is used in both push and pull models balancing the cost drivers to achieve the right product mix with efficiencies across the entire network.

Note

In this training the terms Supply Optimizer and Optimizer both refer to the Time Series based Supply Optimizer. If we refer to the Order Based Planning Optimizer this will be mentioned explicitly.

The figure shows a diagram comparing different planning methods in terms of complexity and quality of planning results. The diagram positions these methods along two axes: complexity (vertical) and quality of planning results (horizontal). 1. Infinite Heuristic Planning is described as an unconstrained heuristic that propagates demand through the network without capacity constraints, planning end-to-end supply with multi-level demand propagation. 2. Finite Heuristic Planning is described as a Priority Heuristic that creates a feasible plan by fulfilling the highest-priority demand first, considering material and capacity constraints, and taking the first feasible result. 3. Supply Optimizer focuses on Optimization to create the best feasible plan by considering the entire network to minimize total cost, balance inventory, and achieve optimal results.

Advantages of unconstrained Heuristics is its speed. Since, there is no constraint, the demands and supplies are aligned, and it is easy to understand the results. It is also a great tool to run as a check to see if the data model is working correctly prior to using a constraint-planning operator.

Priority-based Heuristics can use rules-based approach to prioritize the demands. Target inventory is planned after the primary demands automatically. The planning results could also be faster since it is based on pull model.

Optimizer uses MILP (Mixed-Integer Linear Programming) model to balance costs to achieve a set of competing objectives. It tries to improve the initial solution found till the accuracy is acceptable or the maximum run time is reached.

The figure compares two production planning approaches: Finite Heuristic and Optimizer. On the left, the Finite Heuristic prioritizes individual demand elements one by one, focusing on pull-based production, demand priority, demand fairshare, and assembly. Key industries include high-tech, telecommunications, consumer electronics, fashion, machinery and equipment manufacturing, and components. On the right, Optimizer plans the entire network for the right product mix, considering constraints and key objectives. It involves pull and push production, demand priority by costs, asset utilization, fairshare of demand, safety stock and maximum inventory, and production wheel. Key industries include chemical, consumer products, mill products, oil and gas, food and beverage, distribution, pharmaceuticals, and life sciences. Various icons represent different industries and production elements, such as computers, machinery, lab equipment, and manufacturing processes.

Priority-based Heuristics is prevalent where assembly is required, for example, consumer electronics. It can find solutions for multi-level assembly with alternative sources and procurement priorities. As it works with one order at a time, it is also easy to retain the pegging relationship between the primary demands and multi-layered sources of supply and supply elements.

Optimizer solves complex problems typically associated with process industries and continuous manufacturing. The products could range from short shelf-life to high value-add through multiple transformations. Fairshare is key to balance the network demands with supplies and common constraints such as capacity and materials, both in pull and push mode. Industry-specific features such as Production Wheel may be of importance to better perform operational production planning in SAP IBP.

Evaluating the available planning algorithms in SAP IBP for response and supply

Introduction of the available planning algorithms in SAP IBP for response and supply