sap scm apo snc


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SAP SCM APO SNCSummary Symptom The following questions arise in connection with the integration of Supply Network Planning (SNP) and Production Planning and Detailed Scheduling (PP/DS): What are the differences between SNP plans and PP/DS plans? What are mixed resources? How do I set up mixed resources? I work with periodic lots in the PP/DS heuristic. If part of a period is outside the PP/DS horizon, the PP/DS order nevertheless covers the requirements in the second half of the period. This means that requirements outside the PP/DS horizon are covered by PP/DS. Why? Does this not affect the separation of SNP and PP/DS? What is the function of the heuristic switch for taking into account shortages outside the production horizon? SNP plans a stock build-up. This stock build-up is destroyed by PP/DS. How can I prevent this? Is there a heuristic that considers the requirements only up to the horizon end in the planning? What is the role of mixed resources? I use mixed resources. After the system converts an SNP planned order to a PP/DS planned order, the capacity requirements in the SNP capacity view differ from the capacity requirements before the conversion. Why? What is modeled using the lot-size intervals in the SNP production process model (PPM) and in the PP/DS PPM? What do I need to take into account regarding lot sizes when converting from SNP to PP/DS? Which planning area is relevant for the SNP->PP/DS order conversion? What strategy profile is to be used for the SNP->PP/DS order conversion? Packaging SNP orders during the SNP order conversion

Other terms SNP-PP/DS integration, documentation, FAQ, production horizon, PP/DS horizon, SNP>PP/DS Reason and Prerequisites

Comparing SNP with PP/DS: SNP The Role Of SNP ================= SNP planning makes the decisions on tactical planning and source of supply determination. The strengths of SNP planning are: The selection of the source of supply taking into account the costs, and the determination of the approximate production date taking into account both the procurement costs and the storage costs. The result of the SNP planning is the answer to "What is produced, where and when?". The answer to "when" cannot be more precise than a planning period, and it does not take the sequence-dependent setup activity into account. The SNP Optimizer =================== The SNP optimizer performs cost-based optimization. The SNP optimizer creates a production plan in a way so that an objective function is as close to its minimum value as possible. The objective function is the weighted total of the following costs: Production costs, procurement costs, storage costs, and transportation costs Penalty costs for non-coverage or delayed coverage of the requirement or the safety stock Costs for increasing the capacity (with regard to the production, the storage, the shipment, and so on)

The SNP optimizer is therefore able to make the following decisions in particular: A product is subject to seasonal fluctuations. Is it cheaper to build up a warehouse stock in low season (storage costs) or to use an expensive source (in-house production or external procurement)? You can either procure a product externally or produce it in-house. In the current requirement situation, is it cheaper to procure a product externally (in this case, the resource is available for the production of another product) or to produce it inhouse? A product is in stock in one plant, but it is required in another plant. Is it more favorable to transfer the product (as a result, a future requirement may have to be covered differently in this plant) or to produce it again in-house? A resource can produce a quantity A of products. Different costs are incurred when these products are produced. Another resource can produce a different quantity B of products. The quantities A and B have an intersection. On which resource are the products from the quantities A and B produced at the lowest cost?

The SNP optimizer determines the following:

The production quantities, the procurement quantities, the stock transfer quantities for each product and period The selection of the resources and the plans for the production The selection of the plants, the warehouses, the suppliers, and the transportation lanes

The SNP optimizer works on the basis of periods. The sequence of orders within a planning period is not defined. This has the following consequences: The SNP optimizer can consider only a constant setup time. Sequence-dependent setup times or sequence-dependent setup costs are not supported. It can only provide a rough model for the material flow. In principle, a material flow occurs only at the period boundaries.

Setup In The SNP Optimization =============================== SNP planning is period-based planning. SNP planning determines the production quantities for each period; it does not determine the sequence of production orders. Therefore, SNP planning cannot take into account the exact effort required for the setup. Usually, setups in SNP optimization are only taken into account by a setup allowance. The attributable capacity of a resource available in SNP is reduced by the loss factor. This means that a part of the capacity is kept free for the setup. You can set the loss factor for mixed resources in the master data of the resource. Using the modeling option for fixed resource consumptions in SNP, the SNP optimizer supports a simple lot size planning, which may also be performed across periods for APO Release 3.1. Lot size planning reduces setup costs by summarizing the orders for large lots. With cross-period lot size planning (APO Release 3.1), the setup statuses of the previous period are taken into account when a product is produced in the current period with the same plan. This lot size planning is used in industries where setup operations greatly influence the production costs. If setups play a major role, you should try to ensure that the setups are already taken into account in SNP. SNP then generates plans that represent a reliable template for planning in PP/DS. Material Flow In SNP Optimization ========================================= The period factor or bucket offset controls the modeling of the material flow in SNP. If the period factor is high, you can often produce two or more production levels in one bucket. The lead times on several production levels become shorter. However, the result may be too optimistic. If you wish to err on the side of caution, you must work with a period factor of zero, but this is at the expense of the lead time. The period factor in the product master applies only to the SNP heuristic. The same function can be applied in SNP optimization using the bucket offset in the SNP optimization profile and in the PPMs and transportation lanes. Model Size And Complexity =========================== Due to the runtime and memory requirements of SNP optimization, we must also think

about the model size and the model complexity. Creating an SNP production plan is an NPcompleteness problem. IT theory has taught us that there is no known algorithm to guarantee finding an optimal solution for the problem in polynomial runtime. For more information on NP-completeness problems, we recommend that you read the following book by Michael R. Garey and David S. Johnson: "Computers and Intractability: A Guide to the Theory of NP-Completeness". In the case of SNP optimization, a distinction must always be made between purely continuous models and models known as discrete models. If fixed lot sizes, minimum lots, or piecewise linear cost functions are to be taken into account, you will require a discrete model. In this case, the system must perform a mixed integral optimization. The continuous models are primarily restricted because of the memory required. However, the required runtime of purely continuous models is linearly dependent on the model size and is generally non-critical as a result. On the other hand, the solution of discrete models using a mixed integral optimization can be considerably more complicated. It is generally not possible to guarantee the global optimum under realistic CPU time targets. Instead, permitted and high-quality solutions must be found with acceptable runtimes. The runtime and memory requirement of SNP optimization depends on the number of the variables. The number of variables is therefore restricted. You require a variable for each production quantity or procurement quantity that you want to determine using SNP optimization. In other words, you require a variable for each location product for each planning period. The practical upper limit is approximately two (2) million continuous variables or 100,000 discreet variables. The following is an example of a continuous model: 7,000 products * 5 locations * 52 weeks = 1,820,000. This type of continuous model already belongs to the large optimization models. This means that we require 4GB RAM for this continuous model. If the problem is too large or too complex, it may be simplified as follows: We are planning in large periods rather than small periods (months instead of weeks). We consider fixed order quantities, minimum lot sizes, and so on only where necessary. Non-critical products and resources are not included in the planning.

You can use decomposition procedures to divide the optimization problem into several subproblems that are optimized separately. You can therefore optimize larger models than those listed above. Contact your consultant for more information. Restricting the model size is a considerable incentive for hierarchical production planning. In SNP planning, you often have to work on aggregated data in order to reduce the number of variables. Nevertheless, you should determine detailed production plans at a later stage. The detailed production plans are then created using PP/DS. Advantages Of SNP Optimization


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