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SAP Certified Associate - SAP IBP for Supply Chain Sample Questions (Q59-Q64):
NEW QUESTION # 59
Which options can be used to reduce the runtimes of a time-series optimizer run? Note: There are 3 correct answers to this question.
Answer: B,D,E
Explanation:
The Time-Series-Based Supply Optimizer in SAP IBP is a powerful tool for supply planning, but its runtime can be significant due to the complexity of constraints and variables. Reducing runtime involves optimizing the problem size and configuration, as outlined in SAP's performance best practices.
* Option A: Keep the number of fair share segments smallThis is correct. Fair share segments (used in demand prioritization or allocation) increase the optimizer's complexity by adding variables and constraints. Limiting segments (e.g., fewer priority tiers) reduces the computational load, a recommended practice in SAP IBP's optimizer configuration documentation.
* Option B: Split into multiple planning areas to support weekly vs. daily planning needsThis is incorrect. Splitting into multiple planning areas might simplify individual runs but doesn't directly reduce the runtime of a single optimizer run. Planning areas are structural, not runtime-specific, and this approach addresses granularity needs, not performance.
* Option C: Use non-overlapping networks by using Subnetwork ID maintained at Location- Products to reduce the size of the problemThis is correct. Subnetwork IDs (e.g., assigned to Location- Product combinations) partition the supply chain network into smaller, independent subproblems. The optimizer solves these separately, significantly reducing runtime by shrinking the problem scope, as per SAP IBP's network optimization guidelines.
* Option D: Eliminate the usage of telescopic time bucketsThis is correct. Telescopic time buckets (e.
g., daily near-term, weekly mid-term, monthly long-term) increase complexity by requiring the optimizer to handle variable time granularities. Using uniform buckets (e.g., all weekly) simplifies the model and cuts runtime, a known performance tweak in SAP IBP.
* Option E: Increase the use of incremental lot size beyond the frozen horizonThis is incorrect.
Incremental lot sizes affect planning quantities, not optimizer runtime directly. Adjusting lot sizes might influence solution feasibility but doesn't inherently optimize performance.
Thus, A, C, and D are proven methods to reduce time-series optimizer runtimes, per SAP IBP's official performance optimization documentation.
NEW QUESTION # 60
What are some of the available ABC segmentation methods in SAP IBP? Note: There are 2 correct answers to this question.
Answer: C,D
Explanation:
ABC segmentation in SAP IBP classifies items (e.g., products) based on value or volume, using methods in the ABC/XYZ Segmentation app, per SAP IBP's demand planning documentation.
* Option A: By Number of Items (Sorted Average)This is incorrect. "Sorted Average" is not a standard ABC method; it's not defined in SAP IBP's segmentation options.
* Option B: By Pareto Principle (Sorted and Cumulated %)This is correct. The Pareto Principle (80
/20 rule) sorts items by value (e.g., revenue) and cumulates percentages (e.g., top 20% = A), a standard method, per SAP IBP's documentation.
* Option C: By Number of Items (Sorted Value)This is correct. Sorting by value (e.g., total sales) and assigning classes (A, B, C) based on item count thresholds is a supported ABC method, per SAP IBP's segmentation features.
* Option D: By Pareto Principle (Sorted and Calculated Values)This is incorrect. "Calculated Values" is vague and not a distinct method; B covers the Pareto approach accurately.
Thus, B and C are available ABC methods, per SAP IBP's official segmentation capabilities.
NEW QUESTION # 61
What are possible approaches to modeling a customer demand in time-series-based optimization with SAP IBP for response and supply? Note: There are 2 correct answers to this question.
Answer: B,D
Explanation:
Time-series-based optimization in SAP IBP for Response and Supply balances demand and supply constraints over a horizon. Modeling customer demand involves prioritization and cost considerations.
* Option A: Ensure discounting does not result in negative costs for each customer-product combinationThis is incorrect. Discounting (e.g., price reductions) isn't a standard concept in time- series optimization; costs (e.g., non-delivery) are positive penalties, not negative adjustments.
* Option B: Assign a high cost value (1 million or more) for non-delivery to priority customersThis is correct. In the optimizer, assigning high non-delivery costs (e.g., 1M) to priority customers ensures their demand is met first, a common prioritization technique in SAP IBP, per optimization documentation.
* Option C: Ensure product prioritization with the combination of customer and productThis is correct. Time-series optimization can prioritize demand at the Customer-Product level (e.g., via demand priority rules or costs), ensuring key combinations are favored, per SAP IBP's supply planning features.
* Option D: Try to go as granular as possible with the customer productThis is incorrect. Excessive granularity increases complexity without guaranteeing better results; optimization balances granularity with performance, not mandating maximum detail.
Thus, B and C are valid approaches to modeling customer demand, per SAP IBP's optimization capabilities.
NEW QUESTION # 62
A company only purchases a license for the SAP IBP S&OP module and wants to maximize the use of its standard functionality. What processes will they be able to cover? Note: There are 3 correct answers to this question.
Answer: A,D,E
Explanation:
The SAP IBP S&OP module focuses on demand, supply, and inventory alignment using time-series planning, per its standard functionality documentation.
* Option A: Review critical resources against demandThis is incorrect. Resource capacity planning (e.
g., finite scheduling) is part of Supply or Response modules, not standard S&OP, which uses infinite heuristics by default.
* Option B: Conduct Sales and Operations Planning meetings with cross-functional representatives This is correct. Facilitating S&OP meetings to align demand and supply (e.g., via Consensus Demand Plan) is a core S&OP process, per SAP IBP's documentation.
* Option C: Build what-if scenarios for breaching supply-demand gapsThis is correct. What-if analysis using versions/scenarios (e.g., adjusting supply plans) is standard in S&OP, per SAP IBP's capabilities.
* Option D: Prepare versions of S&OP plan based on different optimizer modeling costsThis is correct. The S&OP optimizer can model cost-based scenarios (e.g., varying non-delivery costs), a standard feature, per SAP IBP's documentation.
* Option E: Get system-solving recommendations on how to react to production bottlenecksThis is incorrect. Detailed bottleneck resolution is part of Response or finite planning, not standard S&OP functionality.
Thus, B, C, and D are covered by S&OP, per SAP IBP's official module scope.
NEW QUESTION # 63
You are working with inventory key figures. What are some of the business scenarios where you can use the Last Period Aggregation function? Note: There are 2 correct answers to this question.
Answer: A,B
Explanation:
The Last Period Aggregation function (LASTPERIOD) in SAP IBP retrieves the most recent value in a time horizon for a key figure, useful for inventory analysis. Its applications align with SAP IBP's calculation capabilities.
* Option A: Calculating the value of inventory on a weekly basis, using static aggregation from the daily levelThis is incorrect. Static aggregation (e.g., sum, average) across periods isn't what LASTPERIOD does; it returns the last period's value, not an aggregate.
* Option B: Calculating how many periods inventory is going to last based on the planned demand This is correct. Using the last period's inventory value (via LASTPERIOD) divided by planned demand can estimate inventory duration, a common scenario in inventory planning, per SAP IBP's documentation.
* Option C: Searching for and returning the last not-null value of the inventory key figureThis is correct. LASTPERIOD retrieves the most recent non-null value in the horizon, ideal for tracking current inventory levels, per SAP IBP's calculation guides.
* Option D: Calculating the value of inventory on any level from the time profile, ensuring flexibility of calculationThis is incorrect. LASTPERIOD is time-specific (last period), not flexible across all levels; it's not a general aggregation method.
Thus, B and C are valid scenarios for Last Period Aggregation, per SAP IBP's official use cases.
NEW QUESTION # 64
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