C_IBP_2502 Exam Dumps - SAP Certified Associate - SAP IBP for Supply Chain

Rolling aggregation in SAP IBP refers to calculating a cumulative or moving total across a time horizon (e.g., year-to-date sales). This is a common requirement in planning and reporting, achieved through specific configuration methods.

Option A: Using an attribute as a key figureThis is incorrect. Attributes as key figures provide static values (e.g., Product Category), not dynamic time-based aggregations like rolling totals.

Option B: Using an appropriate period weight factorThis is incorrect. Period weight factors adjust proportional disaggregation (e.g., splitting monthly data to weeks), not rolling aggregation across periods.

Option C: Using a key figure calculationThis is correct. Rolling aggregation is achieved in SAP IBP via key figure calculations, such as the CUMULATE function (e.g., KF2 = CUMULATE(KF1)), which sums values from the start of the horizon to the current period. This is configured in the Planning Areas app and is a standard method for time-series calculations, per SAP IBP’s official documentation on key figure calculations.

Option D: Using a local memberThis is incorrect. Local members in the Excel add-in allow ad-hoc calculations within a planning view, but they are user-specific and not a system-configured method for rolling aggregation across the planning area.

Thus, C is the correct method for achieving rolling aggregation, aligning with SAP IBP’s calculation capabilities.

Demand Sensing in SAP IBP refines short-term forecasts using real-time data, per SAP IBP’s demand sensing documentation.

Option A: Results of time series analysisThis is incorrect. Time series analysis (statistical forecast) is an input to demand planning, not sensing, which adjusts based on current signals.

Option B: Forecast Accuracy measuresThis is incorrect. Accuracy measures evaluate forecasts, not serve as direct inputs to sensing.

Option C: Open Sales OrdersThis is correct. Open sales orders provide real-time demand signals, a key input to demand sensing, per SAP IBP’s documentation.

Option D: Consensus DemandThis is correct. The Consensus Demand Plan (from S&OP) is a baseline input, adjusted by sensing with short-term data, per SAP IBP’s process.

Thus, C and D are inputs, per SAP IBP’s official demand sensing scope.

Key figure calculations in SAP IBP, configured in the Planning Areas app, follow specific rules, per SAP IBP’s calculation engine documentation.

Option A: All key figure calculations have calculation inputs, which can be marked as stored or calculatedThis is correct. Calculations (e.g., KF3 = KF1 + KF2) use inputs that are either stored (persisted data) or calculated (derived), a fundamental rule, per SAP IBP’s guides.

Option B: The calculation chain for a key figure must always result in a calculated key figureThis is incorrect. The chain can result in a stored key figure if configured to persist, notalways calculated.

Option C: Aggregation calculations using SUM or MAX functions must be based on a higher aggregation levelThis is incorrect. Aggregation can occur at any level, not strictly higher; it depends on the planning level definition.

Option D: Key figures can be calculated across the different planning levelsThis is correct. Calculations can span levels (e.g., aggregating from PERPROD to PERPRODLOC), using disaggregation/aggregation, per SAP IBP’s flexibility.

Thus, A and D are key rules, per SAP IBP’s official calculation principles.

A successful consistency check in SAP IBP ensures the planning area’s configuration is valid, per SAP IBP’s documentation.

Option A: Configure at least one calculated key figure for the planning areaThis is incorrect. Calculated key figures are optional; a planning area can function with only stored key figures.

Option B: Specify a planning horizon in the planning area for each level of the assigned time profileThis is correct. The planning horizon (e.g., past/future periods) must align with the time profile levels (e.g., week, month) for data consistency, a prerequisite, per SAP IBP’s setup.

Option C: Configure at most two input key figures on the same planning level in a key figure calculationThis is incorrect. There’s no such limit; calculations can use multiple inputs, depending on complexity.

Option D: Assign the compound master data type and its component master data typesThis is correct. Compound types (e.g., SOURCECUSTOMER) and their components (e.g., Customer, Location) must be assigned for network consistency, per SAP IBP’s documentation.

Thus, B and D are prerequisites, per SAP IBP’s official consistency check requirements.

SAP IBP offers multiple solvers for supply planning: finite heuristics, infinite heuristics, and the optimizer. Choosing the right solver for a complex supply chain involves evaluating their capabilities.

Option A: Finite heuristics work with demand prioritizationThis is correct. Finite heuristics respect capacity constraints and can prioritize demand (e.g., based on priority rules or fair share settings), making them suitable for complex scenarios, as per SAP IBP’s supply planning documentation.

Option B: Push and Pull production models are used by the optimizerThis is correct. The optimizer supports both push (supply-driven) and pull (demand-driven) models, optimizing production and inventory across the network, a key feature for complex supply chains, per SAP IBP’s optimizer capabilities.

Option C: Prioritization of demand elements one by one is achieved by unconstrained heuristicsThis is incorrect. Infinite (unconstrained) heuristics ignore capacity and don’t prioritize demand sequentially; they plan supply assuming unlimited resources, unlike finite methods.

Option D: Planning results can be generated faster by the optimizerThis is incorrect. The optimizer, while powerful, is typically slower than heuristics due to its complex constraint-solving, not faster, per SAP IBP’s performance comparisons.

Thus, A and B are critical review points for solver selection, per SAP IBP’s official solver descriptions.

SAP IBP for Sales and Operations Planning (S&OP) includes demand planning with various statistical forecast algorithms to predict demand in time-series planning.

Option A: Data-cleansing algorithmsThis is incorrect. Data cleansing (e.g., outlier correction) is a preprocessing step, not a forecast model algorithm in SAP IBP’s demand planning engine.

Option B: Trend models algorithmsThis is correct. Trend models (e.g., linear regression, Holt’s method) are supported in SAP IBP to forecast demand with consistent growth or decline patterns, per official demand planning documentation.

Option C: Sporadic demand models algorithmsThis is incorrect. While sporadic demand (intermittent) is handled (e.g., via Croston’s method), it’s not a distinct category in SAP IBP’s standard algorithm list; it falls under broader models.

Option D: Naive models algorithmsThis is correct. Naive models (e.g., simple moving average, last period’s demand) are basic forecast algorithms in SAP IBP, used for stable demand patterns, per SAP’s forecast model library.

Thus, B and D are valid forecast algorithms in SAP IBP for S&OP, per official documentation.

SAP IBP supports multiple tenants (e.g., development, test, production), and users may need to distinguish between them when working in parallel sessions, typically via the Fiori-based Web UI. Customization of the UI is a key feature to enhance usability.

Option A: Define a different default planning area via the User PreferencesThis is incorrect. While users can set a default planning area in their preferences, this affects functionality (e.g., which planning area loads first), not the visual appearance of the UI. It doesn’t help differentiate tenants visually.

Option B: Have a different set of tiles in the Home group of appsThis is correct. In the SAP IBP Fiori launchpad, the Home page displays app tiles (e.g., Planner Workspaces, Manage Analytics Stories). Administrators or users with appropriate roles can customize the tile layout per tenant, creating distinct sets of tiles. This visual difference helps users identify which tenant they’re in, as per SAP IBP’s Fiori UI customization options.

Option C: Set a separate theme for the Home page in the Web UIThis is correct. SAP Fiori supports theming (e.g., colors, logos) via the UI Theme Designer or tenant-specific settings. By applying a unique theme to each tenant’s Web UI, users can visually distinguish them. This is a standard SAP capability leveraged in IBP to enhance user experience.

Option D: Add the system name and tenant information from the About section to the Home pagesThis is incorrect. The "About" section provides system details (e.g., tenant ID), but there’s no standard feature to dynamically display this on the Home page as a customizable UI element. Manual workarounds (e.g., custom tiles) are possible but not a native option.

Thus, B and C are practical, supported methods to differentiate tenant UIs in SAP IBP’s Webinterface.

Helper key figures in SAP IBP are intermediate calculated key figures that simplify complex logic, per SAP IBP’s calculation documentation.

Option A: Used to break down a large calculation into manageable subcalculationsThis is correct. Helper key figures split complex formulas (e.g., multi-step demand adjustments) into smaller, reusable parts, a primary use case, per SAP IBP’s guides.

Option B: Used by end-users in planning views to help visualize cross-period calculationsThis is incorrect. Helper key figures are backend tools, not typically exposed for visualization; local members serve that purpose in views.

Option C: Used at any level of calculation level except the Request LevelThis is incorrect. Helper key figures can be used at any level, including Request Level, depending on configuration.

Option D: Used when you have more than three inputs at different planning levels in one calculationThis is correct. They manage complexity when combining multiple inputs (e.g., from PERPROD and PERPRODLOC), a common scenario, per SAP IBP’s documentation.

Thus, A and D are relevant use cases, per SAP IBP’s official helper key figure roles.