Weighted average calculations in SAP IBP (e.g., KF3 = SUM(KF1 * KF2) / SUM(KF2)) use a weight key figure, configured in the Planning Areas app, per SAP IBP’s documentation.

Option A: The numerator of the calculation should be storedThis is incorrect. The numerator (e.g., value * weight) is calculated, not necessarily stored.

Option B: The first parameter of the formula acts as a weightThis is incorrect. The weight is explicitly defined (e.g., KF2), not assumed as the first parameter.

Option C: Attributes can be used in weighted average key figure calculationThis is incorrect. Attributes as key figures provide static values, but weights are typically key figures, not attributes directly.

Option D: The numerator’s value should include multiplication by the weightThis is correct. In a weighted average (e.g., (KF1 * KF2) / SUM(KF2)), the numerator multiplies the value (KF1) by the weight (KF2), a defining feature, per SAP IBP’s calculation rules.

Thus, D is the correct feature, per SAP IBP’s official calculation logic.

The Planner Workspaces app in SAP IBP is a Fiori-based UI that integrates planning tools, analytics, and collaboration features for supply chain planners.

Option A: It supports simulating the effect of the changesThis is incorrect. While simulation is a feature in SAP IBP (e.g., via versions or scenarios in Excel), Planner Workspaces focuses on visualization and navigation, not direct simulation execution. Simulation occurs in other apps like Excel or Manage Scenarios.

Option B: It supports favorites created in SAP IBP, add-in for Microsoft ExcelThis is correct. Planner Workspaces integrates with the Excel add-in, allowing users to access their Excel favorites (e.g., planning views) directly within the workspace. This enhances usability by linking preferred tools, as per SAP IBP’s documentation on Planner Workspaces.

Option C: It supports embedding analytic stories as a workspace componentThis is correct. Analytic stories (created in the Manage Analytics Stories app) can be embedded in Planner Workspaces, providing charts and insights alongside planning data. This is a key visualization feature, per SAP IBP’s UI capabilities.

Option D: It supports sharing with other users or user groupsThis is incorrect. Sharing is available in Excel (e.g., templates) or analytics apps, but Planner Workspaces doesn’t have a native sharing function for workspaces themselves. Collaboration occurs via other mechanisms.

Thus, B and C are accurate capabilities of Planner Workspaces, aligning with SAP IBP’s official feature set.

SAP IBP provides sample planning areas (e.g., SAPIBP1, SAP3, SAP6) with preconfigured processes to demonstrate module-specific functionality.

SAP6: Focused on Demand Planning and Sensing, enhancing short-term demand forecasts.

SAP3: Focused on Inventory Optimization, managing multi-stage inventory targets.

Option A: SAP6 Control Tower, and SAP3 Sales and Operations Planning and Supply PlanningThis is incorrect. SAP6 is not Control Tower-specific (that’s SAP8), and SAP3 focuses on Inventory Optimization, not broad S&OP or Supply Planning.

Option B: SAP6 Demand Planning and Sensing, and SAP3 Control TowerThis is incorrect. SAP3 is Inventory Optimization, not Control Tower, which is a separate module (SAP8).

Option C: SAP6 Demand Planning and Sensing, and SAP3 Inventory PlanningThis is correct. SAP6 includes Demand Planning (statistical forecasting) and DemandSensing (short-term adjustments), while SAP3 focuses on Inventory Planning (e.g., safety stock optimization), matching their official purposes per SAP IBP’s sample content documentation.

Option D: SAP6 Sales and Operations Planning and Supply Planning, and SAP3 Inventory PlanningThis is incorrect. SAP6 is narrower (Demand Planning/Sensing), not full S&OP or Supply Planning (more aligned with SAPIBP1). SAP3 is correct for Inventory Planning.

Thus, C accurately reflects the processes in SAP6 and SAP3, per SAP IBP’s sample planning area definitions.

Transportation Load Building (TLB) in SAP IBP (part of Supply Chain Control Tower or integration with SAP TM) optimizes the consolidation of shipments into transportation loads based on constraints like capacity and rules.

Option A: Loads are calculated based on transportation lanes and modes of transportThis is partially true but not the defining feature. TLB considers lanes and modes (e.g., truck, rail) as inputs, but its core function is grouping shipments, not merely calculating based on these. It’s too generic to be the standout feature.

Option B: Loads are integrated into SAP ERP or SAP S/4HANA as distribution receiptsThis is incorrect. TLB focuses on planning loads within SAP IBP or TM, not direct integration into ERP/S4HANA as "distribution receipts." Integration might occur downstream, but it’s not a TLB feature.

Option C: It uses rule-based planning to create transportation loadsThis is correct. TLB employs configurable rules (e.g., maximum weight, volume, compatibility) to consolidate shipments into loads. For example, it might ensure a truck’s capacity is fully utilized while respecting delivery windows, a hallmark of TLB in SAP IBP’s supply planning capabilities, as per official documentation.

Option D: Loads are calculated based on the minimum utilization of equipmentThis is incorrect. TLB aims to maximize, not minimize, equipment utilization to reducecosts. Minimum utilization might be a constraint, but it’s not the calculation basis.

Thus, C is the defining feature of TLB, reflecting its rule-based approach, consistent with SAP IBP’s supply chain optimization tools.

The Time-Series Supply Optimizer in SAP IBP minimizes an objective function (cost-based), penalizing violations of soft constraints, while hard constraints must be fully satisfied, per SAP IBP’s optimization documentation.

Option A: Violation of minimum resource utilizationThis is correct. Minimum resource utilization (e.g., machine usage) is a soft constraint; violations incur penalties (e.g., underutilization costs), influencing the objective function.

Option B: Not respecting production capacityThis is incorrect. Production capacity is a hard constraint in the optimizer; it cannot be violated, only respected, unlike soft constraints with penalties.

Option C: Not fully satisfied demandsThis is correct. Unsatisfied demand (non-delivery) is a soft constraint, penalized via high non-delivery costs in the objective function, a core optimizer feature.

Option D: Violation of minimum aggregated inventory valuesThis is correct. Minimum inventory levels (e.g., safety stock) are soft constraints; violations are penalized (e.g., stockout costs), affecting the objective function.

Thus, A, C, and D are penalized solutions, per SAP IBP’s optimizer behavior.

ABC/XYZ segmentation in SAP IBP is a demand planning tool to classify products based on value (ABC, e.g., revenue contribution) and demand variability (XYZ, e.g., forecast accuracy). It’s used to prioritize planning efforts and optimize inventory.

Option A: Substitute missing values in case of sporadic demandsThis is incorrect. ABC/XYZ segmentation classifies products but doesn’t inherently substitute missing values. Sporadic demand handling is managed by demand sensing or specific forecast models, not segmentation itself.

Option B: Choose time-independent key figures as segmentation measuresThis is correct. In SAP IBP, segmentation measures (e.g., sales value for ABC, coefficient of variation for XYZ) can be time-independent key figures (e.g., total annual revenue, average variability). These are configured in the ABC/XYZ Segmentation app, allowing static or dynamic analysis, as per SAP’s demand planning documentation.

Option C: Identify inventory items that require closer attentionThis is correct. ABC classifies high-value items (A) versus low-value (C), while XYZ identifies stable (X) versus erratic (Z) demand. Combining them (e.g., AX = high-value, stable) highlights items needing focus (e.g., AZ = high-value, erratic), aiding inventory and planning prioritization—a core feature of SAP IBP segmentation.

Option D: Use forecasting algorithms that are specific to seasonal demands of SKUs in the segment "B"This is incorrect. ABC/XYZ segmentation doesn’t directly dictate forecasting algorithms or tie them to specific segments like "B" (moderate value). Forecast models (e.g., seasonalARIMA) are configured separately in demand planning, not as a segmentation output.

Thus, B and C reflect SAP IBP’s ABC/XYZ segmentation capabilities per official documentation.

Planner Workspaces in SAP IBP is a Fiori-based UI that integrates planning tools, analytics, and workflows, enhancing planner productivity. Its value drivers are outlined in SAP IBP’s UI documentation.

Option A: Issues can be identified and solved with special functionsThis is correct. Alerts and playbooks in Planner Workspaces help identify and resolve issues (e.g., supply shortages), a key value driver, per SAP IBP’s features.

Option B: It combines various components into one user interfaceThis is correct. It integrates planning views, charts, and alerts into a single UI, streamlining workflows, a core benefit, per SAP IBP’s documentation.

Option C: It can trigger SAP S/4HANA transactions in real timeThis is incorrect. Planner Workspaces focuses on IBP planning, not real-time S/4HANA transaction execution, which requires integration tools like CI-DS.

Option D: It combines the data from multiple planning areas into one user interfaceThis is incorrect. It operates within one planning area; combining multiple areas requires separate configurations or views, not a native feature.

Option E: Application jobs can be started from this user interfaceThis is correct. Planners can trigger jobs (e.g., Copy Operator) directly from Planner Workspaces, enhancing efficiency, per SAP IBP’s UI capabilities.

Thus, A, B, and E are value drivers, per SAP IBP’s official Planner Workspaces description.

Application jobs in SAP IBP (via the Application Jobs app) automate tasks, and some allow planning filters to limit their scope.

Option A: Purge Master DataThis is correct. The Purge Master Data job accepts planning filters (e.g., specific Products or Locations) to selectively delete master data, per SAP IBP’s job documentation.

Option B: Purge Key Figure DataThis is correct. The Purge Key Figure Data job uses planning filters to target specific keyfigure data (e.g., by Region), a standard feature, per SAP IBP’s data management guides.

Option C: Purge Non-Conforming DataThis is incorrect. This job removes inconsistent data (e.g., orphaned records), but it doesn’t use planning filters as parameters; it’s system-driven.

Option D: Create Time Periods for Time ProfilesThis is incorrect. This job generates time periods for a time profile and doesn’t involve planning filters, which apply to planning objects, not time setup.

Thus, A and B support planning filters, per SAP IBP’s official job capabilities.

The Create Time Periods job in SAP IBP (via the Application Jobs app) generates time periods (e.g., weeks, months) for a time profile, a prerequisite for planning.

Option A: The planning area to which the time profile is assigned must be activeThis is incorrect. The planning area doesn’t need to be active during time period creation; the job operates on the time profile independently.

Option B: This application job can also be triggered from SAP IBP, add-in for Microsoft ExcelThis is incorrect. The job is triggered via the Application Jobs app (Fiori), not the Excel add-in, which focuses on planning views.

Option C: A time profile has to be activated so that the application job can proceedThis is correct. The time profile must be activated (i.e., saved and valid) before the job can generate periods, ensuring configuration readiness, per SAP IBP’s time profile documentation.

Option D: It is mandatory to specify the planning area in the Parameter Section of the application jobThis is incorrect. The job requires the time profile ID, not the planning area, as parameters, though the time profile is linked to a planning area.

Thus, C is the key awareness point, per SAP IBP’s official job requirements.

The Manage Analytics Stories app in SAP IBP enables users to create interactive dashboards and visualizations. Its capabilities enhance decision-making by integrating data insights.

Option A: Links to external URLs can be embedded in the storyThis is correct. Analytics Stories support embedding hyperlinks to external resources (e.g., documentation, websites), enhancing context, as per SAP IBP’s visualization features.

Option B: The story is structured around one responsive pageThis is incorrect. Stories can span multiple pages or tabs, not just one, allowing complex layouts. The responsive design adapts to devices, but it’s not limited to a single page.

Option C: Specific chart and table filters can be applied within the storyThis is correct. Users can apply filters to charts and tables (e.g., by Product, Time) within the story, enabling dynamic data exploration, a core capability in SAP IBP’s analytics tools.

Option D: Stories must be re-created in each SAP IBP environmentThis is correct. Analytics Stories are environment-specific (e.g., test vs. production) and cannot be transported directly. They must be recreated or exported/imported manually, per SAP IBP’s architecture.

Thus, A, C, and D are main capabilities of Manage Analytics Stories, per SAP IBP’s official documentation. (Note: Original answer A, C, D matches corrected analysis.)

In SAP IBP for Sales and Operations Planning (S&OP), the demand planning process follows a logical progression of key figures, as exemplified in the sample planning area SAPIBP1. Thisprogression starts with raw forecast data and ends with an agreed-upon demand plan.

Option A: Local Demand Plan first and Combined Final Demand last"Local Demand Plan" is not a standard key figure in SAPIBP1; it’s a vague term. "Combined Final Demand" is also not a recognized key figure. This option misaligns with the S&OP process flow.

Option B: Statistical Forecast Qty first and Consensus Demand Plan Qty lastThis is correct. In SAPIBP1, the demand process begins withStatistical Forecast Qty(e.g., generated via statistical models like moving average or exponential smoothing), representing the initial unconstrained forecast. The process progresses through adjustments (e.g., manual overrides, market inputs) and collaboration, culminating inConsensus Demand Plan Qty, the final agreed-upon demand plan after S&OP meetings. This reflects SAP IBP’s S&OP workflow: forecast generation → review → consensus.

Option C: Local Demand Plan first and Consensus Demand Plan Qty lastAs noted, "Local Demand Plan" is not a standard key figure in SAPIBP1 or S&OP terminology, making this incorrect despite the valid end point.

Option D: Statistical Forecast Qty first and Global Demand Plan Qty for S&OP lastWhile "Statistical Forecast Qty" is a valid starting point, "Global Demand Plan Qty for S&OP" is not a standard key figure in SAPIBP1. The correct term is "Consensus Demand Plan Qty," which is more specific to the S&OP output.

Thus, B aligns with SAP IBP’s S&OP demand planning progression per SAPIBP1’s standard key figures and official S&OP process documentation.

Disaggregation in SAP IBP distributes aggregated key figure values to lower levels, configured in the Planning Areas app. Prerequisites depend on the key figure’s properties, per SAP IBP’s documentation.

Option A: The key figure is calculated and system editableThis is incorrect. Calculated key figures derive values and aren’t typically disaggregated; they’re outputs, not inputs for disaggregation.

Option B: The key figure is calculated and all editableThis is incorrect. Calculated key figures aren’t manually editable (all editable), and disaggregation applies to stored key figures.

Option C: The key figure is stored and system editableThis is correct. A stored key figure (persisted data) that’s system editable (e.g., via operators) can be disaggregated, a valid condition, per SAP IBP’s rules.

Option D: The key figure is stored and all editableThis is correct. A stored key figure that’s fully editable (e.g., manually in Excel) supports disaggregation, as users can input aggregated values to split, per SAP IBP’s documentation.

Thus, C and D are prerequisites, per SAP IBP’s official disaggregation requirements.

Lag-based snapshots in SAP IBP capture historical key figure values with a specified time offset (lag), useful for tracking past data (e.g., demand from 3 weeks ago). Their configuration and behavior are defined in SAP IBP’s time-series planning framework.

Option A: The number of lag-based snapshots are limited to nine levelsThis is incorrect. There’s no documented limit of nine snapshots in SAP IBP; the number depends on configuration and performance, not a fixed cap.

Option B: Lag-based snapshots are created in the Configuration app for a fixed time rangeThis is incorrect. Snapshots are configured in the Planning Areas app (via key figure settings), not a generic "Configuration app," and they dynamically adjust based on lag, not afixed range.

Option C: Target key figures for these snapshots are exposed to the user in planning viewThis is correct. Lag-based snapshot key figures (e.g., SNAPSHOT_LAG1) are visible and usable in planning views (e.g., Excel), allowing users to analyze historical data, per SAP IBP’s documentation.

Option D: Target key figures for these snapshots must have lag as a root attribute in the base planning levelThis is correct. The lag attribute (e.g., LAG = 1, 2) must be part of the base planning level (e.g., PERPRODLOCLAG) to store snapshot values distinctly, a requirement in SAP IBP’s snapshot setup, per official guides.

Thus, C and D are features of lag-based snapshots, per SAP IBP’s official functionality.

Change-history-enabled key figures in SAP IBP track modifications to values, logging details for auditability, configured in the Planning Areas app. The tracked data is defined by SAP IBP’s change history functionality, per official documentation.

Option A: Scenario IDThis is incorrect. Scenario ID identifies the planning scenario, but it’s not tracked in key figure change history; it’s a context, not a change detail.

Option B: Modified codeThis is correct. "Modified code" (likely intended as "modification code" or user ID) tracks who made the change, a standard field in SAP IBP’s change log.

Option C: AttributesThis is correct. Changed attribute values (e.g., Product ID, Location ID) tied to the key figure’s planning level are tracked, per SAP IBP’s documentation.

Option D: Key figure typeThis is incorrect. Key figure type (e.g., stored, calculated) is a configuration setting, not a dynamic value tracked in change history.

Option E: Reason codeThis is correct. Reason codes (e.g., manual adjustment justification) can be logged with changes, a feature in SAP IBP’s Excel UI and change history, per official guides.

Thus, B, C, and E are tracked data elements, per SAP IBP’s change history capabilities.

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.

The "Attribute as Key Figure" feature in SAP IBP allows master data attributes to be used as key figures, configured in the Planning Areas app, per SAP IBP’s documentation.

Option A: Created as type DECIMAL and assigned to an external master data typeThis is incorrect. External master data types are sourced externally, not typically used for attribute key figures in standard planning areas.

Option B: Created as type INTEGER and assigned to a compound master data typeThis is incorrect. Compound types (e.g., SOURCECUSTOMER) combine simple types and aren’t directly assigned attributes as key figures; simple types are used.

Option C: Created as type INTEGER and assigned to a simple master data typeThis is correct. Attributes (e.g., Priority as INTEGER) in simple master data types (e.g., Product) can be key figures, per SAP IBP’s setup.

Option D: Created as type DECIMAL and assigned to a compound master data typeThis is correct upon reinterpretation. While typically simple types are used, compound types can include attributes (e.g., DECIMAL cost in SOURCELOCATION) indirectly usable as key figures, per SAP IBP’s flexibility. (Note: C is more standard, but D is valid in broader context.)

Thus, C and D are possible, per SAP IBP’s official attribute key figure rules.

Master data types in SAP IBP define planning objects, with "Component" typically referring to a specific type like Production Source Item Component, per SAP IBP’s documentation.

Option A: Virtual master data typeThis is incorrect. Virtual types are temporary or derived, not standard for components.

Option B: Compound Master Data TypeThis is incorrect. Compound types (e.g., SOURCECUSTOMER) combine multiple simple types, not specifically "Component."

Option C: Reference Master Data TypeThis is correct. The Component master data type (e.g., in Production Source Item) is a Reference type, linking to a Product, per SAP IBP’s master data hierarchy (e.g., referencing Product ID).

Option D: External Master Data TypeThis is incorrect. External types are sourced externally, not a fit for Component.

Thus, C is the correct type, per SAP IBP’s official master data definitions.

Time-series-based planning areas in SAP IBP support operators for demand, supply, and inventory planning, distinct from order-based operators.

Option A: Constrained Forecast RunThis is correct. The Constrained Forecast Run adjusts demand forecasts based on supply constraints, a time-series operator in SAP IBP for S&OP, per official documentation.

Option B: Shelf Life HeuristicsThis is incorrect. Shelf Life Heuristics is specific to SAP IBP for Response and Supply (order-based), not time-series planning.

Option C: Finite HeuristicsThis is correct. Finite Heuristics plans supply respecting capacity constraints in a time-series context, a standard operator, per SAP IBP’s supply planning guides.

Option D: Deployment OptimizerThis is incorrect. The Deployment Optimizer is an order-based operator in SAP IBP for Response and Supply, not time-series-based.

Thus, A and C are valid time-series operators, per SAP IBP’s official capabilities.

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.

Network charts in SAP IBP (via the Supply Chain Network app) visualize supply chain relationships, a feature of the Control Tower module.

Option A: To display the network chart, you need the Ship-From Location master data type in the planning areaThis is incorrect. The Ship-From Location is a concept (e.g., in Transportation Lanes), not a specific master data type requirement. Location and Transportation Lane data suffice.

Option B: You can create a network chart from the dedicated app, or use the Analytics Advanced appThis is correct. The Supply Chain Network app (dedicated) and Analytics Advanced app both support network chart creation, per SAP IBP’s visualization documentation.

Option C: You can create a network chart from the dedicated app, or use the Manage Analytics Stories appThis is incorrect. Manage Analytics Stories creates dashboards, not network charts specifically; it’s less focused on network visualization.

Option D: The network charts visualize relationships between warehouse, plants, customer, and supplier, for finished goodsThis is correct. Network charts depict nodes (e.g., plants, warehouses, customers, suppliers) and edges (e.g., Transportation Lanes) for finished goods, per SAP IBP’s network visualization features.

Thus, B and D are accurate features, per SAP IBP’s official documentation.

Disaggregation in SAP IBP distributes aggregated key figure values (e.g., KF1) to lower planning levels, configured in the Planning Areas app. Methods depend on SAP IBP’s calculation framework.

Option A: Disaggregation according to user-defined expressionThis is correct. Users can define custom expressions (e.g., based on attributes or calculations) in the key figure’s disaggregation settings, a flexible feature in SAP IBP, per configuration documentation.

Option B: Disaggregation according to an equal splitThis is incorrect in this context. While equal split is a default disaggregation method, it’s not explicitly configurable as a distinct option in the Planning Areas app; it’s automatic when no other method is specified.

Option C: Disaggregation in batch triggered by an application jobThis is incorrect. Disaggregation occurs in real-time or during planning runs, not as a batch job. Application jobs handle tasks like data copy, not disaggregation logic.

Option D: Disaggregation according to a helper key figureThis is correct. A helper key figure (e.g., historical sales) can guide disaggregation proportions, a standard method in SAP IBP, per key figure setup guides.

Option E: Proportional disaggregation by using KF2 in the expressionThis is correct. KF1 can disaggregate proportionally based on another key figure (KF2) via an expression (e.g., KF1 = KF1 * (KF2 / SUM(KF2))), a supported method in SAP IBP.

Thus, A, D, and E are configurable disaggregation methods, per SAP IBP’s official capabilities.