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認定する-権威のあるC-IBP-2502出題範囲試験-試験の準備方法C-IBP-2502受験対策解説集

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>> C-IBP-2502出題範囲 <<

試験の準備方法-便利なC-IBP-2502出題範囲試験-効率的なC-IBP-2502受験対策解説集

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SAP C-IBP-2502 認定試験の出題範囲：

トピック
出題範囲

トピック 1

サプライプロセスのモデル化：このセクションでは、サプライチェーンプランナーのサプライプロセスの設計と管理に関する専門知識を評価します。調達、在庫管理、供給制約の設定が含まれます。受験者は、サプライネットワークをモデル化し、リソース配分を最適化する能力について評価されます。

トピック 2

ソリューションアーキテクチャとデータ統合：この試験セクションは、SAPデータ統合に携わるソリューションアーキテクトを対象としています。外部データソースをSAPと統合し、システム間のシームレスなデータフローを確保するための基本概念を網羅しています。受験者は、パフォーマンスと信頼性を最適化するためにシステムアーキテクチャを維持する方法を理解する必要があります。

トピック 3

分析とレポート：このセクションでは、SAP 内でレポートを生成および解釈するレポート作成スペシャリストの専門知識を評価します。計画のパフォーマンスに関する洞察を提供する主要な分析ツールとレポート機能を網羅しています。受験者は、ビジネス上の意思決定を支援するためにデータを効果的に抽出、分析、提示する能力について評価されます。

トピック 4

需要計画：このセクションでは、需要計画担当者のスキルを測定し、需要計画の中核概念に焦点を当てます。予測手法、需要センシング、需要伝播の理解が含まれます。受験者は、需要シグナルを管理し、計画をビジネス目標と整合させる能力をテストされます。

トピック 5

主要数値と属性：このセクションでは、サプライチェーンアナリストのスキルを測定し、計画策定に用いられる主要数値と属性に焦点を当てます。正確なデータ表現と意思決定を実現するために、主要数値を定義および設定する方法を網羅しています。また、様々な計画シナリオをサポートする属性を管理する能力も試されます。

トピック 6

セールス＆オペレーションプロセスのモデル化：このセクションはオペレーションマネージャーを対象とし、セールス＆オペレーションプランニングに関する知識を評価します。需要と供給の調整、シナリオプランニング、そしてオペレーション効率を最適化するための意思決定プロセスを網羅します。受験者は、戦略的なビジネス目標をサポートするモデルを構築する能力を評価します。

トピック 7

ユーザーインターフェース：このセクションでは、ビジネスユーザーがSAPインターフェースを効果的に操作・活用するための知識を評価します。様々な機能の操作方法、ビューのカスタマイズ方法、そして効率的な計画・レポート作成のためのUI機能の活用方法を網羅します。受験者は、システム内のデータにアクセスし、解釈する能力を習得していることが求められます。

トピック 8

プランニングオペレーターとアプリケーション：ジョブこのセクションは需要計画担当者向けに設計されており、プランニングオペレーターとアプリケーションジョブの設定と実行に焦点を当てています。これらのツールがどのようにプランニングプロセスを自動化し、システムパフォーマンスを向上させるかを理解する必要があります。受験者は、様々なプランニング機能をサポートするジョブを設定および実行する能力をテストされます。

トピック 9

計画領域の一般的な設定：このセクションはSAPソリューションコンサルタントを対象としており、計画領域の設定について解説します。主要な計画パラメータの定義、構造の設定、そしてビジネスニーズを満たすシステム構成の確保などが含まれます。受験者は、最適なパフォーマンスを実現するために計画領域をカスタマイズする能力をテストされます。

SAP Certified Associate - SAP IBP for Supply Chain 認定 C-IBP-2502 試験問題 (Q50-Q55):

質問 # 50
Which options can be used to reduce the runtimes of a time-series optimizer run? Note: There are 3 correct answers to this question.

A. Eliminate the usage of telescopic time buckets
B. Keep the number of fair share segments small
C. Split into multiple planning areas to support weekly vs. daily planning needs
D. Use non-overlapping networks by using Subnetwork ID maintained at Location-Products to reduce the size of the problem
E. Increase the use of incremental lot size beyond the frozen horizon

正解：A、B、D

解説：
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.

質問 # 51
You are modeling co-products in SAP Integrated Business Planning for Supply Chain. What are some of the properties of co-production you need to be aware of? Note: There are 2 correct answers to this question.

A. The number of co-products that can be defined in the supply model is unlimited
B. The output coefficient is time-dependent and should be modeled as a time series
C. The relationship between main product and co-product is specified in the production source ofsupply
D. Co-production can be modeled only by supply optimizer and finite heuristics

正解：A、C

解説：
Co-products in SAP IBP represent items produced simultaneously with a main product (e.g., in chemical manufacturing). They are modeled in supply planning, typically via the Production Source of Supply master data.
* Option A: The number of co-products that can be defined in the supply model is unlimitedThis is correct. SAP IBP's Production Source Item allows multiple co-products to be linked to a main product via output coefficients. There's no hardcoded limit, though practical constraints (e.g., performance) may apply, as per SAP IBP's supply planning documentation.
* Option B: The output coefficient is time-dependent and should be modeled as a time seriesThis is incorrect. The output coefficient (e.g., 1 unit of main product yields 0.5 units of co-product) is a static attribute in the Production Source Item master data, not a time-dependent key figure by default. Time- series modeling is possible but not required.
* Option C: The relationship between main product and co-product is specified in the production source of supplyThis is correct. In SAP IBP, the Production Source of Supply (e.g., Production Source Header and Item) defines the main product and co-products, including output ratios, as a core feature of supply planning, per official documentation.
* Option D: Co-production can be modeled only by supply optimizer and finite heuristicsThis is incorrect. Co-products are supported by both infinite heuristics (e.g., calculating unconstrained supply) and finite methods (optimizer, heuristics), not limited to finite planning.
Thus, A and C accurately describe co-production properties in SAP IBP, per its supply modeling capabilities.

質問 # 52
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.

A. Calculating the value of inventory on a weekly basis, using static aggregation from the daily level
B. Calculating the value of inventory on any level from the time profile, ensuring flexibility of calculation
C. Calculating how many periods inventory is going to last based on the planned demand
D. Searching for and returning the last not-null value of the inventory key figure

正解：C、D

解説：
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.

質問 # 53
You are evaluating criteria for choosing the solver to support planning for a complex supply chain. What are some of the points that should be reviewed? Note: There are 2 correct answers to this question.

A. Prioritization of demand elements one by one is achieved by unconstrained heuristics
B. Finite heuristics work with demand prioritization
C. Push and Pull production models are used by the optimizer
D. Planning results can be generated faster by the optimizer

正解：B、C

解説：
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 heuristics This 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.

質問 # 54
You want to display and edit data in different Units of Measure (UOM). Which of the following must you consider before you use the UOM? Note: There are 3 correct answers to this question.

A. Conversion to the target unit of measure is handled by the SAP IBP Excel add-in
B. Units of measure are an attribute of a master data type, such as Product
C. Units of measure are usually not time-dependent
D. Analytics allow the user to select the target unit of measure
E. Units of measure are usually not time-independent

正解：B、C、D

解説：
Units of Measure (UOM) in SAP IBP allow data to be displayed and edited in different units (e.g., kg, lbs), configured via master data and UI settings, per SAP IBP's documentation.
* Option A: Analytics allow the user to select the target unit of measureThis is correct. Analytics apps (e.g., Advanced Dashboards) and Excel planning views let users choose the target UOM for display, leveraging conversion factors, per SAP IBP's visualization capabilities.
* Option B: Units of measure are usually not time-independentThis is incorrect. UOMs are typically static (time-independent) unless explicitly modeled as time-dependent (rare), making this a misstatement.
* Option C: Units of measure are an attribute of a master data type, such as ProductThis is correct.
UOM (e.g., Base UOM) is an attribute of the Product master data type, with conversion factors defined in UOM Conversion master data, per SAP IBP's setup.
* Option D: Units of measure are usually not time-dependentThis is correct. UOMs are generally static attributes, not varying by time unless custom-configured, aligning with SAP IBP's standard behavior.
* Option E: Conversion to the target unit of measure is handled by the SAP IBP Excel add-inThis is incorrect. While the Excel add-in displays converted values, the conversion logic is defined in the planning area (via UOM Conversion factors), not handled solely by the add-in.
Thus, A, C, and D are key considerations, per SAP IBP's official UOM handling.

質問 # 55
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C-IBP-2502認証試験に合格したいのは簡単ではなく、いい復習方法は必要です。我々はあなたに詳しい問題と答えがあるC-IBP-2502問題集を提供します。この問題集は我々の経験がある専門家たちによって開発されています。我々のすばらしいC-IBP-2502問題集はお客様の試験への成功を確保することができます。

C-IBP-2502受験対策解説集: https://www.it-passports.com/C-IBP-2502.html