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Why Data Quality in SAP IBP?
The success of SAP IBP hinges on the availability of quality data from all relevant sources across the organization. Without this foundation of high-quality data, the collaborative and cross-functional nature of IBP will be severely hampered, leading to flawed insights, poor decisions, and an ineffective planning process that fails to deliver the expected business benefits.
Therefore, organizations implementing SAP IBP must prioritize data quality initiatives as a critical component of their implementation and ongoing operations to ensure that they can leverage the full potential of the integrated planning platform. This often involves establishing data governance processes, data quality and validation routines, and ongoing monitoring of data quality across all the heterogeneous systems and departments.
I, as an IBP solution architect and consultant, have seen data quality impact on various functions and across all levels.
1. Accurate Forecasting and Demand Planning:
- IBP heavily relies on accurate demand forecasts to plan production, inventory, and supply chain activities.
- Poor data quality in historical sales data, market trends, or customer information will lead to inaccurate forecasts, resulting in either overstocking (leading to waste and costs) or understocking (leading to lost sales and customer dissatisfaction).
2. Reliable Supply Planning:
- Accurate demand forecasts and reliable data on current inventory levels, lead times, and supplier capabilities are essential for effective supply planning.
- Inaccurate data in these areas can lead to production delays, material shortages, and increased costs.
3. Effective Financial Planning:
- IBP integrates financial planning with operational plans. Accurate financial data, including costs, revenues, and profitability, is crucial for making sound financial decisions.
- Poor data quality can lead to inaccurate financial projections and budgets, impacting the overall financial health of the organization.
4. Informed Decision-Making:
- IBP aims to provide a holistic view of the business, enabling informed decision- making across different departments.
- If the underlying data is inaccurate, incomplete, or inconsistent, the insights derived from IBP will be flawed, leading to poor strategic and operational decisions.
5. Seamless Collaboration:
- IBP fosters collaboration among different departments. Consistent and reliable data ensures that all stakeholders are working with the same information, facilitating effective communication and alignment.
- Data quality issues can create friction and mistrust among teams, hindering the collaborative nature of IBP.
6. Performance Measurement and Improvement:
- IBP involves monitoring key performance indicators (KPIs) to track progress and identify areas for improvement.
- Accurate data is essential for measuring performance accurately and identifying the root causes of any issues. Poor data quality can lead to misleading performance metrics and ineffective improvement initiatives.
Data quality is paramount for IBP success. Without high quality data, the benefits of IBP cannot be fully realized, and the organization risks making poor decisions that can negatively impact its bottom line and overall performance.
5C Framework for SAP IBP Data Validation
The proposed 5Cs are the core traits which determine whether the data is fit for the SAP IBP planning purpose. By adhering to these 5C's, organizations can improve their data quality, leading to more reliable insights, better decision-making, and increased operational efficiency. It is important to note that maintaining high data quality is an ongoing process that requires continuous monitoring and improvement.
- Conformity
- Consistency
- Completeness
- Comprehensiveness
- Concurrency
Figure 1: 5Cs of Data Quality Framework
1. Conformity
This aspect of the SAP IBP data quality ensures that data adheres to predefined rules and standards regarding format, type, and size.
- Case for the predefined rules: In SAP IBP there are predefined special characters which cannot be used. (Refer to SAP note: 2453887)
Figure 2: SAP Note 2453887 listing restricted characters
b. Case for the format, type, and size: In SAP IBP, each attribute that exists has a format, type and size associated. All the value must comply by the formats, types, and sizes otherwise there is a risk of the job failure with fatal error.
| FORMAT TYPE | SIZE |
| NVARCHAR (with/without Only Upper Case) | Character Length (max length up to 450) |
| DECIMAL | 18,6 |
| INTEGER | Whole Number |
| TIMESTAMP | YYYYMMDD hh:mm:ss.000 |
Table 1: SAP IBP Format Types and Sizes
2. Consistency
This aspect of the SAP IBP data quality ensures uniformity, coherency, and avoidance of contradictions across different data sources, planning areas, data objects and time periods. Consistency ensures that information is uniform and comparable throughout the organization. However, when SAP IBP receives data from multiple sources such as ERP systems, data warehouses, cloud-based data lakes, third-party systems, home-grown applications, production planning system, transport planning system and planners' Excel sheets, maintaining data consistency becomes key area to focus on. This diversity of data sources, if ignored, can lead to discrepancies in data formats, definitions, and quality, potentially compromising the accuracy of SAP IBP's planning processes.
To ensure consistency in master Data SAP IBP provides some in-build checks which must be configured like attribute checks and mandatory attributes.
Figure 3: IBP Master Data object consistency checks with attribute checks
For IBP transactional data, we can ensure consistency by adding a mandatory attribute from compound master data in planning level for the key figure. Such configuration avoids inconsistent transactional data flowing into the system.
Figure 4: IBP mandatory attribute used in base planning level of key figures
It is important to understand that SAP IBP does not inherently guarantee consistency within object hierarchies such as Product, Customer, and Location hierarchies. Therefore, the Customer and System Integrator's consultants must collaboratively develop a strategy to ensure consistency in parent-child relationships (1:N cardinality) for hierarchical data objects. This proactive approach is crucial for accurate aggregation and disaggregation, enabling effective and actionable insights.
Figure 5: IBP Data Hierarchy with Parent Child relationship
To ensure the correct parent-child relationships, one of the approaches is to extract a parent-child combination in excel sheet and create a pivot table on unique records. All children must have single parent otherwise aggregation and disaggregation will be wrong, leading to wrong planning results.
3. Completeness
The completeness of data refers to the state where all the necessary and relevant data points required for the specific planning process and the overall IBP framework are present and available within the system. It means that there are no significant gaps or missing pieces of information that hinder the ability to perform accurate analysis, generate reliable plan and make informed decisions.
Figure 6: Incomplete/Broken Supply Network
Figure 6 clearly demonstrates the incompleteness of the data from the supply chain network where capacity data at resource is missing along with a broken network with respect to missing lanes. Such an incomplete dataset will generate improper planning results.
To avoid the incomplete data flowing into the IBP system, we need to develop interfaces outside the IBP system using ABAP validations in SAP S4 or BW4HANA or CIDS to identify the incompleteness and generate a report for correction by clients IT Team / Data Management Team. By using the "Manage Rules for Master Data Maintenance" feature in IBP, we can effectively define the correct "Subnetwork" node and diagnose which node is disrupting the supply chain network. Example: All Location product combinations to pass through following checks updating a corresponding custom attribute which finally impacts the subnetwork.
1.Inbound: Master data rule to check for Location source in Location Source object.
2.Inbound: Master data rule to check source production in Source Production Header object.
3.Outbound: Master data rule to check Ship-from with product combination in Location Source object.
4.Outbound: Master data rule to check Location Product combination in Source Customer object.
5.Outbound: Master data rule to check Location Product combination in production source Item.
* This is just an approach which may differ from project to project and network schema variations.
4. Comprehensiveness
Data Comprehensiveness refers to the degree to which all the necessary and relevant data required to support the IBP processes, models, and overall planning objectives is available, accessible, and integrated within the IBP system. It goes beyond just having completeness. It entails:
- Breadth of Data: Having data across all relevant dimensions and business areas that are within the scope of the IBP implementation. This includes data related to sales, demand, supply, inventory, finance, and potentially other relevant areas.
- Depth of Data: Having data at the appropriate level of detail required for the planning activities, ensuring that data is available for all the products, customers, locations, and other relevant entities that are included in the IBP planning scope.
Figure 7: Data Comprehensiveness covers all the IBP data objects including each attribute
Example for missing comprehensiveness can be missing key attribute values like Lead Times, Lot Sizes, rounding values, Quota split ratios, Cost rates, On-hand Inventory for few combinations only in IBP system. It may cause unreliable planning results. To systematically capture all such cases, IBP custom alerts can be valuable to report missing attributes. Along with this the IBP’s feature like “Discover Pattern using Machine Learning”, “Find Numerical Outliers” can help identify the completeness gaps.
Figure 8: IBP Feature for data pattern discovery and numeric outliers
5. Concurrency
Every business executive stives to strike alignment between business strategic planning and execution system. Concurrency in planning and execution represents a paradigm shift from traditional sequential approaches, enabling organizations to adapt dynamically in fast-paced environments. This methodology interweaves planning and action in real time, allowing adjustments based on evolving conditions while maintaining operational continuity. The planning and execution should be coupled like Yin-Yang.
Figure 9: Yin-Yang of Planning and Execution
From IBP planning perspective the concurrency is achieved while keeping it updated with the latest execution data. The execution data represents the latest customer orders, internal goods movements between Plants and RDCs, latest production orders, and vendor related purchase orders are integrated daily or even lesser frequency to IBP. This enables IBP planners to have complete visibility of the execution system, and they can align the plan or coordinate with execution teams to keep working on the same organizational objective.
Conclusive Remarks
At its core, the success of SAP IBP depends entirely on high-quality data. Without a solid foundation of accurate, reliable information, the platform's ability to facilitate collaboration and integrated planning is undermined. This leads to flawed insights, poor decision-making, and ultimately, a failure to deliver the expected business benefits.
Key impacts of poor data quality include:
Inaccurate forecasting and supply planning, resulting in issues like over- or under-stocking.
Flawed financial planning and projections.
Uninformed decisions because the underlying data is inconsistent or incomplete.
Friction among teams due to a lack of trust in shared data.
Misleading performance metrics, which hinder effective improvement initiatives.
Therefore, prioritizing data quality through robust governance, validation, and monitoring is not just a best practice—it's essential for organizations to fully leverage SAP IBP's potential. The 5C Validation Framework (Conformity, Consistency, Completeness, Comprehensiveness, Concurrency) provides a core set of traits to ensure data is fit for purpose, leading to more reliable insights and greater operational efficiency.
References
- Article by Spyrodon D. Tsolas, M.M. Faruque Hsan, “Survivability‐aware design and optimization of distributed supply chain networks in the post COVID‐19 era”, Journal of Advanced Manufacturing and Processing , June 2021
- https://www.linkedin.com/pulse/sap-ibp-explained-how-integrated-planning-technology-shaping-pandey-f...
- https://learning.sap.com/learning-journeys/exploring-planning-processes-in-sap-ibp/sap-best-practice...
- https://learning.sap.com/learning-journeys/discovering-sap-ibp-for-inventory-planning-and-optimizati...
- https://www.linkedin.com/pulse/understanding-difference-between-planning-executing-systems-spires-d3...
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