Recognizing the Possibilities of SAP Integrated Business Planning

The Integrated Sales & Operations Planning (S&OP) and Sales & Operations Execution (S&OE) process ensures a seamless alignment between long-term strategic objectives, tactical planning, and short-term execution within the automotive supply chain. The process is structured across three horizons:

This process focuses on strategic forecasting and capacity planning to prepare for future demand and market changes. Usually, the planning granularity is quite low, meaning manufacturer focus on the prediction of sales numbers for vehicles on a model/platform level in different regions/markets. This demand is matched with the high-level capacities of the production and distribution network as well as with the procurement quantities for strategic components. Due to its simplified nature, the mid- to long-term planning can exclusively be carried out with SAP Integrated Business Planning. Key activities include:

• Manage Scoping: Adjust/create/integrate master data to reflect future changes in the products and the supply network (create new products, define sales markets/customers, define planning bills of material etc.)
• Create Long-Range Vehicle Volume Forecast: Develop forecasts for vehicle sales volumes in various market/regions
• Create Long-Range Strategic Component Forecast: Plan for production and/or procurement requirements for strategic components (for example, powertrains)
• Create Management Reports: Generate insights to inform stakeholders and enable strategic decision-making (portfolio strategy, supply network design decisions etc.)

This process ensures alignment between forecasted demand and supply capabilities within the S&OP horizon (typically up to 24 months).

Key activities involve:

• Predict and Plan Vehicle Sales, Registrations, and Deliveries: Use advanced demand forecasting tools to predict market needs
• Manage Opportunities and Risks: Identify and mitigate potential risks while capitalizing on market opportunities
• Calculate Optimal Inventory Targets (ECS): Optimize inventory levels to balance costs and responsiveness
• Determine Production Demand: Determine the production demand at the production sites by propagating the customer demands from the markets to the production sites while considering lead times, stock etc.
• Predict and Plan Option Take Rates: Forecast the take rates for vehicle options and configurations
• Generate Forecast Orders and Consume Sales Orders: Derive a plan of fully-configurable vehicles/orders while considering existing sales orders
• Determine and Evaluate Supply Requirements: Check violations of vehicle assembly, internal manufacturing, and external procurement constraints
• Generate Supply Plan (Production Answer): Create a feasible supply plan which respect production, distribution, and procurement constraints
• Check and Refine Market Allocations (Commercial Answer): Adjust supply allocations based on regional and market-specific factors as well as defined business rules
• Compare Costed Supply Plan vs. Budget in SAP Analytics Cloud (SAC): Ensure financial alignment with supply chain strategies

Within the short-term horizon, the focus is on operational implementation of the plan and the agility to meet real-time market demands. Key activities include:

• Create Mid-Term Production Plan (Model-Mix Planning): Generate an optimized production plan of fully configured vehicles/orders based on time buckets
• Create Short-Term Production Sequence: Generate an optimized sequence of fully configured vehicles/orders
• Calculate Material/Part Requirements: Determine materials requirements for internal & external suppliers
• Collaborate with Suppliers on Orders: Use electronic data interchange to collaborate with suppliers and ensure seamless execution

Through this integrated process, automotive supply chains achieve alignment across all planning horizons, enabling efficient production, cost control, and responsiveness to market dynamics.

Due to the large number of options which can be selected when configuring a vehicle, it's usually not possible to manually plan and check the volume of all possible fully configured variants. Thus, Demand Planning at OEMs starts at the level of partially configured vehicles - in the following referred to as "planning variants". Only some key features of the vehicle are defined for a planning variant. Examples for such key features are the model, the trim line, the engine/powertrain or the emission standard. The remaining features/options of the vehicle are planned as a percentage linked to the planning variant ("take rate"). Take rates are often planned on an aggregated level, for example, the model, and then applied to the lower levels, so all planning variants which are associated with this model do have the same take rate. Planning combinations consist usually of the planning variant itself and the market/customer it is sold to. One of the challenges in planning is to determine the valid combinations of planning variants and markets, respecting engineering, product definition as well as marketing changes.

The planning of the planning variants as well as the take rates is carried out in SAP Integrated Business Planning. The standard functionalities available in IBP for Demand can be leveraged for this purpose (see Learning Journey, Discovering SAP IBP for Demand):

• Product segmentation
• ABC classification
• XYZ Analysis
• Time series analysis
• Forecast automation

Statistical Forecasting can be leveraged to predict vehicle sales based on historical sales figures. It can be used by planners as a starting point for planning or as an additional input to refine an existing plan. IBP offers a variety of powerful forecasting models which allow the prediction based on historical sales as well as on other internal or external factors. This is in particular interesting for the prediction of option take rates which might correlate with other industry or cross-industry trends (for example, correlation of vehicle body colors with fashion trends).

• Local Demand Planning:
• Demand planning at location/market
• Measure forecast accuracy
• Bias analysis
• What If Analysis

Vehicle sales are usually forecasted by the local sales and marketing experts in the markets. SAP IBP offers simplified planning views on web basis to easily involve a large number of local planners in the process without extensive training on the tool. The local forecasts are dynamically consolidated to higher levels (region, global) based on flexible aggregation rules.

• Compare Budget / AOP
• Marketing Forecast
• Sales Forecast
• Demand Planner Qty0
• Consensus Demand Plan
• What If Analysis

Global demand planners review the aggregated sales forecast submitted by the markets and compare them with budget and marketing plans. SAP IBP allows to compare different scenarios side-by-side to perform what-if analysis.

S/4HANA's Variant Configuration (VC) data needs to be leveraged to plan highly configurable products with SAP Integrated Business Planning (IBP). Third party applications like SBP's Variant Configuration Planner allow the integration and usage of VC data in SAP IBP. Partially configured planning variants as well as the associated options are defined based on VC characteristics (features) and their dependencies/rules. These technical and commercial rules determine the valid characteristic value combinations for planning variants as well as the applicable take rates. The valid combinations are time dependent.

Example 1 (commercial): only right-hand drive vehicles can be sold in UK

Example 2 (commercial): a 360° camera cannot be ordered anymore as an individual option in the German market after calendar week 30 in 2025 (will become part of a package)

Example 3 (technical): a fixed bucket seat cannot be combined with back seats

After the organization has aligned on the Consensus Demand, the supply capabilities to fulfill this unconstrained demand plan need to be evaluated. Due to the common data model and the seamless integration in SAP IBP, the Consensus Demand in form of sales volumes for planning variants can be made instantly available for supply planning.

After the organization has aligned on the Consensus Demand, the supply capabilities to fulfill this unconstrained demand plan need to be evaluated. Due to the common data model and the seamless integration in SAP IBP, the Consensus Demand in form of sales volumes for planning variants can be made instantly available for supply planning.

After the organization has aligned on the Consensus Demand, the supply capabilities to fulfill this unconstrained demand plan need to be evaluated. Due to the common data model and the seamless integration in SAP IBP, the Consensus Demand in form of sales volumes for planning variants can be made instantly available for supply planning.

After the organization has aligned on the Consensus Demand, the supply capabilities to fulfill this unconstrained demand plan need to be evaluated. Due to the common data model and the seamless integration in SAP IBP, the Consensus Demand in form of sales volumes for planning variants can be made instantly available for supply planning.

After the organization has aligned on the Consensus Demand, the supply capabilities to fulfill this unconstrained demand plan need to be evaluated. Due to the common data model and the seamless integration in SAP IBP, the Consensus Demand in form of sales volumes for planning variants can be made instantly available for supply planning.

After the organization has aligned on the Consensus Demand, the supply capabilities to fulfill this unconstrained demand plan need to be evaluated. Due to the common data model and the seamless integration in SAP IBP, the Consensus Demand in form of sales volumes for planning variants can be made instantly available for supply planning.

After the organization has aligned on the Consensus Demand, the supply capabilities to fulfill this unconstrained demand plan need to be evaluated. Due to the common data model and the seamless integration in SAP IBP, the Consensus Demand in form of sales volumes for planning variants can be made instantly available for supply planning.

In S/4HANA, the material requirements are determined for the S&OP horizon using the Rapid Planning Matrix and MRP runs (see chapter on Rapid Planning Matrix). Subsequently, the material requirements towards the suppliers are usually distributed using EDI interfaces and/or the SAP Business Network. The planning and sequencing of the fully configured orders happens within the Model-Mix-Planning & Sequencing module (see chapter Rapid Planning Matrix).

SAP IBP offers planning algorithms to propagate the demand of the planning variants from the customer/market locations to the vehicle production plants (see also Learning Journey Discovering Response & Supply for SAP IBP - time-series planning). During the propagation, factors like transportation lead times and inventory (in market location or logistical hubs) are considered. In the first place, it's recommended to execute an infinite planning run to determine the unconstrained net demands at the factory locations.

Key Facts

• Generate assembly requirements that correspond to Product demand
• Generate assembly requirements according to Option %
• Consider ECN Changes
• What-if scenarios

High-level constraints can be checked after the planning run in either SAP IBP or a connected BTP application. In the following example, several alerts pop up in weeks where the unconstrained production demand for heavy cars exceeds the maximum number of heavy cars which can be produced on that line in a week.

High-level constraints can be checked after the planning run in either SAP IBP or a connected BTP application. In the example below, several alerts pop up in weeks where the unconstrained production demand for heavy cars exceeds the maximum number of heavy cars which can be produced on that line in a week.

Key facts:

• Evaluate assembly restrictions based on characteristics
• Evaluate capacities to fulfill product volume and configuration pattern
• Integrate with IBP Supply Planning

After having checked the assembly as well as other high-level constraints, the constraints on the lower levels related to assemblies and components need to be examined as well. One common approach to determine material requirements is to start off from a program of fully configured vehicles. To derive such a program, the planning variant volumes and associated take rates are used. This translation is facilitated by BTP extensions of SAP IBP which hold the required VC data. The basis for the prediction of the fully configured vehicles are usually historical sales orders or artificial seed orders. During the generation of the program, existing sales orders in the planning horizon need to be considered (potential forecast consumption). When the program of fully configured vehicles (a mix of sales and forecast orders) has been generated, the material requirements can be determined via a bill-of-material explosion.

For configurable materials (like vehicles) the material requirements depend on the configuration. The rules and conditions for the selection of materials are part of the VC data integrated to the BTP application extending SAP IBP. Within the BTP application, the assembly and component requirements are determined. This allows to check on internal manufacturing constraints, for example, for assemblies like power-trains which are produced in-house. Furthermore, constraints for externally procured components must be evaluated. In the short-term, the procurement plan from S/4HANA can be used as a basis for the comparison of material requirements and supply. In the mid- to long-term, the weekly capacities can be obtained from suppliers via SAP Collaborative Demand & Capacity Management (leveraging the Catena-X network) or the SAP Business Network.

• Evaluate manufacturing capacities for in-house produced components
• Multi-level BOM explosion
• Integrate with current production plan
• Evaluate capacities

• Generate external requirements for components
• Provide an ability to integrate with SAP Business Networks for supplier collaboration
• Ability to receive supplier commitments
• What-if analysis
• Integrate with S4 procurement status

The plan in SAP IBP which consist of weekly volumes per planning variant and the corresponding option take rates has been translated into a plan of fully configured vehicles. In order to plan critical components in SAP IBP as well, the bill of material coefficients for those components are determined and integrated back to SAP IBP. This enables the consideration of lower level constraints on the component/assembly level while performing a finite planning in IBP. To create a feasible and balanced plan, SAP IBP offers powerful planning algorithms like the Finite Heuristic and the Supply Optimizer. These algorithms strive to maximize the fulfillment of the (prioritized) customer demand while respecting the modeled constraints (assembly, manufacturing, procurement) and considering various planning parameters (lead times, lot sizes, and so on). When SAP IBP has come up with a feasible, constrained plan, it is integrated back to the BTP application and again translated into a plan of fully configured vehicles for the entire S&OP horizon (usually up to 24 months).

Key facts:

• Generate external requirements for components
• Provide an ability to integrate with SAP Business Networks for supplier collaboration
• Ability to receive supplier commitments
• What if analysis
• Integrate with S4 procurement status

As a last step, the S&OP plan is sent to the S/4HANA ERP system. There are several alternatives to integrate the plan to transactional system. The suitable approach depends on the scenario and the time horizon. One option is to send a plan of fully configured vehicles for the entire S&OP horizons. The sales orders are already present in the S/4HANA system, but they need to be complemented with the forecast orders to obtain a complete plan in the ERP system. Forecast orders can be represented by Customer Independent Requirements (CIR) in S/4HANA. CIRs are placeholders for future sales orders and do have a full configuration of the configurable material associated with them. The BTP application extending SAP IBP is sending the forecast orders to the ERP system at the end of a planning cycle.

Alternatively, the BTP application sends only the assembly requirements (usually non-configurable materials) which have been determined during the explosion of the fully configured vehicles during planning. This approach is more performance efficient and suitable in the mid- and long-term horizon where the focus is on material requirements planning only. For short-term processes like order sequencing, the fully configured orders are required. A mix of both approaches depending on the time horizon and the purpose is recommended.

Government regulations require that service parts for vehicles are provided for many years after End of Production (EOP) of the vehicle (a.k.a. right of repair). In many cases Original Equipment Manufacturers (OEMs) are even offering an extended availability (for example, 12+ years after EOP) as additional customer service.

Most manufacturers and suppliers however cannot cover the entire timespan with regular supplies due to various reasons (economics, space constraints, and so on). Instead suppliers often offer a so-called All-time or Last-Time Buy at a regular part price to the OEM prior to changing their production line / scrapping their tooling used to produce the part. OEMs must then forecast the expected required quantity for the remaining years of guaranteed service which is often >10 years.

Forecasting for such an extended period of time is very challenging because the individual part may be at very different stage of its lifecycle. Traditional forecasting approaches yield the risk of significant over- or under-forecasting.

In S/4HANA, the material requirements are determined for the S&OP horizon using the Rapid Planning Matrix and MRP runs (see chapter on Rapid Planning Matrix). Subsequently, the material requirements towards the suppliers are usually distributed using EDI interfaces and/or the SAP Business Network. The planning and sequencing of the fully configured orders happens within the Model-Mix-Planning & Sequencing module (see chapter Rapid Planning Matrix).

Over-Forecast:

• Leads to more stock than required
• Higher storage and handling costs
• Significant scrapping costs

Under-Forecast:

• Leads to out-of-stock situations
• High re-procurement costs (for example, tools)
• Part stock-outs, customer dissatisfaction

Over-forecasting may result in much higher inventory levels and parts that are never sold, causing eventually high scrapping costs and blocking warehouse space.

Under-forecasting may result in loss in customer satisfaction and loyalty because service parts are not available when required and significant procurement costs as potentially a new supplier must be identified, qualified and contracted to get the missing service part supplied, which is often bound to a high minimum order quantity and / or significant higher part price and in case of a high minimum order quantity may result eventually in high scrapping costs again.

Recognizing this challenge, SAP developed a special new AI-powered algorithm (curve-based forecasting) which considers the specialties of such forecasting. In a large validation program with an industry representative company, this new way of forecasting was evaluated against the legacy solution of an Automotive OEM and showed a double-digit improvement.