Outlining Capabilities of Aftersales Service Parts Planning

Aftersales Service Parts Planning has some special and unique requirements that sets it apart from regular supply chain planning. Specialties include but are not limited to the following:

• NETWORK planning
• High volume of part-locations to be planned (for example, more than 300.000 parts in more than 50 locations)
• Advanced part replacement rules (a.k.a. supersessions or substitutions)
• Touchless planning with system-supported/automatic decision making
• Automatic re-planning of individual parts over night WHEN REQUIRED
• Extensive distribution planning across the globe

SAP extended Service Parts Planning is a solution specifically designed to cover the needs of the spare parts and service parts industry which has existed for the last 20 years as SAP Service Parts Planning but has been moved to SAP S/4HANA some years ago. Consequently, it is a market proven solution, well equipped to deal with high volumes of parts, being executed mainly using background planning runs, utilizing bills of distribution for planning central and local warehouses with extensive warehousing integration.

This solution has a proven track record with a broad range of customers who leverage the capabilities of this solution to service their global spare parts networks.

These major Service Parts Planning processes are differentiated in Tactical and Operational Planning. Each set of processes has special requirements and less or more tight integration in Service Parts Execution.

Effective service parts planning hinges besides relevant future-looking external predictors (key figures) on a robust demand history as the basis for forecasting and stocking decisions. Capturing demand history is the first crucial step in this process, and processing this data accurately is essential to meet the specific needs of service parts planning.

When uploading demand history data, several critical processing steps are performed to transform raw data into high quality and structured time series as the basis for successful planning.

• Demand Category Determination: Classifying the demand correctly (for example, in forecast-relevant and non-relevant demand) is essential for accurate forecasting.
• Future Dated Orders Identification: Identifying orders intended for delivery dates far out in the future ensures they are accurately accounted for without distorting current demand.
• Location Determination: Establishing the correct customer facing and stockholding locations helps to handle slow moving parts in a more efficient manner and acknowledges that not every part is stocked in every location.
• Period Aggregation: Consolidating data based on specific time periods allows for trend analysis and smoother forecasting.
• Demand Scaling: Normalizing the demand history allows to make demand history more comparable across time buckets.
• Aggregation along the BOD: Forms the basis for alternative forecasting on aggregated BOD level.

Adjustments to the demand history may be needed due to several reasons, including stocking decision changes, which involve realignments based on decisions that affect stock levels or stock locations. Supersession may also necessitate adjustments due to product replacements or the discontinuation of items. Additionally, there may be a need to isolate demand influenced by promotions to avoid skewing future forecasts. Furthermore, it is important to remove demand spikes caused by unique events, such as technical campaigns, as these do not represent typical demand patterns and should not influence future forecasting models.

Forecasting within SAP extended Service Parts Planning is designed to address the unique lifecycle dynamics of service parts, ranging from the introduction of new products to the retirement of obsolete ones. The system supports many forecasting models to account for demand variation, enabling it to adapt its approach based on the demand patterns observed. For instance, if a part has a stable demand, eSPP applies a straightforward forecasting model, but if demand is seasonal or sporadic, it leverages more complex algorithms that account for these irregularities.

The system begins forecasting by assessing historical sales data, which includes volume, seasonality, and trending patterns. The system automatically selects the most relevant forecasting models for each part based on the assessment results, calculates forecasts in the past to determine the model with the fewest forecast errors. This minimizes human intervention and ensures optimal forecasting accuracy. If monitoring parameters indicate that the forecast model's performance is worse than defined thresholds, and the stability rules outlined in the forecast profile permit a change in the forecasting model, then an automatic model selection is initiated, and the forecast parameters are adjusted accordingly.

In addition to traditional history-based forecasting, use eSPP to create, for example, a leading indicator forecast. This is a special feature that incorporates, for example, market data from installed products or equipment. These as well as external predictors in different special machine learning algorithms help project future requirements based on real-world usage and environmental data, enabling a more future oriented forecast.

eSPP employs an automated approval system for forecasts, where new calculated forecasts that align closely with the most recent forecast are automatically approved, while large deviations are flagged for manual review. This approval system not only increases the efficiency of forecast validation but also minimizes errors in cases of unusual forecast spikes or drops.

The Inventory Planning Service in SAP extended Service Parts Planning (eSPP) is a strategic module that balances stock levels across locations to meet customer service requirements while minimizing inventory.

The system’s planning capabilities extend across a Bill of Distribution (BOD) hierarchy, allowing for dynamic stock allocation based on business rules and, for example, actual demand at specific locations. eSPP’s inventory planning module identifies and recommends stock level adjustments, such as increasing stock in high-demand areas and reducing it in locations where demand has fallen.

Inventory planning in eSPP can adapt to different sales behaviors through statistical models. For fast-moving parts, it uses normal distribution models, while for items with sporadic demand, it applies Poisson distribution to better align with unpredictable usage patterns. This flexibility enables eSPP to maintain a high service level without overstocking, reducing both inventory holding and stockout costs.

The system takes a data-driven approach, using forecast, variability and cost data in, for example, multi-echelon optimization or a combined EOQ and safety stock calculation to determine optimum stock quantities for each location while considering target service levels.

Another outcome of the EOQ and safety stock planning is the determination of the deployment indicator, which decides whether the lead time for pull or push deployment is used in the planning process.

Distribution Requirements Planning (DRP) in SAP eSPP orchestrates the purchasing process for service parts across a predefined distribution hierarchy, known as the Bill of Distribution (BOD). DRP takes into account forecasted demand, current inventory, and transportation schedules to create a precise plan for restocking locations throughout the network. It automates the generation of purchase requisitions and scheduling agreement lines, which streamlines the complex logistics of service parts distribution.

The DRP process begins by calculating net demand at each location, rounding quantities to meet practical shipping and packaging constraints, and aggregating these demands along the BOD structure. DRP also considers product supersession and the management of supplier shutdowns, such as holiday closures, ensuring that replenishment plans are smoothened and not disrupted by such events.

Flexible stability rules are available to ensure that changes to purchase plans can be managed efficiently and with least human interactions. Fixed demands and fixed receipts can be added to influence the DRP result. Multiple planning methods such as period based planning or re-order point based planning are available.

To enhance accuracy, DRP includes an approval and review function, allowing planners to validate the DRP outputs before execution. This multi-level approval process provides an added layer of control, especially in complex situations. By automating and refining the distribution requirements planning process, SAP eSPP’s DRP module ensures that inventory is available at the right place and time, minimizing stockouts and maximizing operational efficiency.

The Deployment Service in SAP extended Service Parts Planning (eSPP) optimizes the allocation of incoming stock at the entry location (master warehouse) to the various locations in the network in a way that meets immediate demand while accounting for the relative priority of the demands . It uses a tiered priority system, sequence rules, and fair-share distribution, which allows it to allocate stock in a balanced manner across high-demand and low-demand areas. Deployment helps companies maximize service levels by ensuring that urgent needs are met promptly while avoiding over-allocation in less critical areas.

Service parts planning allows for two distinct deployment methods:

• Pull Deployment: This method is executed through regular planning runs and is initiated by demand at the receiving (child) location.
• Push Deployment: Unlike pull deployment, this method is triggered by inventory receipt at the supplying (parent) location. Instead of storing the goods, they are directly sent to the receiving locations. Push deployment is typically used for fast-moving parts and offers the benefit of expediting replenishment along the Bill of Distribution (BOD).

A key feature of Deployment is its ability to dynamically adjust product distributions based on actual demand data. This real-time responsiveness helps mitigate service disruptions and ensures that high-priority demands are consistently met.

By aligning stock distribution with current demand and priority levels, the Deployment Service enhances the agility of the supply chain. It complements the DRP and Inventory Balancing modules, working in tandem to prevent stock imbalances and avoid situations where excess stock is present in one area while shortages occur in another. Through these capabilities, SAP eSPP’s Deployment Service provides a robust framework for responsive and efficient inventory distribution.

Inventory Balancing within SAP extended Service Parts Planning (eSPP) is designed to optimize stock levels across multiple locations by redistributing excess stock from overstocked areas to locations experiencing demand. This function is helpful in managing the variability in demand that is common with service parts, ensuring that stock levels are balanced where economically reasonable to avoid unnecessary inventory buildup in the network and minimizing holding costs.

Inventory Balancing operates within flexible defined inventory balancing areas, thus allowing to move inventory between different locations (even across different regions), depending on demand requirements and a cost-benefit analysis.

The system includes advanced monitoring and approval tools like the Planner’s Worklist, Shortage Monitor and the Alert Monitor. These tools provide alerts for example, unscheduled or critical demand surges, enabling planners to respond proactively to potential stockouts or disruptions. As most planning decisions are automated in eSPP, some need human attention and decision making. These approvals can be executed in central planning UIs.

SAP eSPP seamlessly integrates with Procurement Execution and Stock Transfer Execution, ensuring that demand for service parts translates smoothly into procurement activities and inventory transfers. Through Procurement Execution, eSPP can automatically trigger purchase orders and release scheduling agreement lines to suppliers based on rounded net demands, streamlining the purchasing process and reducing administrative burden. Stock Transfer Execution is aligned with eSPP to manage the movement of stock between locations, utilizing stock transport orders to fulfill demand across the BOD hierarchy.

SAP eSPP operates within the SAP S/4HANA ecosystem, leveraging core SAP S/4HANA master data for products, locations, and distribution setups. By doing so, eSPP ensures seamless data consistency across planning and execution processes. eSPP also integrates with SAP Extended Warehouse Management (EWM), enhancing warehouse operations with potential cross-docking scenarios.

In cross-docking scenarios the tight integration allows incoming goods to be immediately routed to outgoing shipments, minimizing storage needs and expediting fulfillment.