Pass Using C_TS422_2023 Exam Dumps

Simple discontinuation in SAP S/4HANA (material master, MRP 4 view, fields: Discontinuation Indicator, Effective-Out Date, Follow-Up Material) replaces a discontinued component with a follow-up material when stock is depleted. If the available stock is zero and the discontinuation date is in the past:

Material requirements planning(C): MRP (MD01/MD02) is affected because it checks the discontinuation settings. When stock is zero and the effective-out date has passed, MRP stops generating requirements for the discontinued material and creates them for the follow-up material instead (visible in MD04). This ensures planning continuity with the replacement.

Backflushing(A) (e.g., during CO11N) uses existing stock or reservations, not directly impacted by discontinuation unless stock is unavailable—but it’s a downstream process.Kanban(B) relies on replenishment triggers, not discontinuation logic, though stock-outs could disrupt it indirectly.Production order release(D) (CO02) checks material availability, but discontinuation is handled in MRP, not at release. This is per SAP’s discontinuation process.

In SAP S/4HANA Cloud Private Edition, alternative items in a BOM (transaction CS01/CS02) allow substitution of components based on predefined strategies. These are maintained in the BOM item data (Alternative Item Group and Strategy fields). The available strategies are:

Manual maintenance(C): This strategy (Strategy field: "1" - Manual Maintenance) allows planners to manually select which alternative item to use during production order processing (e.g., in CO02). The system does not automate selection, relying on user intervention based on availability or preference.

100% check(D): This strategy (Strategy field: "2" - 100% Check) ensures that only one alternative item is used per order, with the system checking availability (via ATP) for 100% of the required quantity. If the first item isn’t fully available, it moves to the next in the priority sequence (Priority field).

First in first out (FIFO)(A) is a stock management concept (e.g., in WM or batch determination),not a BOM alternative item strategy.Simultaneous(B) is not a standard strategy—alternative items are sequential or selective, not used simultaneously unless custom-developed. This aligns with SAP’s BOM functionality documentation.

In SAP S/4HANA PP/DS, achieving a feasible production plan under capacity constraints involves:

Determine a time period with available capacity on the planning board(C): Using the DS Planning Board (/SAPAPO/CDPS0), planners can visually identify periods with free capacity on resources and manually or automatically schedule orders into those slots, ensuring feasibility.

Increase the capacity supply in a time-phased interval(D): In the work center (CR02, Capacity tab) or PP/DS resource (/SAPAPO/RES01), you can define additional capacity (e.g., shifts, overtime) for specific time intervals via capacity variants, allowing more orders to fit within constraints.

Form optimum sequences to reduce setup times(E): Using heuristics (e.g., SAP_PP_020) or the PP/DS Optimizer, you can sequence operations on resources to minimize setup times (defined in routing, CA02), maximizing throughput and resolving capacity bottlenecks.

Execute an infinite planning run(A) ignores capacity constraints, creating an unfeasible plan requiring later adjustment—not a solution.Reduce the planning time interval(B) (e.g., horizon in /SAPAPO/CDPSC11) limits scope but doesn’t address capacity directly. This is per SAP’s PP/DS capacity planning strategies.

In an SAP S/4HANA Cloud Greenfield project,clean corefocuses on a standardized, extensible system with high-quality data. Key aspects for data quality include:

Efficiency(A): Clean core principles prioritize efficient processes and data structures, minimizing customizations that could degrade performance or complicate upgrades. Efficient data quality ensures streamlined operations (e.g., fast MRP runs) and leverages standard SAP functionalities effectively.

Stability(D): Maintaining a stable core with consistent, reliable data (e.g., master data integrity in MM03) prevents disruptions during updates or extensions. Stability ensures the system remains robust and predictable, a cornerstone of clean core in a Greenfield implementation.

Accuracy(B) is critical for data quality in general (e.g., correct BOM quantities), but clean core emphasizes systemic qualities over content accuracy, which is assumed.Timeliness(C) matters for operational data (e.g., stock updates), but it’s less tied to clean core’s structural focus. This is per SAP’s clean core guidelines.