You are implementing SAP IBP for sales and operations and are researching forecast model algorithms.

SAP IBP for Sales and Operations Planning (S&OP) includes demand planning with various statistical forecast algorithms to predict demand in time-series planning.

Option A: Data-cleansing algorithmsThis is incorrect. Data cleansing (e.g., outlier correction) is a preprocessing step, not a forecast model algorithm in SAP IBP’s demand planning engine.

Option B: Trend models algorithmsThis is correct. Trend models (e.g., linear regression, Holt’s method) are supported in SAP IBP to forecast demand with consistent growth or decline patterns, per official demand planning documentation.

Option C: Sporadic demand models algorithmsThis is incorrect. While sporadic demand (intermittent) is handled (e.g., via Croston’s method), it’s not a distinct category in SAP IBP’s standard algorithm list; it falls under broader models.

Option D: Naive models algorithmsThis is correct. Naive models (e.g., simple moving average, last period’s demand) are basic forecast algorithms in SAP IBP, used for stable demand patterns, per SAP’s forecast model library.

Thus, B and D are valid forecast algorithms in SAP IBP for S&OP, per official documentation.