The Predict Demand process within the Demand to Management OMBP in Oracle Fusion Cloud SCM

leverages advanced capabilities to enhance demand planning. Collaborative Forecasting Platform (A)

enables stakeholders—such as sales teams, suppliers, and distributors—to collaborate in real time,

inputting qualitative insights (e.g., market trends or promotions) that refine forecasts beyond pure

data analysis. For example, a retailer might adjust forecasts based on an upcoming sale confirmed via

the platform, improving accuracy. Machine Learning-based Forecasting (B) uses algorithms to analyze

historical data, detect patterns (e.g., seasonality or anomalies), and adapt predictions dynamically,

making it more precise than traditional methods. For instance, it might identify a spike in demand for

umbrellas during unexpected rainy seasons. Option C (Statistical Forecasting) is a traditional method

relying on statistical models but lacks the adaptive intelligence of machine learning, though it’s still

used as a foundation. Option D (Demand Sensing) focuses on short-term demand signals (e.g., point-

of-sale data) rather than long-term planning, making it complementary but not a core strength of

Predict Demand. Together, A and B empower businesses with both human collaboration and cutting-

edge AI, ensuring robust demand planning that balances quantitative and qualitative inputs.

Reference: Oracle Fusion Cloud SCM Documentation - "Demand Management and Predictive

Analytics"