Mandatory Prerequisites:
The learner must have attended the following Instructor-Led Workshops:
- o9 IBP Functional Workshop
- o9 Platform Technical - Basics Workshop

The learner ideally should have completed the following o9 Academy courses and their certification:
• Functional Insights into Demand Planning Core Solution Building Blocks
• What's New in Demand Planning Solution Building Blocks R2024.06?

Target Audience:
Demand Planners, S&OP and IBP Coordinators, Analysts, Solution Architects, Super Users, CoE Teams

About the Course:
This course introduces the latest features and enhancements in R2024.09 Demand Planning, emphasizing how these updates improve forecast accuracy and operational efficiency.

One of the key highlights is the Forecast Ensemble, which allows demand planners to combine multiple statistical and machine learning forecasts into a single, more reliable model. This approach improves forecast accuracy and adaptability by leveraging the strengths of each model. By reducing the weaknesses of individual forecasting methods, businesses can better manage volatile datasets and respond effectively to market uncertainties.

Another key highlight is the Demand Sensing feature, which breaks down longer-term forecasts into more granular daily predictions. This feature provides a real-time view of demand fluctuations using internal factors like shipments and promotions and external events like holidays. Demand Sensing helps planners adjust operational forecasts, improving short-term decisions related to resource deployment and ensuring businesses meet customer demand more efficiently.

This course also covers several feature enhancements, including improvements to the Cycle over Cycle Change (CoCC) KPI, which allows for better forecast stability tracking.

The Analysis Cockpit feature has also been enhanced, allowing users to evaluate forecast violations based on volume percentage rather than the number of intersections impacted.

For deal planning, the course introduces a summarized view of Cycle over Cycle (CoC) opportunity changes, allowing planners to track opportunity shifts more quickly.

The New Product Introduction (NPI) workflow has also been streamlined, simplifying the process of generating forecasts for new products. Planners can now navigate more efficiently and complete tasks faster, improving efficiency. Additionally, planners can manually enter NPI forecasts at both detailed and aggregate levels, giving them greater control over managing new product launches.

The course also explores enhancements in Multi-level and Multi-Horizon forecasting, which enable businesses to create forecasts tailored to different decision-making timeframes. This reduces processing times, optimizes storage, and ensures forecasts align with varying planning needs.

The enhancements in Separate Train and Predict in CML enable the decoupling of training and prediction processes, allowing for more efficient batch processing and real-time predictions without needing to retrain models each time. This improves forecast stability while reducing runtimes, making the process faster and more efficient. In CML History Decomposition, a new history decomposition feature enables users to break down historical data into its underlying components, such as promotions and holidays, providing more precise insights into what drove past sales and improving future forecasts.

Lastly, the course highlights improvements in Collaborative Demand Planning by integrating business segmentation into workflows. This helps planners prioritize critical areas of their portfolios, improving decision-making. The Demand Assumption workflow has also been simplified, making it easier for users to input and track assumptions within the forecasting model.

Overall, this course provides a detailed overview of features and enhancements in R2024.09 that empower demand planners to refine their forecasting processes, react quickly to changes, and make data-driven decisions aligned with business needs. These updates ensure improved accuracy, flexibility, and usability in Demand Planning, allowing businesses to operate more effectively in a dynamic market.