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Effective demand planning is crucial for staying competitive in a dynamic market environment and for meeting customer expectations. In that context, Microsoft Dynamics 365 Supply Chain Management and its new Demand Planning capabilities are designed to streamline demand modeling and planning processes while maximizing efficiency, accuracy and collaboration.
Microsoft Dynamics 365 Supply Chain Management’s Demand Planning can be integrated via connectors with one or many data sources all on one platform, including Dynamics 365 FSCM. This allows for seamless integration with existing infrastructure, empowering businesses to leverage their current systems while unlocking the full potential of advanced demand planning capabilities.
Via data transformation, calculation and forecast profiles, a user can process repeatably complex data conversion, forecast orchestration and calculation logic with ease. It streamlines workflows, reduces manual effort, and enhances overall efficiency in demand planning operations.
Worksheets allow users to review different forecast results and collaborate on forecasted data. With intuitive what-if scenario modeling, commenting across users and comparisons across time series and forecast data, users can analyze, optimize, and compare different planning scenarios in minutes. This capability enables businesses to make informed decisions based on a comprehensive understanding of potential outcomes, mitigating risks and maximizing opportunities.
Microsoft announced Copilot functionality in Dynamics 365 Supply Chain Management’s Demand Planning to be generally available starting in April. First demonstrations shown at NRF Conference in January already gave a glimpse of what impact this feature can have. Users can leverage natural language to gain insights into your forecast data. It detects and highlights patterns or outliers and can give you internal and external context to why such data outlier may be the case. (E.g. internal context: A promotion that you ran last year but do not run this year. E.g. external context: An external event that had impact on demand f certain products such as the women’s world cup on soccer balls) Additionally, users can even interact via copilot capabilities with forecasted data to e.g. uplift certain forecast time series by x %. This intuitive way of interacting with demand planning simplifies complex tasks, allowing users to extract valuable insights and drive actionable outcomes without the need for data scientists.
Unlike traditional demand planning solutions that require specialized expertise, Dynamics 365 Supply Chain Management’s Demand Planning requires no data scientists and adopts a low code no code approach, making it accessible to over 85 percent of demand planners who aren't data scientists. This democratization of demand planning empowers users at all levels to contribute to the planning process effectively.
If you are currently running legacy Dynamics 365 Supply Chain Management Demand Planning via the Master planning module, rest assured, the new demand planning feature is backwards compatible with such, ensuring a seamless transition for businesses currently using Master planning module and Azure ML services.
With robust version control capabilities, seamless collaboration features, and intuitive data visualization tools, the new Demand Planning feature facilitates effective communication and decision-making throughout the planning cycle. Users can track changes, evaluate forecast accuracy, and collaborate in real-time, ensuring agility and responsiveness in demand planning operations.
In conclusion, the Demand Planning app represents a paradigm shift in Microsoft Demand Planning, offering a comprehensive suite of capabilities designed to drive efficiency, accuracy, and agility in today's dynamic business environment. By leveraging advanced technologies such as AI, natural language interaction, and seamless integration with ERP systems, businesses can unlock new levels of insight and optimize their demand planning processes for sustainable growth and success.