Statistical Forecasting in SAP S/4 HANA

Statistical forecasting in SAP S/4 HANA refers to the use of statistical methods and algorithms to predict future demand for products and services. 

This can help organisations optimise their inventory levels, production planning, and supply chain operations.

Some of the key features of Statistical Forecasting in SAP S/4HANA

• Time series forecasting: Using historical demand data to forecast future demand based on statistical models such as exponential smoothing and ARIMA (Autoregressive Integrated Moving Average).
  
  
• Collaboration and feedback: Incorporating feedback from sales and marketing teams, as well as external data such as weather forecasts, to refine and improve forecasts.
  
  
• Scenario planning: Simulating different scenarios to assess the impact of changing market conditions or other factors on demand and supply.
  
  
• Automated forecasting: Automating the forecasting process, enabling organisations to quickly generate forecasts for large volumes of products and services.
  
  
• Integration with other modules: SAP S/4 HANA can integrate with other modules such as inventory management, production planning, and sales and distribution, to ensure that forecasts are used to optimise operations across the organisation.

What benefits does Statistical Forecasting in SAP S/4HANA provide to your organisation?

• Improved accuracy: Statistical forecasting methods use historical data to generate forecasts, which can help to improve the accuracy of predictions. By analysing patterns and trends in the data, statistical forecasting can provide more accurate forecasts than other methods.
  
  
• Faster decision-making: By providing accurate and timely forecasts, statistical forecasting can help businesses make faster and more informed decisions. This can be particularly important in industries where demand can be volatile or unpredictable.
  
  
• Reduced costs: By improving the accuracy of forecasts, statistical forecasting can help businesses reduce costs by optimising inventory levels, improving production planning, and minimising waste.
  
  
• Better planning: Statistical forecasting can help businesses to plan for future demand, which can be particularly important for long-term planning and budgeting.
  
  
• Improved supply chain management: By providing accurate forecasts, statistical forecasting can help businesses better manage their supply chains. This can include optimising inventory levels, reducing lead times, and improving supplier relationships.

With the help of FusionGraph it's easy to discover and understand new functionality like this and add it to your business case for S/4HANA migration. 

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