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Vital ideas for improving forecast accuracy

Cash flow forecasting is one of the most difficult tasks in corporate treasury, the web contains many reports and ideas. We review Logiity’s latest ideas in their report “Eight Methods to Improve Forecast Accuracy in 2019”.

Eight Methods to Improve Forecast Accuracy in 2019

Logility’s detailed report is focused on Forecasting best practices for varying supply chain scenarios. It examines three forecasting models and “the eight methods that have produced superior results for Logility’s many clients in a variety of industries and market conditions around the world.” It also discusses “how Multi-Variate Demand Signal Management can help you incorporate internal and external demand data to improve forecast quality and uncover insights to make better and faster decisions.”

Three Categories of Forecasting Models

Forecasting models classically fall into three categories: 

  • Qualitative models which are are experience-driven, relying on subjective inputs from knowledgeable personnel, such as salespeople, account managers
  • Quantitative models which are statistically driven, drawing heavily on historical performance data as the basic data input
  • Hybrid typically draw on historical demand information as a starting point, then use empirical data to further refine the forecast.

The primary differences between them include the type of input data and the mathematical and statistical methods employed to generate forecasts.

Eight Forecasting methods that improve supply chain performance

Logility has found that, “For many supply chain scenarios, it’s typically best to employ a variety of methods to obtain optimal forecasts. Ideally, managers should take advantage of several different methods and build them into the foundation of the forecast. The best practice is to use automated method switching to accommodate selection and deployment of the most appropriate forecast method for optimal results.” 

Working with over 1,200 organisations worldwide Logility has found that eight forecasting methods stand out ranging from:

  • 1. Modified Holt is a best-fit statistical technique used when demand is trended but does not vary by the time of the year. A Holt-Winters variant is often used when demand is seasonal
    • ......
  • 8. Demand Sensing techniques provide real-time visibility and insights into short- term demand, and are enabled through down-stream data sources such as POS and syndicated scanner data and by advanced technologies such as machine learning driven pattern recognition and natural language processing algorithms, simulation, and optimization.

Bringing it together

Logility’s paper shows how to integrate Best-fit Statistical Modelling, with Derived Modeling, Modeling for Intermitent Demand, Attribute-based Modeling and Demand Sensing with Multi-Variate Demand Signal Management as the figure below shows:

Source & Copyright©2019 - Logility

Conclusion

The report finishes with this comment, “Logility’s software solutions help planners leverage the best methods, spot trends and forecast demand signal changes more quickly, and sense and respond to market changes and inventory investments and deployments.” Which really reflects the difficultiess and demands of cash flow forecasting in a global MNC.


CTMfile take: This is a serious and in-depth report on the dynamics of forecasting cash flows in multiple supply chains. It is the integration of different techniques that impresses. Definitely worth a study.

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