Statistical approaches to forecasting can provide a framework for creating rolling budgets to which analytical skills and judgment can be applied in supporting a sound budgeting process.
Hans Levenbach
Hans Levenbach, PhD is Executive Director, CPDF Training and Certification Professional Development Programs. He conducts hands-on Workshops on Demand Forecasting for multi-national supply chain companies worldwide. He is group manager of the LinkedIn groups:
- Demand Forecaster Training and Certification, Blended Learning, Predictive Visualization,
- New Product Forecasting and Innovation Planning, Cognitive Modeling, Predictive Visualization.
He invites you to join if you have interest in sharing conversations on these topics.
Author's Articles
For organizations with annual expense budgets, it is important to have procedures for monitoring expenditures and budget items throughout the year. This article visually describes how to use statistical forecasting models, uncertainty ranges and space forecasting models for this purpose.
As most forecasting methods require data, a forecaster analyzes the availability of data from both external and internal sources. The availability of external data is improving rapidly. With the explosion of Internet websites, potential sources of valuable data are becoming limitless. With unstructured data, the need for data mining tools has become a necessity for exploring potential sources of data for consumer analyses and predictive modelling purposes.
Planners and managers in supply chain organizations are accustomed to using the Mean Absolute Percentage Error (MAPE) as their best (and sometimes only) answer to measuring forecast accuracy. It is so ubiquitous that it is hardly questioned.