Businesses make and sell a wide range of products to a diversity of customers globally. Each product, customer, and geographic region have unique demand patterns. Generating an accurate forecast for every combination of product, market, and region is the responsibility of the demand planner (of the organization). The demand planner cannot use a one size fits all forecasting process for all combinations. Neither can they determine the best forecasting approach for each combination manually.
What is Statistical Forecasting?
Statistical forecasting uses different statistical methods to help predict the future by determining trends using past and present data. Statistical forecasts are a fundamental aspect of creating a better demand planning and forecasting process. Statistical forecasts often serve as the baseline forecast for starting a collaborative forecasting process.
Find the Best-Fit Demand Planning and Forecasting Technique
Arkieva’s statistical forecasting software employs a proprietary best in class “Best Fit” algorithm to identify the best statistical forecast method for each combination. The best method is selected from hundreds of methods in the Arkieva Statistical Forecasting Engine include moving average based, trend based, cycle based (for example seasonal or Fourier) and more special methods such as ARIMA (Box-Jenkins), causal and Croston’s method.
The Arkieva statistical forecasting engine is a robust demand planning and forecasting tool that integrates with segmentation and life cycle management modules. As a result, the output of those modules can be used to drive the forecasting engine differently for different segments of the data.
Built on Arkieva’s attribute based planning data model, the statistical forecast software can be run using multiple attributes:
- Product or product related attributes including hierarchies or families
- Market or market-related attributes such as ship to, sold to, market channel
- Location or location related attributes – region or country
All attributes can be reconciled via dynamic top-down and bottom-up reallocation.
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