If you think demand forecasting can only help you if your forecasts are close to accurate – then you might be surprised. According to a recent
study by industrial engineers at Lehigh University, “a decrease in prediction intervals via methods that reduce forecast variance translates into a direct decrease in expected operational costs.” Thus, simply decreasing the variations between your demand forecasts will result in lessening your costs, regardless of whether your resulting forecasts are close to the actual future demand.
To decrease the variation in your forecasts, it's best to utilize one of the four main approaches toward demand forecasting. One of these approaches is called the
Relational/Causal Approach. Bluntly, this approach bases its forecasts on the reasons behind a consumer's desire to purchase your product. This approach heavily relies on assuming your product is in demand for specific reasons, and that all forecasts made should incorporate these reasons.
For obvious products like calculators, figuring out the reasons behind its popularity is easy. However, for most products, the motives being their purchase aren't so easily uncovered. When this is the case, you must look to historical data and “find relationships” between your data such that will allow you to form possible reasons for why your consumers have purchased your product. You can use consumers' income, ethnicity, and other demographic factors and draw conclusions from trends that you see.
Anseris, Inc. uses advanced methodologies such as econometric models, input-output models, life cycle models, and simulation models when forecasting via the Relational Approach. Our seasoned team of forecast experts will use our fine-tuned methods which incorporate all four approaches to give you very accurate demand forecasts for your products. We have over two decades of experience and research in
Inventory Forecasting – you can surely trust us for your demand forecasting needs. Contact us today!