Final answer:
New item forecasting or forecasting for intermittent demand is used for items lacking historical demand data, utilizing methods such as using similar items' demand patterns, judgmental forecasting, and analytical approaches like bootstrap methods or Bayesian techniques, often with the aid of specialized forecasting software.
Step-by-step explanation:
The process used to compute levels for items that have no demand history or extremely low historical demands is known as new item forecasting or sometimes referred to as forecasting for intermittent demand. This involves a variety of strategies, as traditional demand forecasting methods are not applicable when historical data is lacking or unreliable. One common approach for new item forecasting, particularly in inventory management, is to use similar items' demand patterns as a proxy. This method acknowledges that while the new item does not have its own demand history, comparable products can offer insights into what demand might look like.
Another technique is to employ judgmental forecasting, which relies on expert opinions or market research to estimate future demand. Experts can use their knowledge of the market, comparable items, and any pre-launch data to estimate how well a new product might perform. For instance, specialized focus groups, surveys, and pre-market testing can inform these estimates. Finally, analytical methods like bootstrap methods or Bayesian approaches may also be used. These methods create statistical models that incorporate prior belief or information, which is particularly useful when dealing with limited data.
Certain software tools can also assist in this process, often incorporating a range of the above methods to offer a holistic approach to forecasting for new or low-demand items. These tools can apply algorithms that are specifically designed to deal with sparse data, helping to make the best possible prediction by recognizing patterns and correlations in related datasets.