In a previous post, I discussed one of the thornier problems demand planners sometimes face: working with product demand data characterized by what statisticians call skewness—a situation that can necessitate costly inventory investments. This sort of problematic data is found in several different scenarios. In at least one, the combination of intermittent demand and very effective sales promotions, the problem lends itself to an effective solution. Continue reading
Demand planners have to cope with multiple problems to get their job done. One is the Irritation of Intermittency. The “now you see it, now you don’t” character of intermittent demand, with its heavy mix of zero values, forces the use of advanced statistical methods, such as Smart Software’s patented Markov Bootstrap algorithm. But even within the dark realm of intermittent demand, there are degrees of difficulty: planners must further cope with the potentially costly Scourge of Skewness.
Smart Software President Nelson Hartunian, PhD
Tremendous cost-saving efficiencies can result from optimizing inventory stocking levels using the best predictions of future demand. Familiarity with forecasting basics is an important part of being effective with the software tools designed to exploit this efficiency. This concise introduction (the first in a short series of blog posts) offers the busy professional a primer in the basic ideas you need to bring to bear on forecasting. How do you evaluate your forecasting efforts, and how reliable are the results?
In order to reap the efficiency benefits of forecasting, you need the most accurate forecasts—forecasts built on the most appropriate historical data. Most discussions of this issue tend to focus on the merits of using demand vs. shipment history—and I’ll comment on this later. But first, let’s talk about the use of net vs. gross data.
Net vs. Gross History
Many planners are inclined to use net sales data to create their forecasts. Systems that track sales capture transactions as they occur and aggregate results into weekly or monthly periodic totals. In some cases, sales records account for returned purchases as negative sales and compute a net total. These net figures, which often mask real sales patterns, are fed into the forecasting system. The historical data used actually presents a false sense of what the customer wanted, and when they wanted it. This will carry forward into the forecast, with less than optimal results.
Posted in Excellence in Forecasting
Tagged accuracy, demand, demand data, efficiency, forecasting, gross history, net history, returns, sales pattern, shipment data, stock-out