Please wait while the transcript is being prepared...
0:04
Examine the situation facing a manager who must periodically forecast the inventories for hundreds of products. Each day, week or month, updated forecasts for the many products are required within a short time. While it might be possible to develop sophisticated forecasting models for each of the items, in many cases, some very simple short-term forecasting tools are adequate for the job. A manager facing such a task is likely to use some form of time-series smoothing. All the time-series smoothing methods use a form of weighted average of past observations to smooth up-and-down movements, that is, some statistical method of suppressing short-term fluctuations. The assumption that the forecaster makes which underlies these methods is that the fluctuations in past values represent random departures from some smooth curve that, once identified, can plausibly be extrapolated into the future to produce a forecast or series of forecasts. Each of the time-series methods examines different combinations of the types of departures from the smooth curve that commonly occur. This particular example is the forecast of a standard main body kit. The data is real from a company that has over 160 plants located throughout the United States supplying the recreational vehicle industry. The blue line represents the sales of these kits over time.

Quiz available with full talk access. Request Free Trial or Login.

Hide

Time-series forecasting: a vehicle industry example

Embed in course/own notes