Question

Suppose that a company uses an application of time series analysis that is known as “demand forecasting”. Meaning that they have a need to forecast the periodic demand for their products over time. In their use of demand forecasting, they use smoothing to remove random variation(noise) from their historical demand. This allows them to better identify demand patterns (primarily trend and seasonality) and demand levels that can be used to estimate future demand. The following represents their Excel spreadsheet with 11 weeks of historical demand for their product and an exponentially smoothed forecast calculated from that demand. The smoothing factor is 25%. The following represents a time series chart of the Excel demand data for the past 12 weeks: The above chart displays their historical demand data and the comparative exponentially smoothed forecast The value of applying exponential smoothing to their demand data, as displayed by the above chart, is best represented by which of the following statements: a. The forecast can be a poor and misleading representation of the actual demand data. b. Although “noise” and random demand variation is removed thru smoothing, the periodic highs and lows are also removed and can detract from the effectiveness of the smoothing application. c. Application of the smoothing technique tends to lower the demand line, which is a side effect of smoothing known as the “trend lag”. This causes the forecast to “lag” behind the trend and could require further adjustments in relation to employing the exponential smoothing technique. d. All of the above.

Answer 1

In exponential smoothing (as opposed to in moving averages smoothing) older data is given progressively-less relative weight (importance) whereas newer data is given progressively-greater weight. Also called averaging, it is employed in making short-term forecasts.The lag is a side effect of the smoothing process. There’s a reason this method has “smoothing” in its name because it neglects the ups and downs associated with random variation. As such, seeing on a graph shows you a smoother line or curve. But ignoring the random variation also allows you to see the underlying phenomenon, which helps when presenting data and making a forecast of future values.so we have correct optio C.