Question 1

A 7-day moving average will be more responsive than a 3-day moving average.

Accepted Answer

The 3-day moving average will be more responsive than the 7-day moving average because it takes a shorter timeframe into account. The 7-day moving average will be smoother and more stable, but it may not pick up on short-term fluctuations as quickly as the 3-day moving average.

Question 2

The benefit of a weighted moving average is that it allows a greater emphasis on the past demand than most recent periods.

Accepted Answer

A weighted moving average allows for greater emphasis on more recent periods by assigning higher weights to them, rather than emphasizing past demand.

Question 3

A naïve forecast considers several periods of past data in creating a forecast.

Accepted Answer

A naïve forecast uses only the most recent period's data as the forecast for the next period, without considering multiple past periods.

Question 4

In exponential smoothing,a small value of alpha will produce a more responsive forecast than a large alpha value.

Accepted Answer

A small value of alpha in exponential smoothing makes the forecast less responsive to changes by giving more weight to older observations, whereas a large alpha value makes the forecast more responsive to recent changes.

Question 5

Using many periods of data in a moving average will produce a more stable forecast than using three or four periods.

Accepted Answer

Using more periods in a moving average will smooth out the fluctuations in the data and provide a more stable forecast. Conversely, using only a few periods in a moving average will result in a forecast that is more sensitive to short-term fluctuations in the data.

Question 6

Cyclical factors are similar to seasonal factors,but have a much longer time period than seasonal factors.

Accepted Answer

Cyclical factors refer to long-term fluctuations in economic activity that can last for several years and are often related to changes in business cycles. Seasonal factors, on the other hand, are shorter-term fluctuations that occur within a year and are typically related to changes in weather or holidays.

Question 7

A moving average filters out the effects of random variation.

Accepted Answer

A moving average smooths out a time series by taking the average of a set number of data points over a given time period. This can help to eliminate the effects of short-term fluctuations or random variation in the data, making it easier to identify underlying trends or patterns.

Question 8

Seasonal demand is measured in time periods of less than a year.

Accepted Answer

Seasonal demand refers to fluctuations in demand that occur during specific periods of the year, such as holidays, festival seasons, or weather-related changes. These periods usually last for a few weeks or months, and hence, it is measured in time periods of less than a year.

Question 9

Random variation is caused by chance events after all other components of demand have been accounted for in the forecast.

Accepted Answer

Random variation is unpredictable and caused by chance events that are not related to any known factors or patterns. It is often referred to as "noise" or "residuals" in statistical models, and cannot be explained or accounted for by the forecasted variables. This means that it is independent of any other factors or trends in the data, and is likely to occur randomly over time. Therefore, it is true that random variation is caused by chance events after all other components of demand have been accounted for in the forecast.

Question 10

Seasonal patterns can be exhibited over a quarter,a week,or in hours.

Accepted Answer

Seasonal patterns can be observed over different time frames including a quarter (three months), a week, or even in hours. For example, sales of beach gear may increase during the summer months, but may also fluctuate on weekends versus weekdays or during different hours of the day.

Question 11

Preparing an accurate forecast for an aggregated group of similar products is often easier than forecasting for an individual product separately.

Accepted Answer

Aggregated forecasting takes into account the general trends and patterns of a group of similar products, making it easier to predict their overall demand. Forecasting for individual products requires more specific data and analysis, which can be more challenging to obtain and use accurately.

Question 12

Weighted moving average and exponential smoothing both account for trends in the data being forecasted.

Accepted Answer

Weighted moving average does not inherently account for trends in the data; it simply assigns different weights to past observations. Exponential smoothing can be adapted to account for trends by using methods like Holt's linear trend method.

Question 13

Fashion and economic conditions produce cycles.

Accepted Answer

This is true. Fashion and economic conditions are closely intertwined and can influence each other, resulting in cyclical trends in the fashion industry. Economic recession can lead to more conservative fashion choices, while a booming economy can lead to more extravagant and experimental fashion trends. Similarly, changes in fashion tastes and preferences can have an impact on consumer spending habits and thus, the economy.

Question 14

Forecasting becomes more difficult the farther a forecast looks into the future.

Accepted Answer

This is true because as time progresses, more variables and uncertainties come into play, making it harder to accurately predict future outcomes. Additionally, unforeseen events and disruptions can also occur, further complicating the forecasting process.

Question 15

A time-series analysis considers cycles,trends,and seasonal variation to construct forecasts.

Accepted Answer

A time-series analysis takes into account patterns such as cycles, trends and seasonal variation to make predictions or forecasts about future values of the data.

Question 16

Sophisticated forecasting models using massive computing power allow companies to produce exact forecasts.

Accepted Answer

While sophisticated forecasting models and massive computing power can significantly improve the accuracy of forecasts, they cannot produce exact forecasts due to the inherent unpredictability of future events and external factors.

Question 17

Companies must have perfect forecasts for planning purposes.

Accepted Answer

While forecasts are important for planning, companies cannot always have perfect forecasts due to factors such as market changes, unexpected events, and human error. Thus, companies must be prepared to adjust their plans and strategies as needed based on actual outcomes.

Question 18

Forecasting methods should be used for products with dependent demand.

Accepted Answer

Forecasting methods are typically used for products with independent demand, as dependent demand is directly calculated based on the demand of the final product it contributes to, rather than forecasted.

Question 19

Quantitative techniques for forecasting are based on subjective factors,estimates,and opinions.

Accepted Answer

Quantitative techniques for forecasting involve the use of statistical and mathematical models to analyze historical data and project future trends. These methods do not rely on subjective factors such as opinions and estimates. Qualitative techniques, on the other hand, rely heavily on subjective factors, such as expert opinions and market surveys.

Question 20

The naïve forecast for the next period is the actual value observed in the current period.

Accepted Answer

This is the definition of a naïve forecast.

Quiz 5: Forecasting