Question 1

A collaborative planning,forecasting and replenishment system eliminates the need for forecasting.

Accepted Answer

A collaborative planning, forecasting and replenishment system uses the input and insights of multiple stakeholders to create more accurate forecasts and align inventory levels with projected demand. While it can improve forecasting, it still relies on forecasting as a key component of the process.

Question 2

What distinguishes collaborative planning,forecasting,and replenishment (CPFR)systems from traditional planning approaches is the emphasis on forecasting.

Accepted Answer

CPFR distinguishes itself by emphasizing collaboration among partners in the supply chain, not just on forecasting, but on planning, forecasting, and replenishment activities together.

Question 3

A video game publishing company needs to predict the total sales in the European market for the next year.This is an example of a(n):
A)firm-level demand forecast.
B)overall market demand forecast.
C)supply forecast.
D)price forecast.

Accepted Answer

The question is asking about predicting the total sales in the European market for the next year, which is a market demand forecast. It is not a firm-level demand forecast because it is not focused solely on the sales of one specific company. It is also not a supply forecast or a price forecast, as it is not focused on predicting the supply or price of a specific product.

Question 4

The slope of the regression equation is positive if the r-squared value is greater than 0.0.

Accepted Answer

The r-squared value measures the proportion of the variance in the dependent variable that is predictable from the independent variable, and it does not indicate the direction of the relationship (positive or negative slope) between the variables.

Question 5

The Delphi method,panel consensus forecasting,and market surveys are all qualitative methods,but only market surveys do not use experts.

Accepted Answer

Market surveys gather data from a broad audience or consumers rather than relying on expert opinions, unlike the Delphi method and panel consensus forecasting which involve expert input.

Question 6

A model with a positive mean forecast error suggests that,on average,the model underforecasts.

Accepted Answer

A positive mean forecast error indicates that the actual outcomes were consistently higher than the model's predictions, implying underforecasting.

Question 7

Forecasts are almost always wrong.

Accepted Answer

Forecasts are based on assumptions and predictions about the future, which are often subject to numerous variables that are difficult to predict accurately. As a result, even the most well-informed forecasts are often wrong to some degree.

Question 8

A seasonal index less than 1.0 means that the model is overforecasted.

Accepted Answer

The answer of A seasonal index less than 1.0 means...

Question 9

Multiple regression is used when the forecaster believes that more than one independent variable should be used to predict the variable of interest.

Accepted Answer

Multiple regression involves using two or more independent variables to predict the dependent variable, making it a useful tool for forecasting when there are multiple factors that could impact the outcome.

Question 10

The independent variable is the quantity the forecaster is interested in estimating with a linear regression model.

Accepted Answer

The independent variable in a linear regression model is the variable that is used to explain or predict the dependent variable, not the quantity the forecaster is interested in estimating. The dependent variable is the quantity of interest that is being estimated or predicted.

Question 11

A seasonal pattern in time series data is evident when the level of the variable of interest moves erratically up or down from one period to the next.

Accepted Answer

A seasonal pattern in time series data is characterized by regular, predictable changes that recur every calendar year, not erratic movements from one period to the next.

Question 12

When there is a significant upward or downward trend in the data,the two best forecasting models are exponential smoothing and linear regression.

Accepted Answer

Exponential smoothing is better suited for data with no trend or seasonality, while linear regression can handle trends. For significant trends, models like ARIMA or Holt's linear trend method are more appropriate.

Question 13

The greater the randomness in the model,the greater the number of periods should be used in a moving average forecast.

Accepted Answer

A moving average forecast smooths out random fluctuations in the data, and the more random the data, the more periods are needed to properly capture the underlying trend.

Question 14

Qualitative forecasts are used when there is plenty of relevant data.

Accepted Answer

Qualitative forecasts are used when there is limited or no relevant data. Quantitative forecasts are used when there is plenty of relevant data.

Question 15

Fed up with her working conditions at the call center,Lisa decides to invest in a state-of-the-art sewing machine and produce limited quantities of her own clothing designs.After a few months of operation,she decides to apply some of the forecasting techniques she mastered in school.Which of these statements about her forecasts is correct?
A)Her forecasts will probably be 100% accurate.
B)Her demand forecasts for a year from now will probably be more accurate than her demand forecasts for three months from now.
C)Her demand forecasts for each style of skirt will be less accurate than her demand forecasts for all skirts.
D)The best way for her to determine the amount of fabric she needs is to forecast it based on her customer orders for each type of skirt.

Accepted Answer

The answer of Fed up with her working conditions at...

Question 16

Exponential smoothing with an alpha of one will yield identical results to a last period actual demand.

Accepted Answer

Exponential smoothing with an alpha of one means that the forecast is entirely based on the last period's actual demand, and therefore, will yield identical results to the last period actual demand.

Question 17

Over the long run,fluctuations in demand due to seasonality are greater than those due to randomness.

Accepted Answer

Seasonality refers to the predictable patterns of demand fluctuations that occur at regular intervals (e.g. holiday seasons, weather changes, etc.). These fluctuations tend to be greater in magnitude than random fluctuations because they are driven by external factors that affect consumer behavior in a predictable manner. Random fluctuations, on the other hand, are more unpredictable and tend to be smaller in magnitude since they are not influenced by external factors in a consistent way. Therefore, the statement is true.

Question 18

Demand was low two years ago but increased sharply last year thanks to an aggressive marketing campaign.A time series model that puts the greatest emphasis on the most recent period is probably the best choice to predict next year's demand.

Accepted Answer

The answer of Demand was low two years ago but...

Question 19

The forecast data matches the actual data perfectly if the mean absolute deviation is 0.0.

Accepted Answer

If the mean absolute deviation is 0.0, it means that there is no difference between the forecasted values and the actual values, indicating a perfect match.

Question 20

The tracking signal calculated for the first forecast is always either +1 or -1.

Accepted Answer

The tracking signal for the first forecast is not always +1 or -1. It depends on the actual demand and whether the forecast was over or underestimating the demand.

Quiz 10: Forecasting