Ch 4 DSM - Forecasting

A seasonal index for a monthly series is about to be calculated on the basis of three years' accumulation of data. The three previous July values were 110, 150, and 130. The average over all months is 190. The approximate seasonal index for July is:

.684 Step 1: find average historical demand (110+150+130)/3 = 130 Step 2: find seasonal index = 130/190

Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naïve forecast?

1.0

Given an actual demand of 103, a previous forecast value of 99, and an alpha of .4, the exponential smoothing forecast for the next period would be:

100.6 = 99+.4(103-99)

Demand for a certain product is forecast to be 800 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25. What is the seasonally-adjusted sales forecast for January?

1000 product forecast*monthly index 800*1.25

If demand is 106 during January, 120 in February, 134 in March, and 142 in April, what is the 3-month simple moving average for May?

132 = (120+134+142)/3

The last four months of sales were 8, 10, 15, and 9 units. The last four forecasts were 5, 6, 11, and 12 units. The Mean Absolute Deviation (MAD) is:

3.5 8-5=3; 10-6=4, 15-11=4, 9-12=-3 (3+4+4+3)/4 months

Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation?

4 (1+4+8+3)/4

A time-series trend equation is 25.3 + 2.1X. What is your forecast for period 7?

40 = 25.3+2.1(7)

Given the following data about monthly demand, what is the approximate forecast for May using a four month moving average? November = 39 December = 36 January = 40 February = 42 March = 48 April = 46

44 = (40+42+48+46)/4

Given an actual demand of 61, a previous forecast value of 58, and an alpha of .3, the exponential smoothing forecast for the next period would be:

58.9 formula on #10 = 58+.3(61-58)

Given last periods forecast of 65, and last periods demand of 62, what is the simple exponential smoothing forecast with an alpha of .4 for the next period?

63.8 Last periods forecast + α(Last periods demand - last periods forecast last periods forecast = 65 a = .4 last periods demand = 62 = 65+.4(62-65)

Time-series patterns that repeat themselves after a period of days or weeks are called __________

seasonality

The last four weekly values of sales were 80, 100, 105, and 90 units. The last four forecasts were 60, 80, 95, and 75 units. These forecasts illustrate:

Bias (form of measurement error that occurs when forecasts are consistently greater or less than actual values)

A forecast that projects a company's sales is a(n):

Demand forecast

Quantitative methods of forecasting include

Exponential smoothing

Which of the following statements about time-series forecasting is true? A. It is based on the assumption that the analysis of past demand helps predict future demand. B. It is based on the assumption that future demand will be the same as past demand. C. Because it accounts for trends, cycles, and seasonal patterns, it is always more powerful than associative forecasting.

It is based on the assumption that the analysis of past demand helps predict future demand.

The primary purpose of the mean absolute deviation (MAD) in forecasting is to:

Measure forecast accuracy

Which time-series model assumes that demand in the next period will be equal to the most recent period's demand? A. Naïve approach B. Moving average approach C. Exponential smoothing approach

Naïve approach

Forecasts are usually classified into three categories including:

Short-range, medium-range, and long-range

Which of the following uses three types of participants: decision makers, staff personnel, and respondents? A. The Delphi method B. Executive opinions C. Sales force composites

The Delphi method

A regression model is used to forecast sales based on advertising dollars spent. The regression line is y=500+35x and the coefficient of determination is .90. Which is the best statement about this forecasting model?

The correlation between sales and advertising is positive.

The degree or strength of a relationship between two variables is shown by the__________

correlation coefficient

For a given product demand, the time-series trend equation is 53 - 4X. The negative sign on the slope of the equation:

is an indication that product demand is declining

Forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize a__________

long-range time horizon

The tracking signal is the__________

ratio of cumulative error/MAD