Q&A Hero

Q: (p. 76) Which of the following forecasting methods is very dependent on selection of the right individuals who will judgmentally be used to actually generate the forecast? A. Time series analysis B. Simple moving average C. Weighted moving average D. Delphi method E. Panel consensus

Q: Which of the following is not an example of General Purpose Simulation Software? A. SIMAN B. GPSS/PC C. RESQ D. GAPE/H E. SLAMII

Q: (p. 75) Which of the following forecasting methods uses executive judgment as its primary component for forecasting? A. Historical analogy B. Time series analysis C. Panel consensus D. Market research E. Linear regression

Q: Which of the following is an example of General Purpose Simulation Software? A. PC-MODEL B. MAP/1 C. SIMFACTORY D. Canella IV E. OPUS/2.3

Q: (p. 50) Which of the following forecasting methodologies is considered a causal forecasting technique? A. Exponential smoothing B. Weighted moving average C. Linear regression D. Historical analogy E. Market research

Q: Which of the following is an example of General Purpose Simulation Software? A. Link Trainer Programs B. SIMSCRIPT II.5 C. SIM-Central D. Fortran E. Visual Basic

Q: (p. 52) Which of the following forecasting methodologies is considered a time series forecasting technique? A. Delphi method B. Exponential averaging C. Simple movement smoothing D. Weighted moving average E. Simulation

Q: On a good day a distributor will have $5,000 of inventory sales; on a medium day sales of $3,000; on a bad day only $1,000. Suppose you have data on this distributor's sales for the past 100 days and that she had 25 good days, 50 medium days and 25 bad days. If you draw a random number to represent her sales for the first simulated day and that number were 89, which of the following were her simulated sales? (Note: arrange the random number interval probability distribution so it starts with a good day at 00 followed by a medium day, etc.) A. $5,000 B. $3,000 C. $2,500 D. $1,000 E. Can not be determined

Q: (p. 52) Which of the following forecasting methodologies is considered a time series forecasting technique? A. Simple moving average B. Market research C. Leading indicators D. Historical analogy E. Simulation

Q: On a good day, Joe will sell an average of $50,000 of product. On a medium day he will sell $25,000 and on a bad day he will sell only $10,000. Suppose you observe Joe's business for 100 days and, during that time, he had 15 good days, 40 medium days and 45 bad days. If you draw a random number to represent his sales for the first simulated day and that number were 47, what kind of simulated day did Joe's business have? (Note: arrange the random number interval probability distribution so it starts with a good day at 00 followed by a medium day, etc.) A. A good day B. A medium day C. A bad day D. Can not be determined E. Between medium and bad

Q: A small company has either a good day of sales with an average of $50,000 or a bad day with an average of only $10,000 for the day. To simulate these outcomes, random numbers from 00 to 99 should be assigned with the intervals determined from the frequency distribution. If, during the last 50 days, that the vendor had 15 good days and 35 bad days, which of the following is a correct random number interval for a bad day? A. 00 to 49 B. 15 to 35 C. 30 to 99 D. 15 to 99 E. 00 to 60

Q: (p. 75) Which of the following forecasting methodologies is considered a qualitative forecasting technique? A. Simple moving average B. Market research C. Linear regression D. Exponential smoothing E. Multiple regression

Q: A small vendor has either a good day of sales with an average of $1,000 or a bad day with an average of only $500 for the day. To simulate these outcomes, random numbers from 00 to 99 should be assigned with the intervals determined from the frequency distribution. If, during the last 100 days, the vendor had 27 good days and 73 bad days, which of the following is a correct random number interval for a bad day? A. 28 to 99 B. 27 to 73 C. 27 to 100 D. 27 to 99 E. 00 to 26

Q: (p. 50) In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand? A. Forecast error B. Autocorrelation C. Previous demand D. Consistent demand E. Repeat demand

Q: In modeling a simulation random numbers from 00 to 99 are assigned with the intervals determined from frequency distributions for each behavior occurrence. If there are two behaviors, X and Y, out of 50 tallies, on behavior X you record 20 tallies and on behavior Y you record 30 tallies. Which of the following is a correct random number interval for behavior X? A. 00 to 19 B. 01 to 20 C. 00 to 39 D. 41 to 61 E. 45 to 100

Q: (p. 50) In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand? A. Cyclical elements B. Future demand C. Past demand D. Inconsistent demand E. Level demand

Q: (p. 50) In most cases, demand for products or services can be broken down into several components. Which of the following is not considered a component of demand? A. Average demand for a period B. A trend C. Seasonal elements D. Past data E. Autocorrelation

Q: In modeling a simulation, random numbers from 00 to 99 are assigned with the intervals determined from frequency distributions for each behavior occurrence. If there are two behaviors, A and B, out of 100 tallies, on behavior A you record 45 tallies and on behavior B you record 55 tallies. Which of the following is a correct random number interval for behavior A? A. 00 to 45 B. 00 to 44 C. 56 to 99 D. 55 to 100 E. 45 to 100

Q: Which of the following is a way of dealing with a validation problem when the computer simulation yields results that you suspect are incorrect? A. Run the computer program again with more or fewer iterations B. Simulate present conditions and compare results with the existing system C. Approach the problem with a mathematical model instead D. Use a different simulation model to try to verify the mistakes E. Adjust the parameters of the simulation until you get the results you expected

Q: (p. 50) Which of the following is not one of the basic types of forecasting? A. Qualitative B. Time series analysis C. Causal relationships D. Simulation E. Force field analysis

Q: (p. 70) In time series data depicting demand which of the following is not considered a component of demand variation? A. Trend B. Seasonal C. Cyclical D. Variance E. Autocorrelation

Q: In the context of a simulation study, which of the following refers to testing the computer simulation program to ensure that the simulation is correct? A. Validation B. Confirmation C. Substantiation D. Calibration E. Checking

Q: Determining starting conditions is a major tactical decision in simulation analysis. Which of the following is a way to cope with this problem? A. Discard data generated during the early parts of a run B. Select starting conditions that you know are realistic C. Select starting conditions randomly D. Measure the actual values of the system you are trying to simulate E. Use stochastic heuristics as starting conditions

Q: (p. 64) We usually associate the word "seasonal" with recurrent periods of repetitive activity that happen on other than an annual cycle.

Q: In a simulation study, after defining the problem and constructing the simulation model, the immediate next major phase of the study is which of the following? A. Run the simulation B. Specify values of variables and parameters C. Evaluate results D. Validation E. Propose a new experiment

Q: (p. 63) In decomposition of time series data it is relatively easy identify cycles and autocorrelation components.

Q: A key factor in the "construct simulation model" phase of a simulation study is which of the following? A. Determine starting (initial) conditions and values B. Defining simulation objectives C. Specification of probability distributions D. Specification of values for variables and parameters E. Specification of desired outcomes by management

Q: (p. 63) It is difficult to identify the trend in time series data.

Q: The technique of simulation has made significant progress since 2003 when a set of simulation standards was adopted by the International Society for Simulation (ISS) an industry organization set up in Japan linking manufacturing firms with organizations providing products for computer games.

Q: (p. 63) A time series is defined in the text as chronologically ordered data that may contain one or more components of demand variation: trend, seasonal, cyclical, autocorrelation, and random.

Q: One of the features of simulation is that it allows judgment to enter into the design and analysis of the problem.

Q: (p. 63) Decomposition of a time series means identifying and separating the time series data into its components.

Q: The leading general and special purpose simulation models follow a well-documented and standardized approach.

Q: The more time, money and effort you spend developing a simulation model the more likely you are to achieve useful results.

Q: (p. 75) Market research is a quantitative method of forecasting.

Q: (p. 75) Qualitative forecasting techniques generally take advantage of the knowledge of experts and therefore do not require much judgment.

Q: Being user-friendly and easy to understand typically means that the simulation produces less accurate results.

Q: Being user-friendly and easy to understand is considered a desirable feature of simulation software systems.

Q: (p. 73) In causal relationship forecasting leading indicators are used to forecast occurrences.

Q: (p. 50) A good forecaster is one who develops special skills and experience at one forecasting technique and is capable of applying it to widely diverse situations.

Q: Being able to permit interactive debugging of a model so the user can trace flows through the model and more easily find errors is considered a desirable feature of simulation software systems.

Q: Having animation capabilities to graphically display product flows through a production system is not considered a desirable feature of simulation software systems.

Q: (p. 50) For every forecasting problem there is one best forecasting technique.

Q: Special-Purpose Simulation Software is specially built to run specific applications that have provisions in manufacturing models to allow for specifying the number of work centers, their descriptions, arrival rates and even batch sizes.

Q: (p. 74) Multiple regression analysis uses several regression models to generate a forecast.

Q: General-Purpose Simulation Software (GPSS) is really a computer language that allows programmers to build their own simulation models.

Q: (p. 62) The standard error of the estimate of a linear regression is not useful for judging the fit between the data and the regression line when doing forecasts.

Q: (p. 59) Linear regression is not useful for aggregate planning.

Q: Simulation models are classified as discrete if they are based on mathematical equations.

Q: Operations and supply management simulations are most usually the continuous type.

Q: (p. 59) Regression is a functional relationship between two or more correlated variables, where one variable is used to predict another.

Q: Simulation models are classified as continuous if the simulation occurs at specific event points, like a piece of machinery in a factory breaking down.

Q: (p. 59) A restriction in using linear regression is that it assumes that past data and future projections fall on or near a straight line.

Q: (p. 71) A tracking signal (TS) can be calculated using the arithmetic sum of forecast deviations divided by the MAD.

Q: Spreadsheet simulations have become obsolete due in part to specialized computer simulation languages like SLAM or SIMAN.

Q: In order to develop the random number intervals for a simulation study we need to know the probability distributions in the situation to be simulated.

Q: (p. 71) In forecasting, RSFE stands for "running sum of forecast errors."

Q: (p. 71) RSFE in forecasting stands for "reliable safety function error."

Q: Regardless of the computer language chosen for a simulation model, too much data from a simulation can be dysfunctional to problem solving.

Q: (p. 70) MAD statistics can be used to generate tracking signals.

Q: When we have performed a simulation run of a model another simulation experiment might be called for that requires a change in parameters or variables.

Q: (p. 70) When forecast errors occur in a normally distributed pattern, the ratio of the mean absolute deviation to the standard deviation is 2 to 1, or 2 x MAD = 1 standard deviation.

Q: Run length and run time are the same thing.

Q: (p. 70) Random errors in forecasting occur when an undetected secular trend is not included in a forecasting model.

Q: A simulation model does not have to be validated to provide accurate information.

Q: (p. 48) There are no differences in strategic and tactical forecasting. A forecast is a mathematical projection and its ultimate purpose should make no difference to the analyst.

Q: The run length of a simulation depends on the purpose of the simulation.

Q: (p. 70) Random errors can be defined as those that cannot be explained by the forecast model being used.

Q: In a simulation model time cannot be advanced by variable time increments.

Q: (p. 69) Because the factors governing demand for products are very complex, all forecasts of demand contain error.

Q: In a simulation model time can be advanced by fixed or variable time increments.

Q: Time incrementing is a concept of a simulated clock.

Q: (p. 57) Exponential smoothing forecasts always lag behind the actual occurrence but can be corrected somewhat with a trend adjustment.

Q: An empirical probability distribution used in a simulation study is one derived from a standard mathematical distribution like the normal or Poisson probability distributions.

Q: (p. 57) Simple exponential smoothing lags changes in demand.

Q: As stated in the textbook, there are five different categories of probability distributions that can be used for a simulation study.

Q: (p. 59) The value of the smoothing constant alpha in an exponential smoothing model is between 0 and 1.

Q: Decision rules in a simulation are a set of conditions under which the behavior of the simulation model is observed. These rules are either directly or indirectly the focus of most simulation studies.

Q: (p. 56) In exponential smoothing, it is desirable to use a higher smoothing constant when forecasting demand for a product experiencing high growth.

Q: The first step in the major phase "constructing the simulation model" is to determine which properties of the system should be fixed (called parameters) and which should be allowed to assume different values (called variables) throughout the simulation run.