The moving-average method of forecasting
Answer(s): D
The simple moving-average method is a smoothing technique that uses the experience of the past N periods through time period t) to forecast a value for the next period. Thus, the average includes each new observation and discards the oldest observation. The forecast formula for the next period for time period t+1) is the sum of the last N observations divided by N.
As part of a risk analysis, an auditor wishes to forecast the percentage growth in next month's sales for a particular plant using the past 30 months' sales results. Significant changes in the organization affecting sales volumes were made within the last 9 months. The most effective analysis technique to use would be
Answer(s): B
Under exponential smoothing, each forecast equals the sum of the last observation times the smoothing constant, plus the last forecast times one minus the constant. Thus, exponential means that greater weight is placed on the most recent data, with the weights of all data falling off exponentially as the data age. This feature is important because of the organizational changes that affected sales volume.
The average labor cost per unit for the first batch produced by a new process is US $120. The cumulative average labor cost after the second batch is US $72 per product. Using a batch size of 100 and assuming the learning curve continues, the total labor cost of four batches will be
The learning curve reflects the increased rate at which people perform tasks as they gain experience. The time required to perform a given task becomes progressively shorter. Ordinarily, the curve is expressed in a percentage of reduced time to complete a task for each doubling of cumulative production. One common assumption in a learning curve model is that the cumulative average time and labor cost) per unit is reduced by a certain percentage each time production doubles. Given a US $120 cost per unit for the first 100 units and a US $72 cost per unit when cumulative production doubled to 200 units, the learning curve percentage must be 60% US $72 - $120). If production is again doubled to 400 units four batches), the average unit labor cost should be US $43.20 $72 x 60%). Hence, total labor cost for 400 units is estimated to be US $17, 280 400 $43.20).
A plumbing company, a wholesale distributor, supplies plumbing contractors and retailers throughout the Northeast on a next-day delivery. basis. The company has a centrally located warehouse to accept receipts of plumbing supplies. The warehouse has a single dock to accept and unload railroad freight cars during the night. It takes 5 hours to unload each freight car. The company's prior records indicate that the number of freight cars that arrive in the course of a night range from zero to five or mare, with no indicated pattern of arrivals. If more than two freight cars arrive on the same night, some freight must be held until the next dad{ for unloading. The company wants to estimate the wait time when more than two freight cars arrive in the same night. The appropriate technique to analyze the arrival of freight cars is
Answer(s): C
The Monte Carlo simulation method is often used to generate the individual values for a random variable. The performance of a quantitative model under uncertainty may be investigated by randomly selecting values for each variable in the model based on the probability distribution of each variable) and then calculating the value of the solution. If this process is performed many times, the distribution of results from the model will be obtained.
Through the use of decision models, managers thoroughly analyze many alternatives and decide on the best alternative for the company. Often the actual results achieved from a particular decision are not what was expected when the decision was made. In addition, an alternative that was not selected would have actually been the best decision for the company. The appropriate technique to analyze the alternatives by using expected inputs and altering them before a decision is made is
Sensitivity modeling can be used to determine the outcome of a variety of decisions. A trial- and-error method may be adopted, usually in a computer model, to calculate the sensitivity of the solution variability of outcomes) to changes in a variable.
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