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OMGT 3223 Final Exam Questions with Latest Update
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OMGT 3223 Final Exam Questions with Latest Update
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OMGT 3223 Final Exam Questions withLatest Update
The cumulative forecast error is important for determining the - Answer-Bias in forecast
error
Excel and the Analytic (or Risk) Solver are most appropriate for physical simulation
models - Answer-False
If the correlation between two variables X and Y is -0.50, then... - Answer-There is a
negative relationship between the variables
The objective function of an optimization problem is - Answer-the output that is to be
maximized or minimized
Time Series - Answer-a set of numerical data obtained by observing a response
variable at specific TIME periods
refer to a set of techniques that use historical demand data to predict the future
Based on a single variable - TIME
Underlying assumption: Factors influencing the past and the present will continue to
influence the future
Delphi method - Answer-Procedure for acquiring informed judgments and opinions from
knowledgable individuals using a series of questionnaires in order to develop a
consensus forecast about what will occur in the future
Short-to mid-range forecasts - Answer-Usually encompass the immediate future
(Planning horizon: Daily up to a year)
Quantitative
Long-range forecasts - Answer-Usually encompass a period of time longer than a year
Trends - Answer-Gradual long-term up or down movements of demand
Seasonal patterns - Answer-Up-and-down repetitive movements in demand occurring
periodically (Typically occur within 1 year)
Cycles - Answer-Repeating up and down movements (Typically 2-10 years duration)
, Simple Moving Average Example - Answer-The manager of a food supply company
wants to be certain that enough drivers and vehicles are available to
deliver orders promptly and that they have adequate inventory in stock. For that
purpose, the manager would like
to be able to forecast the number of orders that will occur during the next month. From
records of delivery orders,
management has accumulated the following data for the past 10 months.
Weighted Moving Average Example - Answer-Refer to our previous example. Compute
a three-month weighted moving average for the month of November using a weight of
50% for the most recent data point, a weight of 33% for the second most recent data
point, and a weight of 17% for the third most recent data point.
Month Orders/Month
Feb 90
Mar 100
Apr 75
May 110
June 50
July 75
Aug 130
Sept 110
Oct 90
Nov ?
.5 x 90 + .33 x 110 + .17 x 130 = 103.4 orders
Exponential Smoothing - Answer-Forecasting technique based on the idea that a new
forecast can be computed from previous forecasts and the most recent observed
demand value.
This technique:
requires minimal data
is easy to understand
has a good track record of success
A large positive cumulative error value - Answer-indicates forecasts are consistently
smaller than actual demand
A large negative cumulative error value - Answer-implies forecasts are consistently
larger than actual demand
Average Error (Bias) - Answer-Referred to as the bias of the forecast. Ideally, we would
like the bias of a forecast to be ZERO.
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