Kansas State University Utilizes @RISK to Aid in Biosecurity for Beef Cattle
In the beef industry, biosecurity is too often neglected. Beef producers commonly import new animals into their herds, but don’t take measures to decrease risk of disease introduction. Reproductive disease is an important cause of lost production and economic return to beef cow-calf producers, causing estimated losses of $400 to $500 million dollars per year. Because of the complex nature of the production system, the biologically and economically optimal interventions to control disease risk are not always clear.
Dr. Michael Sanderson, a professor of Beef Production and Epidemiology at Kansas State University’s (KSU) College of Veterinary Medicine, addressed this issue by developing a risk management tool for veterinarians and beef cow-calf producers to assist in identifying biologically and economically valuable biosecurity practices, using @RISK.
The project modelled the risk of introduction of the infectious disease Bovine Viral Diarrhea (BVD) into the herd, the impact of disease on the herd (morbidity, mortality, abortion, culling, lost weight) and economic control costs.
As a result of the risk identification insight gained from the research, Dr. Sanderson and his team were able to improve disease management and controls—primarily by identifying management practices that affect the probability of disease introduction and production, as well as strategies that would lower biological and economic risk over a ten-year planning horizon.
» Read the full case study
» More about @RISK
» Learn more about Dr. Michael Sanderson
For this project, @RISK was essential to
model variability and uncertainty in risk for
disease introduction and impact following
introduction, as well as variability and
uncertainty in effectiveness of mitigation
Dr. Michael Sanderson, Beef Production and
Epidemiology, KSU College of Veterinary Medicine
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The DecisionTools Suite is a great way to present quantitative techniques in a straightforward, easy-to-understand way for students.
Using RISKOptimizer to
Optimize Audit Sampling
Excerpted from Decision Making Under Uncertainty,
by Wayne Winston, published by Palisade Corp.
Auditors often try to estimate the mean or variance of a population in a cost effective manner. The only way, of course, to surely know the mean of a population is to completely sample the entire population. Download the example file below to walk through two examples of using RISKOptimizer to optimize audit sampling. The first example estimates the average invoice value of a
population of 5000 invoices. The second example shows how RISKOptimizer can be used to determine optimal allocations in stratified sampling, while trying to determine the total inventory value of 1600 items.
» Download example file and view step-by-step instructions
Specifying a Descriptive Name
an Input Distribution
Every @RISK input distribution has a name for use in graphs and reports as well as the @RISK Model Window. When you first define a distribution, either in the Define Distribution window or by directly entering a formula in the worksheet, @RISK assigns it a default name. This default name comes from text that @RISK finds in your worksheet and interprets as row and column headings. If the name is acceptable, you don't need to do anything. If it's not acceptable, or if it's blank because @RISK couldn't find any suitable text, you can easily change it, using any of these methods:
- In the first box in the Define Distribution window, enter the desired name. (You can do this when first creating the distribution. For an existing distribution, click on the cell and then click Define Distribution to reopen the window.)
- Enter or change the name in the Model Window. Click anywhere in the row for the desired input, right-click, and select Function Properties. The Name box is first in the Properties dialog.
- Edit the formula directly, in Excel, to insert a RiskName property function.
=RiskPoisson(.234, RiskName("Number of claims"))
or, with the name in a separate cell,
Save your workbook after entering or editing any names. The new names will be used in subsequent graphs and reports. (Graphs and reports of simulation results will use the new names when you run a new simulation.)
Learn more in the @RISK manual
The DecisionTools Suite is part of complex analyses published in white papers.
Linking Mineral Systems Models to
Quantitative Risk Analysis and
Decision-Making in Exploration
by Dr. Oliver Kreuzer et al., X-plore Geoconsulting
The authors' approach integrates critical mineralization processes and conditions with concepts of probability theory, decision analysis and financial modeling. A case study, based on an actual porphyry copper project, illustrates how the probabilistic mineral systems model can generate a measure of the probability of ore occurrence as an input for exploration decision trees and simulations to calculate the expected value (EV) of an exploration project and the probability distribution of all possible surrounding NPV values within a minimum and maximum range. Formulation of the probabilistic model closely follows and combines principles of the well-established petroleum and mineral systems approaches and makes use of Excel™-based model templates and PrecisionTree.
Read the full paper here
Over 60,000 students use the DecisionTools Suite annually in their MBA and graduate level programs. In 2012, over 800 universities worldwide adopted the DecisionTools Suite. Professors in business, engineering, OR, and other fields keep their students on the cutting edge by teaching the latest analytical techniques from Palisade. Some major schools with programs and courses that standardized on the DecisionTools Suite last month include:
US and Canada:
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Dakota State University
Harvard School of Public Health
Illinois Institute of Technology
University of Arizona
University of Colorado Denver
University of Maryland
University of Texas Austin
University of Texas El Paso
University of Virginia
University of Washington
University of Waterloo
US Army Logistics Management College
Washington University in Saint Louis
City University London - United Kingdom |
Georg August Universitaet Goettingen - Germany
Hochschule Zittau/Goerlitz - Germany
Pontificia University - Colombia
Ruhr-Universitaet Bochum - Germany
Sabanci Universitesi - Turkey
Universidad de San Buenaventura Cali - Colombia
Universidad Industrial de Santander - Colombia
Universidad Politecnica de Valencia - Spain
Universidad Pontificia Bolivar - Colombia
Universite de Bretagne Occidentale - EMISAB - France
University College London - United Kingdom
University of Split - Croatia
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Risk and Decision Analysis
Palisade is the world’s leading provider of risk and decision analysis software and solutions. Founded in 1984, its products @RISK and the DecisionTools Suite are used by over 95% of the Fortune 100, in nearly every industry around the world. Palisade is headquartered in New York State, and has offices in London, Sydney, Rio de Janeiro, and Tokyo.
What is The DecisionTools Suite?
The DecisionTools Suite is the world’s only integrated set of risk and decision analysis programs. The Suite includes @RISK for Monte Carlo simulation, PrecisionTree for decision trees, and TopRank for “what if” sensitivity analysis. In addition, DecisionTools Suite comes with StatTools for statistical analysis and forecasting, NeuralTools for predictive neural networks, and Evolver and RISKOptimizer for optimization. All programs work together seamlessly, and all integrate completely with Microsoft Excel for ease of use and maximum flexibility.
The Suite is offered in three affordable and flexible licensing options for those in academia: Student Versions, Course Licenses and Full Academic Versions.
» Schools using Palisade software
» Textbooks using Palisade software
» Kansas State U Utilizes
@RISK for Biosecurity
» Licensing Options
» Teaching Tips &
Using RISKOptimizer to
Optimize Audit Sampling
» Tech Tip
Specifying a Descriptive
» Featured White Paper
Linking Mineral Systems
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