University of Tokyo uses @RISK to
Help Improve Mad Cow Disease Detection
Professor Katsuaki Sugiura at the Laboratory of Global Animal Resource Science at the Graduate School of Agriculture and Life Sciences, the University of Tokyo, has used Palisade’s @RISK software since 1995 in his research activities. His current research is on bovine spongiform encephalopathy (BSE) – a progressive and fatal nervous disease found mainly in adult dairy cattle. The cause of BSE is oral exposure to what’s known as an abnormal prion protein.
In Japan, all cattle butchered for human consumption are tested for BSE, as well as all cattle that died over 24 months old. However, due to the variability of the age of slaughter and death, and duration of incubation period and the limited detection capability of the diagnostic test, Professor Sugiura uses Monte Carlo simulation in @RISK to improve the surveillance program. He builds stochastic models that predict how changing the testing age of the cattle will impact the number of cattle tested and BSE infected cattle detected.
The thinking behind BSE testing model was as follows:
- Calculate age-at-clinical-onset as (infection age) + (incubation period)
- Consider cattle are slaughtered for human consumption or not dying from BSE when cattle are slaughtered or die before the age-at-clinical onset.
- Consider BSE death when the age-at-clinical-onset occurs before cattle slaughtered.
- If the cow is slaughtered: (slaughter age) > (testing age) -> undergo testing of the carcass. If the difference between the (age-at-clinical-onset) and the (slaughter age) is less than six months, then the detection rate is positive.
The simulation results demonstrated that increasing the minimum age of testing from 0 to 21 months for both dairy cattle and Wagyu beef cattle had very little impact on the probability that a BSE-infected animal slaughtered for human consumption would be detected. The insights provided by @RISK in Professor Sugiura’s work enables researchers to eliminate testing age as an important factor so they can focus on other, more effective factors.
» Read the full case study
» More about @RISK
» More about Graduate School of Agriculture and Life Sciences
at the University of Tokyo
The key point is that without having
to learn any new programming language
we are able to construct models right
in Microsoft Excel and process them
Dr. Katsuaki Sugiura, Graduate School of
Agriculture and Life Sciences, University of Tokyo
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:
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There are three easy ways to get the DecisionTools Suite for your students:
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network lab and laptop installation. Palisade offers a complimentary Live Web Training session to all instructors who order a Course License.
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The DecisionTools Suite is a great way to present quantitative techniques in a straightforward, easy-to-understand way for students.
The Value of a Customer
Excerpted from Financial Models Using Simulation and
by Wayne Winston, published by Palisade Corp.
In making marketing decisions it is very important to take a long-term view.
Improvements in customer service, for example, may not pay off in the short-term. If we look at the long‐term value of a customer, however, a small increase in customer retention resulting from improved customer service may greatly increase a companyʹs profit.
The book The Loyalty Effect (Reichheld, 1996) contains a great discussion of how the value of a customer depends on the probability that a customer will leave your business. Here is an example on how the average profitability of a credit card customer depends on how many years they have had a credit card.
By using @RISK and the loyalty.xls example file we are able to demonstrate that going to a 95% retention from a 90% retention is much more beneficial than going from an 85% retention to 90%. This example explains the large increase in profitability associated with a seemingly small increase in the retention rate.
Download example files and view step-by-step instructions
Correlating Input Variables
Two input distributions are correlated when their samples should be “related” — that is, the value sampled for one distribution should affect the value sampled for the other. This correlation is necessary when, in reality, two input variables move to some degree in tandem. For example, imagine a model with two input distributions — Interest Rate and Housing Starts. These two inputs are related, as the value sampled for Housing Starts depends on the value sampled for Interest Rate. A high Interest Rate would tend to cause a low value for Housing Starts, and conversely a low Interest Rate would tend to cause a high value for Housing Starts. If this correlation were not accounted for in sampling, some iterations of the simulation would reflect nonsensical conditions that could not occur in reality — such as a high Interest Rate and a high value for Housing Starts.
@RISK's Define Correlations command allows the samples of input probability distributions to be correlated. When the Define Correlations icon is clicked, a matrix is displayed which includes a row and column for each probability distribution in the currently selected cells in Excel. Correlation coefficients between the probability distributions can be entered using this matrix.
» Learn more in the @RISK manual
The DecisionTools Suite is part of complex analyses published in white papers.
@RISK for Developing Healthcare
Model That Cuts Costs
"Reduced use of erythropoiesis-stimulating agents and intravenous iron with ferric citrate: a managed care cost-offset model," by Rich Mutell, Jaime Rubin, T. Christopher Bond, and Tracy Mayne, International Journal of Nephrology and Renovascular Disease, April 2013
The researchers at the DaVita Clinical Research in Minneapolis, MN are using @RISK to develop a new healthcare model that introduces pharmacological innovation to a healthcare system desperate for cost reduction. @RISK’s Monte Carlo simulation allowed the researchers to model the cost savings generated when end-stage renal disease patients with electrolyte disturbances are given ferric citrate. Their research forms the basis of a proposed new healthcare model that could save money for hospitals, patients, and healthcare insurers.
Read the full paper here
The Academic Global Leader in
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.
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