Advertising Selection
A model for choosing numbers of ads of different types to maximize exposures
Resources
All example models may be downloaded, modified, and distributed free of charge. You can use these as inspiration for your own models.
Please note all examples require @RISK or the appropriate DecisionTools software product installed to see the full analysis.
Legal Notice: Lumivero assumes no liability for your use (or inability to use) any of these examples. All models are provided as-is without warranty of any kind, and by downloading them you assume all risk for the use of and/or results generated from these models.
A model for choosing numbers of ads of different types to maximize exposures
A model for purchasing different size products at minimal cost when there are quantity discounts
A simple model of alphabetizing names, used to illustrate Evolver's Order solution method
A model for assigning n workers to n tasks in the most efficient way
A typical product mix model, but with nonlinear constraints, for selecting bakery products to produce
A linear programming model of a credit-granting company trying to collect payments from its customers.
This model requires DTS Industrial Edition
A model for grouping software functions so that those that call each other frequently are in the same group
A model for choosing weekly production levels to meet forecasts and smooth production
A model for locating power stations to cover all towns and use minimal power
A model for locating radio towers to maximize the listening audience
A model for finding a sequence of optimal portfolio returns for different levels of risk
This model requires DTS Industrial Edition
A model for choosing a portfolio of assets to maximize the upside potential minus the downside risk
A fairly complex model for assigning job tasks to machines, where each machine can do only one task at a time
A linear programming model for withdrawing from taxable and non-taxable accounts to meet expenses
This model requires DTS Industrial Edition
A fairly large linear programming model for shipping goods from manufacturers to retailers
This model requires DTS Industrial Edition
A model of shipping goods from origins to destinations by truck, where the objective is to use the least number of trucks
This example looks at cash flow analysis of projects with multiple phases (5) such as those typically found in the pharmacological industry. Each phase has...
An example where copulas are fitted to temperature and precipitation data.
Three different versions of a model used for the U.S. Air Force to estimate the total cost of a potential (but fictitious) missile system. 1....
This model illustrates one possible simulation of hydroelectric power generation for a 120-month horizon. There are three sources of uncertainty: monthly desired power (as a...
This model uses historical mining costs for seven years to project costs for the coming year. The model forecasts line items for the coming year...
Here you will find three different versions of a @RISK model used to value a gold mine lease. 1. A basic @RISK model with uncertainty...
This model simulates the daily expenses of a business traveler who faces uncertainty each day on whether he makes a trip, and if so, the...
This simple model illustrates how the RiskSimtable function can be used for a quick sensitivity analysis on an input.
Three different versions of an example for modeling costs of risk events. 1. Illustrates one way to model cost from an event which might occur...
This simulation model follows a sample of 200 customers who each begin a year in a certain credit rating category and with a certain amount...
This model illustrates how uncertainties can be built into a financial statement (income statement, balance sheet, cash flows) to make future projections.
When a company develops a new product, the profitability of the product is highly uncertain. Simulation is an excellent tool to estimate the average profitability...
This simple model illustrates two ways variable interest rates on a loan might be simulated. In the first model, the yearly interest rates are generated...
This model illustrates variance at risk (VAR) in the context of a put on a stock. It follows two portfolios: one where the investor purchases...
Anybody who owns a portfolio of investments knows there is a great deal of uncertainty about the future worth of the portfolio. Recently the concept...
This model, really a template, is used to value European stock options: calls and/or puts. The model uses the well-known lognormal model of stock price...
A Markov chain is a process observed through time where the probability distribution of the next state of the process, given the current state, is...
The purpose of this model is to provide a comparison between building an onshore plant in the U.S. and building an offshore plant in China....
A new product model that illustrates @RISK's Advanced Sensitivity Analysis feature.
A set of example files which shows you how to use the more useful features of @RISK to a basic model. 1. A deterministic model...
Two versions of a basic @RISK model where:
1. Only one competitor can enter the market.
2. Multiple competitors can enter or exit the market.
A basic @RISK model where: 1. The uncertainty is essentially stationary through time. 2. The uncertainty changes through time because of business cycles. 3. The...
A set of basic @RISK model where you can find: 1. A deterministic model to get started. 2. Various quantities are uncertain but don't change...
A model that illustrates options for hedging against exchange rate variability, using the Time Series Fit feature on historical exchange rate data.
A model which illustrates how to run an Excel Goal Seek each iteration of a simulation.
A model for projecting future financial values, with various sources of uncertainty.
A portfolio model that uses the Time Series Batch Fit feature to project future stock prices from historical stock prices.
A basic portfolio model with correlated assets, including a comparison across different correlation values.
When a new product is developed, it is important to forecast its future financial path. Here you will find a set of examples to do...
An example where extremes in a portfolio of stock options are compared using copulas versus correlations.
If you own a portfolio of investments, you know that there is a great deal of uncertainty about the future worth of the portfolio. The...
A set of examples files to illustrate real options of projects: 1. A model that illustrates the real option of abandoning a project at a...
A model that illustrates how the Time Series Fit feature can be used on a series with trend and seasonality.
A set of examples to illustrate how a copula can be fit to bivariate data.
A model for projecting future stock prices.
A model that illustrates the Time Series Fit and Batch Fit features for fitting multiple correlated stock price series.
Two examples to illustrate one possible way of simulating the spread of an infectious disease. 1. A basic @RISK model where infected people are never...
Unlike other @RISK distribution function such as RiskNormal and RiskBinomial, the RiskCompound function is not used to generate random values from a specific distribution. It...
This model illustrates a projection of property damages and human costs from potential natural disasters over a 5-year period that might be assessed by an...
This model assumes that an insurance company is offering a local organization an insurance policy that will guard the organization against large heating oil costs...
This model uses @RISK to run a discrete-event simulation of insurance claims through time. It assumes that a company starts with an initial number of...
This model enables an insurance company to analyze the possibility of being reinsured. Without reinsurance, the company pays all claims, net of deductibles, for its...
This model contains a portfolio of potential claims of different types. Each claim has different parameters for the distributions of frequency, severity, and duration. The...
This example shows how you might model the uncertainty involved in payment of insurance claims. To model this properly, you must account for the uncertainty...
This example models different types of insurance claims from different lines of business and sums them in order to calculate an estimated total claims paid...
A model for evaluating a contingent contract, where a penalty must be paid if a target is not met.
It is important for an insurance company to estimate the amount of claims it will incur in a given year. Here you will find a...
An insurance model that illustrates @RISK's Stress Analysis feature.
Click here to see a video of this example.
TopRank recognizes @RISK distribution functions and incorporates them in What-If analyses. This ability provides more flexibility and accuracy in modeling the possible input values in...
This model represents the process flow of a company's internal sales quotation process. The process is taken from an actual company and has over 36...
This model is of a manufacturer that needs to reduce the number of defective units produced. It uses @RISK to pinpoint the manufacturing stage that...
This model is used to calculate the percentage of defective products. Each product component is nondefective if some property of its finished state lies within...
This is an extension of the DMAIC (Define Measure, Analyze, Improve, Control) failure model for use in quality control and planning. It includes the use...
This model explores an incentive to increase customer loyalty in a market such as the cell phone market. Each year, each of our customers remains...
This model is for a company that mails its catalog to a customer every quarter. For each customer, the company keeps track of the recency...
This model illustrates one possible way oil prices might change through time, as influenced by the market. @RISK's distribution fitting tool is used to simulate...
This model is of an oil operator who faces random oil prices and uncertainty in oil volumes over the next five years. To forecast future...
This model simulates risks in a network of oil pipelines. There are nine types of risks, and there are nine routes in the pipeline network....
This model is based on an actual Palisade consulting experience. It shows the logic implemented for modeling oil transportation considering severe weather interrupts shipments. For...
This model examines the familiar production forecasting model for oil and gas wells using the exponential decline curve.
This model forecasts production, revenues, and present value based on exponential decline.
This model analyzes a waterflood project, where recoverable oil must be estimated and one of four production schedules is used to generate a revenue stream....
Two important characteristics of rocks are porosity (percentage of "open space" and water saturation (fraction of water in the pore space). This model uses simulation...
A model that uses the Time Series Batch Fit feature to project fuel prices in a simulation of the next year's fuel costs.
A model that illustrates how the oil depletion allowance affects the value of an oil investment.
It is important in the oil industry to estimate a schedule of capital expenditures and production, and to see how this schedule depends on the...
The oil and gas industry needs to make forecasts of production, revenues, and expenses for a multi-year period in the face of much uncertainty. Find...
A model that illustrates how the Time Series Batch Fit feature can be used to fit several potentially correlated series all at once.
A model that illustrates the Time Series Fit feature can be used to generate future forecasts.
A set of examples to illustrate a volumetric reserves calculation in the oil industry: 1. A deterministic model to get started. 2. A basic @RISK...
In many circumstances one wishes to calculate the aggregate impact of many possible yes/no type events. For example, it is often important to answer questions...
The purpose of this model is to see how many hospitals are required to accommodate all patients in various scenarios. The model assumes that patients...
This example illustrates a general multiserver queueing system. Customers arrive at random times. If at least one of the servers is idle, an arrival goes...
This example adapts the general multiserver queueing system to a city's ambulance service. The ambulances are the "servers" and a customer "arrival"; corresponds to a...
A set of basic @RISK models for modeling events: 1. Any (or all) events could occur. 2. A version illustrating the RiskMakeInput function for use...
A model that illustrates one possible generic approach for generating demands for substitute products.
An inventory ordering model that illustrates how the Time Series Define feature can be used to generate demands.
A model which illustrates how the bullwhip effect can occur with a single retailer and supplier and a second model that illustrates the bullwhip effect...
This model of a company's multiple projects over a 12-month horizon. Each project is planned to start in a given month, and from that month...
A model that illustrates how @RISK can be used to correlate task times in a project.
A model for determining whether the total of uncertain costs meets a budget
A model of an electrical system that illustrates @RISK's Six Sigma functions.
These examples files can be used as a template to understand the capability of a system. You will find three of them: 1. A basic...
A model for building a catapult that illustrates @RISK's Six Sigma functions in the context of DOE.
A model for welding a disk onto a ring that illustrates @RISK's Six Sigma functions in the context of DOE.
A model that illustrates the use of the Taguchi quadratic loss function to measure uncertain quality.
A model for selecting a vendor on the basis of quality and cost.
According to ESPN Magazine, the NCAA tournament is one of the most wagered-on events in sports. Veteran bookmakers estimate bets range from $12 billion to...
Most PGA golf tournaments are played over 4 rounds, from Thursday to Sunday. If you follow the PGA, you have probably noticed that one or...
The World Cup features 32 international soccer teams that are allocated into eight groups of four. Within each group, all four teams play against each...
A model for simulating a best-of-7 game baseball World Series.
Click here to see a video of this example.
A model for simulating the NCAA Basketball "March Madness" Tournament
A simulation model for seeing how important a given lead is after early rounds of the tournament.
This model illustrates the RiskResultsGraph function for creating non-interactive graphs of specified inputs or outputs when a simulation is run.
The RiskSimtable feature can be used to run multiple simulations to test the sensitivity of the model, for example to changes in the parameters of...
The various @RISK functions in this series of examples generate families of distributions: the normal family, the binomial family, and so on. Each family is...
'@RISK doesn't have a RiskMultinomial function but this example shows you how to generate such distribution by using the RiskBinomial function repeatedly.
The RiskBeta functions are a set of flexible functions for generating an uncertain quantity known to be between given minimum and maximum values. This example...
This example explains how to use the Binomial and Bernoulli distributions.
The RiskCumul function provides a high degree of flexibility in describing the distribution of a continuous uncertain quantity. This example explains how to use it...
This example explains how to use the RiskDiscrete function which is used when you want to model an uncertain quantity with a finite -- that...
This example explains how to use the RiskDUniform and RiskIntUniform functions to generate equally likely integer values.
This example explains how to use the Exponential distribution.
This example explains how to use the RiskExtValue and RiskExtValueMin functions which are widely used in the Extreme Value Theory.
This example explains how to use the Gamma and Erlang distributions.
This example explains how to use the RiskGeneral and RiskHistogrm functions to to provide a high degree of flexibility in describing the distribution of a...
This example explains how to use the RiskGeomet function.
This example explains how to use the RiskHypergeo function.
This example explains how to use the RiskInvGauss function.
This example explains how to use the RiskJohnson function.
This example explains how to use the RiskLaplace function.
This example explains how to use the RiskLaplace function.
This example explains how to use the RiskLogistic and RiskLogLogistic functions.
This example explains how to use the RiskLognorm and RiskLognorm2 functions.
The RiskMakeInput function is listed in the @RISK Distributions group, but it doesn't really generate a distribution. This example explains how to use it for...
This example illustrates how to generate values from a negative binomial distribution.
This example illustrates how to generate a random value from the famous bell-shaped normal distribution with a given mean m and standard deviation s.
This example explains how to use the RiskLogistic and RiskPareto function.
This example explains how to use the RiskPearson5 and RiskPearson6 functions.
This example explains how to use the RiskPert function.
This example explains how to use the RiskPoisson function.
This example explains how to use the RiskRayleigh function.
The RiskSplice function lets you splice two distributions together at some point. This example shows how to use it.
The RiskStudent, RiskChiSq and RiskFfunctions can be used to simulate values from the three corresponding distributions commonly used in statistical inference: the Student's t, chi-square,...
The RiskTriang, RiskTrigen and RiskDoubleTriang functions are used for modeling a continuous uncertain quantity that must be between specified minimum and maximum values. This example...
The RiskUniform function generates a value that is equally likely to be anywhere between specified minimum and maximum values. This example shows how to use...
The RiskVary function is intended primarily for sensitivity analysis is @RISK's companion product, TopRank, but it is included in @RISK for compatibility with TopRank. You...
This example explains how to use the RiskWeibull function.
This model uses @RISK to illustrate that when several people with different prior beliefs all see the same random outcomes and use Bayes' rule to...
This model uses @RISK simulation to find the distribution of the number of events in a fixed amount of time when the times between events...
This model illustrates how the central limit theorem can be used instead of the RiskCompound function when an output is the sum of a random...
A model that illustrates how @RISK can automatically adjust an "illegal" correlation matrix.
Examples of how alternative parameters can be specified for common distributions.
A model that uses simulation to illustrate the famous central limit theorem.
A model that illustrates special ways of treating dates in @RISK simulations.
A model that illustrates various ways of specifying correlations between uncertain quantities.
Fitting several data sets at once to available distributions.
Fitting one data set to selected distributions.
Examples that illustrate how @RISK "property" functions can be embedded in other @RISK functions for various functionality.
A model that explains Latin Hypercube sampling and illustrates one particular ramification of using it.
A model that compares Monte Carlo and Latin Hypercube sampling.
A model that illustrates the use and syntax of the RiskOutput function
A model that illustrates @RISK's template report option for creating ready-made reports.
A model that illustrates the proper way to use multiple RiskSimtable functions.
A model that illustrates @RISK's tornado and spider graphs for sensitivity analysis.
A list of @RISK's six sigma functions: what they mean and how they work.
A model that illustrates the concept of one alternative stochastically dominating others.
A model that illustrates @RISK's Summary Trend chart, especially useful for time series outputs.
A set of examples to illustrate how to use the RiskTheo functions.
Illustrates how graphs of @RISK inputs/outputs can be included as "thumbnails" in cell comments.
A model that illustrates the RiskTruncate function and how @RISK samples from a truncated distribution.
This example illustrates two uses of Evolver. In the Parameter Estimation sheet, historical monthly values of sales and advertising are used to estimate the parameters...
This model uses Evolver to check whether a chess knight can make 64 consecutive moves and hit each square exactly once. It doesn't optimize anything....
This model uses NeuralTools to classify a large number of email messages as spam or not spam, based on a large number of characteristics of...
This oil drilling example is a classic decision tree problem, it demonstrates the use of PrecisionTree to analyze a multi-stage decision process. Our first decision...
Two versions of a model which illustrates the daily output of a combination of solar and wind energy units. 1. The @RISK outputs include hourly...
A gold mining project is divided into five separate mines, each with unique geological characteristics and cost variables. These variables (input costs, declination rates, plateau...
A manufacturer of fuel efficient cars believes that demand for this type of cars might increase in the next few years, so it wants to...
This model illustrates a portfolio optimization model which uses detailed financial calculations on a number of project worksheets to obtain project NPVs and present values...
Two examples files to illustrate the "conditional value at risk" concept from finance. 1. A model to illustrate how to find the VaR and the...
This model illustrates the newsvendor ordering model in a multiple-product setting with the possibility of demand diversion. This means that if supply of product A,...
An optimization model for deciding which of sequential house offers to accept.
A set of @RISK example models for E-Commerce Service. 1. A deterministic model to get started. 2. A basic @RISK model with demand uncertainty. 3....
An optimization model for deciding how to invest over a multi-year horizon to achieve a retirement goal.
An optimization model for determining how to invest over a time horizon to accumulate a down payment for a house.
An optimization model that balances groups of securities in a portfolio.
A portfolio optimization model that applies the Time Series Fit feature to historical stock price data.
This is a standard product mix model, where five product models must be assembled and then tested on either line 1 or line 2. You...
A capital budgeting model with uncertainty about projects' resource and capital usage and their NPVs.
A multi-day model for determining when cash should be invested or cash should be obtained.
This model plans production over the next six months in the face of uncertain demand. The company produces three products, and these products compete for...
A manufacturing company that is building a new production facility for the next 15 years must decide how much capacity to build now in the...
This is a simplified model of a multistage manufacturing process. Each stage has a number of identical machines, and each machine can produce a random...
This is a supply chain model of the relationship between a manufacturer and its supplier, modeled from the point of view of the supplier. The...
This model illustrates how disruptions at suppliers, such as weather, strikes, or others, can affect a supply chain, and how such disruptions can be mitigated....
This model finds optimal allocations of work hours in several work centers toward production of several SKUs in a specific month. There are four sources...
This model finds the optimal numbers of ads for a company to place in various media to minimize the mean cost per exposure. There are...
The goal of this model is to determine the allocation of marketing expenses to retaining current customers and acquiring new prospects. It assumes a nonlinear...
This model illustrates pricing in a two-channel market, Web and retail. If the price in one channel is set low, it will not only create...
This model illustrates why companies lower their prices from time to time, that is, why they have sales. Essentially, it is because different customers have...
This model, based on a real consulting experience, is of an oil company that has leased a field with 10 old wells to be worked...
A company faces infrequent and uncertain demands for a high-priced product. The company orders the product from a supplier, and there is an uncertain lead...
A company is open from 7AM until 11PM each day of the week. Each of its workers must work 5 consecutive days, and each day...
A model for determining optimal discount and full-fare limits on an airline flight.
Booking hotel rooms is an important problem for hotel managers. On the one hand, they want rooms to be occupied, and because of this and...
A large optimization model for producing multiple SKUs to meet demand (based on a real Palisade case).
An optimization model for determining the best order quantities when substitute products are competing for total demand.
A single-season optimization model for ordering style goods, where ordering can occur after early sales have been observed.
An optimization model that determines the best schedule for performing tasks on various machines.
An optimization model for ordering jobs to meet due dates as nearly as possible.
Two versions of a workforce planning model are shown here: 1. A basic @RISK model for simulating several years of job movements within an organization....
The purpose of this model is to schedule starting years of 10 projects. Each project has a random expenditure in its initial year, a random...
An optimization model that schedules college classes with uncertain enrollments in different time slots.
A set of basic @RISK models for finding the system's reliability: 1. The probability that systems functions. 2. A version where the parts are correlated...
An illustration of penalty functions and how they can be used in soft constraints.
This case study illustrates a potential implementation of regulatory requirements to quantify the probability of business continuity. It demonstrates a potential quantification of operating and...
This example looks at a process with 5 stages that handle units over a period of a year. Units could represet sales opportunities, hiring canditates,...
This case study illustrates a potential implementation to quantify the probability of business continuity. It demonstrates a potential quantification of operating and risk register (low...
