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 model for locating power stations to cover all towns and use minimal power
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.
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 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 is used to calculate the percentage of defective products. Each product component is nondefective if some property of its finished state lies within...
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 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.
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...
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 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 for determining whether the total of uncertain costs meets a budget
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 simulation model for seeing how important a given lead is after early rounds of the tournament.
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...
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 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 RiskLaplace function.
This example explains how to use the RiskLaplace function.
This example explains how to use the RiskLogistic and RiskLogLogistic functions.
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 RiskPearson5 and RiskPearson6 functions.
This example explains how to use the RiskPert 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...
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 special ways of treating dates in @RISK simulations.
A model that illustrates various ways of specifying correlations between uncertain quantities.
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 @RISK's template report option for creating ready-made reports.
Illustrates how graphs of @RISK inputs/outputs can be included as "thumbnails" in cell comments.
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 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...
An optimization model for deciding how to invest over a multi-year horizon to achieve a retirement goal.
A portfolio optimization model that applies the Time Series Fit feature to historical stock price data.
A capital budgeting model with uncertainty about projects' resource and capital usage and their NPVs.
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 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...
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.
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...
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...