Hedging Iceberg Lettuce Production: Palisade Software Determines Best Method for Managing Margins in Agriculture

Many California produce farm operations use a rule-of-thumb to determine a hedge ratio for their seasonal productions. They often aim to contract 80% of their crop in advance to buyers at set prices, leaving the remaining 20% to be sold at spot prices in the open market. The rationale for this is based on many years of experience that indicates costs and a reasonable margin can be covered with 80% of production hedged by forward contracts. The hope is the remaining 20% of production will attract high prices in favorable spot markets, leading to substantial profits on sales. Of course, it is understood spot prices might not be favorable, in which case any losses could be absorbed by the forward sales.

Since the Recession of 2008, agricultural lenders and government regulators have recognized that many farm operators need to manage the risks to their margins, and free cash flows, rather than simply focusing revenue risks. A more quantitative analysis is needed to determine risks in the agricultural industry.

Agribusiness experts from Cal Poly conducted a risk management analysis using @RISK, and found the 80% hedge ratio rule-of-thumb is not as effective as assumed. Growers do not profit from spot market sales over the long run. The analysis shows growers are better off in the long-term selling as much of their product as possible using forward contracts.


Agriculture in California is big business. In 2013, nearly 80,000 farms and ranches produced over 400 commodities – the most valuable being dairy, almonds, grapes, cattle, and strawberries – worth $46.4 billion. Almost half of this value came from exports. The state grows nearly half of the fruits, nuts, and vegetables consumed in the United States. Yet agriculture is traditionally one of the highest risk economic activities.

Steven Slezak, a Lecturer in the Agribusiness Department at Cal Poly, and Dr. Jay Noel, the former Agribusiness Department Chair, conducted a case study on an iceberg lettuce producer that uses the rule-of-thumb approach to manage production and financial risks. The idea was to evaluate the traditional rule-of-thumb method and compare it to a more conservative hedging strategy.

Hedging Bets on Iceberg Lettuce Sales

The grower uses what is known as a ‘hedge’ to lock in a sales price per unit for a large portion of its annual production. The hedge consists of a series of forward contracts between the grower and private buyers which set in advance a fixed price per unit. Generally, the grower tries to contract up to 80% of production each year, which stabilizes the grower’s revenue stream and covers production costs, with a small margin built in.

The remaining 20% is sold upon harvest in the ‘spot market’ – the open market where prices fluctuate every day, and iceberg lettuce can sell at any price. The grower holds some production back for spot market sales, which are seen as an opportunity to make large profits. “The thinking is, when spot market prices are high, the grower can more than make up for any losses that might occur in years when spot prices are low,” says Slezak. “We wanted to see if this is a reasonable assumption. We wanted to know if the 80% hedge actually covers costs over the long-term and if there are really profits in the spot market sales. We wanted to know if the return on the speculation was worth the risk. We found the answer is ‘No’.”

This is important because growers often rely on short-term borrowing to cover operational costs each year. If free cash flows dry up because of operational losses, growers become credit risks, some cannot service their debt, agricultural lending portfolios suffer losses, and costs rise for everybody in the industry. Is it a sound strategy to swing for the fences in the expectation of gaining profits every now and then, or is it better to give up some of the upside to stabilize profits over time and to reduce the probability of default resulting from deficient cash flows?

Combining Costs and Revenues in @RISK

Slezak and Noel turned to @RISK to determine an appropriate hedge ratio for the grower.

For inputs, they collected data on cultural and harvest costs. Cultural costs are the fixed costs “necessary to grow product on an acre of land,” such as seeds, fertilizer, herbicides, water, fuel etc., and tend to be more predictable. The researchers relied on the grower’s historical records and information from county ag commissioners for this data.

Harvest costs are much more variable, and are driven by each season’s yield. These costs include expenses for cooling, palletizing, and selling the produce. To gather data on harvest costs for the @RISK model, Slezak and Noel took the lettuce grower’s average costs over a period of years along with those of other producers in the area, and arrived at an average harvest cost per carton of iceberg lettuce. These costs were combined with overhead, rent, and interest costs to calculate the total cost per acre. Cost variability is dampened due to the fact that fixed costs are a significant proportion of total costs, on a per acre basis.

The next inputs were revenue, which are defined as yield per acre multiplied by the price of the commodity. Since cash prices vary, the grower’s maximum and minimum prices during the previous years were used to determine an average price per carton. Variance data were used to construct a distribution based on actual prices, not on a theoretical curve.

To model yield, the grower’s minimum and a maximum yields over the same period were used to determine an average. Again, variance data were used to construct a distribution based on actual yields.

Palisade StatTools was used to create these distribution parameters. @RISK was used to create a revenue distribution and inputs for the model. With cost and revenue simulation completed, the study could turn next to the hedge analysis.

"A finance professor brought the software in one day and said, ‘if you learn this stuff you’re going to make a lot of money,’ so I tried it out and found it to be a very useful tool."

Steven Slezak
Aribusiness Department, Cal Poly University

To Hedge, or Not to Hedge?

Since the question in the study is about how best to manage margin risk – the probability that costs will exceed revenues – to the point where cash flows would be insufficient to service debt, it was necessary to compare various hedge ratios at different levels of debt to determine their long-term impact on margins. @RISK was used to simulate combinations of all costs and revenue inputs using different hedge ratios between 100% hedging and zero hedging. By comparing the results of these simulation in terms of their effect on margins, it was possible to determine the effectiveness of the 80% hedging rule of thumb and the value added by holding back 20% of production for spot market sales.

Unsurprisingly, with no hedge involved, and all iceberg lettuce being sold on the sport market, the simulation showed that costs often exceeded revenues. When the simulation hedged all production, avoiding spots sales completely, the costs rarely exceeded revenues. Under the 80% hedge scenario, revenues exceeded costs in most instances, but the probability of losses significant enough to result in cash flows insufficient to service debt was uncomfortably high.

It was also discovered that the 20% of production held back for the purpose of capturing high profits in strong markets generally resulted in reduced margins. Only in about 1% of the simulations did the spot sales cover costs, and even then the resulting profits were less than $50 per acre. Losses due to this speculation could be as large as $850 per acre. A hedging strategy designed to yield home runs instead resulted in a loss-to-gain ratio of 17:1 on the unhedged portion of production.

Slezak and his colleagues reach out to the agribusiness industry in California and throughout the Pacific Northwest to educate them on the importance of margin management in an increasingly volatile agricultural environment. “We’re trying to show the industry it’s better to manage both revenues and costs, rather than emphasizing maximizing revenue,” he says. “While growers have to give up some of the upside, it turns out the downside is much larger, and there is much more of a chance they’ll be able to stay in business.”

In other words, the cost-benefit analysis does not support the use of the 80% hedged rule-of-thumb. It’s not a bad rule, but it’s not an optimal hedge ratio.

Early @RISK Adopter
Professor Slezak is a long-time user of Palisade products, having discovered them in graduate school. In 1996, “a finance professor brought the software in one day and said, ‘if you learn this stuff you’re going to make a lot of money,’ so I tried it out and found it to be a very useful tool,” he says. Professor Slezak has used @RISK to perform economic and financial analysis on a wide range of problems in industries as diverse as agribusiness, energy, investment management, banking, interest rate forecasting, education, and in health care.


Identifying Optimum Mitigation Strategies for Debris Flow Hazards with PrecisionTree


Modeling the frequency and magnitude of future debris flows to determine the optimum hazard mitigation strategy. Communicating risk to clients by displaying the probability of event paths for three decisions:

  1. Existing conditions
  2. Constructing a containment dam
  3. Relocating existing residences


Duncan Wyllie, a Principal of Wyllie & Norrish Rock Engineers, uses the Palisade software PrecisionTree for probabilistic modeling of debris flow protection measures.

When analyzing the optimum method of protecting an area at risk from debris flows, three decisions are compared – accepting existing conditions, constructing a containment dam with sufficient capacity to contain future flows, or relocating residences on the debris flow runout area. Creating probabilistic decision trees in PrecisionTree allows uncertainties in the frequency and magnitude of future debris flows to be analyzed, and for comparison of costs between constructing a dam and relocating the residences.


Wyllie & Norrish Rock Engineers, with offices in Seattle and Vancouver, Canada, is a specialist engineering company working in the fields of landslides, tunnels, slopes, and foundations. Duncan Wyllie and Norman Norrish, the company principals, have a combined total of 80 years of experience in applied rock mechanics.

Since the 1990s, Wyllie and Norrish have been utilizing Palisade software to analyze natural hazards and select hazard mitigation procedures.

Using Palisade Products

When a potential debris flow hazard is located above a residential development, PrecisionTree can be used to create a probabilistic decision tree that maps out possible scenarios, the likelihood they will occur, and the estimated damage costs. Three decisions are compared – existing conditions, constructing a debris flow dam, or evacuating the debris flow runout area.

"If we use @RISK and PrecisionTree to present results, people can make rational decisions as to what structural protection to install."Duncan Wyllie
Principal of Wyllie & Norrish Rock Engineer

Debris Flow Dam Decision Tree Example

With reference to the decision tree shown below, the components of the analysis are as follows:

For a closer look, download our free Debris Flow Containment Dam example model.

Analysis shows that the optimum decision is to construct a containment dam because the total cost of mitigation plus the expected cost (EV) of damage is lower for the dam construction (EVΣdam = $200,150) than for existing conditions (EVΣexisting = $360,000) or for relocating the houses (EVΣhouses = $2,000,600).


The use of PrecisionTree allows possible mitigation measures, along with the probability of event occurrence and cost, to be analyzed. The analysis unambiguously identifies the most cost-effective mitigation measure, and the decision process is clearly mapped out in the decision tree.

A Competitive Edge

The use of @RISK and PrecisionTree software to prepare decision trees modeling all potential outcomes enables Wyllie & Norrish Rock Engineers to quantitatively determine the optimum protection strategy and easily communicate the findings.

With Palisade’s products, Wyllie & Norrish Rock Engineers can:

By using probabilistic analysis, Wyllie & Norrish Rock Engineers ensure that the best decision is reached for each at-risk area and if necessary, effective debris flow dams are created to protect nearby structures.

Free Example Models

Download our free example model, Decision Trees in Geotechnical Engineering, to explore three decision tree examples from the geotechnical engineering field: debris flow containment dam, rock slope stabilization, and gravity dam reinforcement anchors.

Leveraging Probabilistic Analysis to Improve Rock Fall Protection Structure Designs with @RISK and PrecisionTree


Modeling rock fall masses, trajectories, velocities, and energies to design rock fall protection structures. Communicating risk to clients by calculating impact energy probability so that the impact capacity of the protection structure can be matched to the consequence of an accident.


Duncan Wyllie, a Principal of Wyllie & Norrish Rock Engineers, uses @RISK and PrecisionTree for probabilistic modeling of rock fall hazards. The analyses incorporate uncertainty in values of the mass and velocity that are expressed as probability distributions, and Monte Carlo simulation in @RISK is used to calculate the probability distribution of the impact energy. The energy calculations are then used to design protection structures such as concrete sheds and wire rope fences with the appropriate impact energy capacity to suit the possible consequences of an accident. Decision analysis using PrecisionTree is then applied to determine the optimum mitigation strategy.

In the example, Wyllie explains how Palisade software is used to calculate potential rock fall impact energies and make recommendations for a fence to protect traffic at the base of a steep mountain slope.


Wyllie & Norrish Rock Engineers, with offices in Seattle and Vancouver, Canada, is a specialist engineering company working in the fields of rock slopes, tunnels, blasting, foundations, landslides, and rock falls. Duncan Wyllie and Norman Norrish, the company principals, have a combined total of 80 years of experience in applied rock mechanics.

Since the 1990s, Wyllie and Norrish have been utilizing Palisade software to analyze rock fall risks and hazards, and design rock fall protection structures. They provide hazard mitigation services for rock falls in mountainous areas by identifying rock fall sources, modelling rock fall trajectories and energies, and designing customized protection structures. Projects have been undertaken for highways, railways, hydro-electric power plants, and residential developments.

Using Palisade Products

For most rock fall projects, very limited, or no, information is available from previous events. In these circumstances, uncertainty exists in the design parameters of rock fall frequency, mass, velocity and trajectory. These uncertainties can be quantified using @RISK to define probability distributions that account for the possible range of values, and the most likely values, based on judgement and experience.

Wyllie found that the BetaGeneral and Pert distributions incorporated in @RISK provide the optimum models for these conditions. Multiplication of mass by the square of the velocity distributions gives the impact energy that is also defined by a probability distribution. This information can be used to design protection structures that reduce the hazard to an acceptable level of societal risk.

Another component of risk management for rock fall projects is to implement decision analysis in which alternative courses of action, such as construction of a high strength containment fence or of a less expensive ditch, can be compared. This analysis can be carried out using PrecisionTree in which the sum of construction costs and expected value of an accident (i.e., the product of an accident cost and its probability) can be compared for each course of action. PrecisionTree allows rapid analysis of these alternatives and incorporates sensitivity analyses that show how uncertainty in values of the costs and probabilities influence the selection of the optimum action.

A particular value of analyses using @RISK and PrecisionTree is that it is possible to define low risk but high consequence events that have a low expected value, such as a large-scale landslide. Comparison of this event with more frequently occurring, but less costly rock falls will show if the optimum mitigation measure is to stabilize the landslide or contain the rock falls. The analyses often show that very rare events are acceptable.

"If we use @RISK and PrecisionTree to present results, people can make rational decisions as to what structural protection to install."

Duncan Wyllie
Principal of Wyllie & Norrish Rock Engineer

Rock Fall Modeling Example

When a rock fall source on a steep mountain slope was identified above a road, Wyllie used @RISK to calculate the probability distribution of the potential rock fall impact energies that would be used to design a protection structure. Because the site had no history of rock falls that could be used for design, @RISK was used to analyze the geology and empirical equations for velocity to develop BetaGeneral distributions for the mass and velocity, from which the distribution for the impact energy was calculated. These plots are shown below where the maximum, minimum, and mean values for mass, velocity, and energy are indicated.

These results were discussed with the owner to determine an appropriate design energy capacity for a fence to protect the road from rock falls.

“Because access to the road was restricted and traffic was infrequent, it was decided that a rock fall event with high energy but low probability was acceptable,” said Wyllie. “The design energy of 1250 kJ was selected such that about 90% of falls would be contained, with the understanding that the low probability of an event with an energy exceeding 1250 kJ was acceptable.”

The image below shows the installed Attenuator fence.

In comparison, if the fence was installed above a busy Interstate highway where the consequence of a high energy rock fall event could be severe, it is likely that the design energy would be about 2500 kJ to 3000 kJ to ensure that almost all rock falls would be contained.

“If we use @RISK and PrecisionTree to present results, people can make rational decisions as to what structural protection to install,” said Wyllie.


Thanks to the probabilistic analysis conducted by Wyllie, the road has a fence in place that can withstand an impact energy of 1250 kJ that will contain about 90% of future rock falls. Those traveling on the road can have peace of mind knowing the hazard mitigation structure was designed through quantitative analysis.

A Competitive Edge

The use of @RISK and PrecisionTree software to prepare designs where many or all of the design parameters are uncertain allows Wyllie to quantitatively determine the best mitigation strategy.

With Palisade’s products, Wyllie & Norrish Rock Engineers can:

By using deterministic analysis, Wyllie & Norrish Rock Engineers ensure that effective hazard mitigation structures are in place to protect people, facilities, and infrastructure.

Using @RISK and PrecisionTree to Shape the Future of Drug Development in Neurodegenerative Diseases

AC Immune SA, a biopharmaceutical company focused on developing product candidates to treat neurodegenerative diseases, harnesses the power of Lumivero's products—specifically @RISK and PrecisionTree, to assess the value of the company’s development candidates leading to the overall enterprise value. Using @RISK, AC Immune calculates the risk adjusted net present values (rNPVs) for its preclinical and clinical drug candidates. Using PrecisionTree, the company values key decisions along the development pathway. Thanks to Lumivero, AC Immune has been able to manage risk, define prediction intervals, communicate clearly to internal stakeholders, and ask more ‘what if’ questions based off their models.


AC Immune SA is a clinical-stage biopharmaceutical company leveraging their two proprietary technology platforms to discover, design and develop novel proprietary medicines and diagnostics for prevention and treatment of neurodegenerative diseases (NDD) associated with protein misfolding.

Misfolded proteins are generally recognized as the leading cause of NDD, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), with common mechanisms and drug targets, such as amyloid beta (Abeta), Tau, alpha-synuclein (a-syn) and TDP-43. AC Immune’s corporate strategy is founded upon a three-pillar approach that targets (i) AD, (ii) focused non-AD NDD including Parkinson’s disease, ALS and NeuroOrphan indications and (iii) diagnostics. They use their two unique proprietary platform technologies, SupraAntigen and Morphomer to discover, design and develop novel medicines and diagnostics to target misfolded proteins.

Using Lumivero Products

AC Immune uses @RISK to assess the Company’s enterprise value, calculating risk-adjusted net present values (rNPVs) for certain preclinical and clinical product candidates. The company then combines each respective value and determines the ultimate “Sum of the Parts” for an overall indication of the company’s price per share. The company uses this internally generated value and bridges to potential variances in their share price (Nasdaq: ACIU) or price targets published by their covering analysts.

Each product candidate that AC Immune elects to value includes many uncertain variables which impact the projected net cash flows in the development and potential commercialization period for the product candidate. The typical inputs AC Immune uses in their @RISK models include, but are not limited to:

“Certain of these variables can be material and are difficult to derive a point estimate for, or can be difficult to otherwise source,” explains Julian Snow, AVP of Financial Reporting.

An Invaluable Asset for Student Field Placement Management

“PrecisionTree allows us to set up dynamic decision trees linked to underlying cash flows to understand the risk/return at a specific point in time along the development timeline, additionally, it helps us weight a decision such as to partner or not partner a potential product candidate.”Julian Snow
AVP of Financial Reporting

Typically, Snow uses a PERT distribution for his @RISK models for the risk-adjusted NPV. These inform AC Immune of the 90% prediction interval range, given the assumptions. In this example, the mean rNPV is expected to be around CHF 523m (illustrative example only).

AC Immune also uses tornado charts to showcase the impact of certain assumptions on the resultant value, all other variables held constant. “The company can then decide how best to minimize the impact of certain factors via additional research into the assumption or other potential adjustments,” says Snow.

In addition to @RISK, AC Immune also uses PrecisionTree. “PrecisionTree allows us to set up dynamic decision trees linked to underlying cash flows to understand the risk/return at a specific point in time along the development timeline,” says Snow. “Additionally, it helps us weight a decision such as to partner or not partner a potential product candidate.”

Other decisions include the expansion of a program, addition of a second indication for research, or assistance in license and collaboration deal structuring. “Assessing the value of one decision or another is valuable for the company,” Snow says.

Benefits of Lumivero Products

For AC Immune, our products significantly improve the quality of the decision-making process, particularly with regard to allocation of resources and improving understanding of the magnitude of uncertainty on key assumptions.

“Palisade software allows our company to sensitize key variables using various distribution methods, as well as convey the sensitivity in impact on the ultimate risk-adjusted net present value,” says Snow. “The software also conveys results in clear output graphics for easy reporting to relevant stakeholders.”

Prior to using our products, Snow and his team relied on Excel functionalities to calculate the relevant data. “We viewed this process as static and more cumbersome to maintain,” says Snow. “Therefore, with Palisade, AC Immune was able to enhance its internal valuation and reporting capabilities.”

A Competitive Edge

According to Snow, other companies in this space do not typically leverage deterministic analysis to their valuation approaches. “Most peers use more static excel models that cannot capture or answer a more robust set of questions that arise over a long development timeline,” says Snow.

In addition, when comparing the cost-benefit of programs, assessing internal funding needs, assessing potential licensing and collaboration terms and other matters relevant to understanding the potential financial return from a product candidate, “AC Immune is able to ask and answer more questions than peers as a result of the use of the software,” Snow says.

Thanks to Lumivero's products, AC Immune has seen both tangible and intangible benefits, including:

Thanks to data-driven, deterministic analysis, AC Immune’s cutting-edge drug discovery technologies are better enabled to potentially help patients around the world.

IRIS and @RISK Keep London Underground's Projects on Track

London Underground uses risk consultants Istria Ltd and its software tool IRIS, with @RISK embedded as the engine, to manage diverse risks from investment projects to passenger safety.

When it opened in 1863, London Underground was the world’s first underground railway. Today, this vital metro system, known locally as ‘The Tube’, provides over three million passenger journeys a day to and from 275 stations. The network stretches across 253 miles (408 km) and is operated, maintained and supported by 12 000 staff. London Underground is crucial to the economy of London and, in turn, of the United Kingdom as a whole. The risks inherent in the operation of such a complex and vital system are many.

IRIS and @RISK Central to London Underground Operation

London Underground uses Risk Consultants Istria Ltd and its software tool IRIS to manage its diverse risks. Everything from investment projects to passenger safety must be considered, and IRIS, with the embedded Palisade @RISK engine, are well-suited to the task.

Istria believes that, like quality, risk management should be designed-in to the planning and implementation of all plans and activities throughout an organisation, rather than being an add-on, peripheral ‘chore’ conducted at the behest of Head Office. Istria’s IRIS software (Istria Risk and Issue Support) provides a comprehensive risk solution based on Istria’s Enterprise Risk Management Method. @RISK is employed within IRIS to generate accurate management information using Monte Carlo simulation. This @RISK data analysis process is critical to effective decision-making.

"London Underground has derived significant financial and performance benefits from using IRIS and its excellent @RISK-driven Monte Carlo functionality."Tylaar Haran
Group Facilities UIP Projects Manager, London Underground

Financial Benefits and Corporate Goals

London Underground’s Tylaar Haran (Group Facilities UIP Projects Manager) is quick to praise the value of using @RISK to augment IRIS. “London Underground has derived significant financial and performance benefits from using IRIS and its excellent @RISK-driven Monte Carlo functionality. The combination of IRIS and @RISK is a powerful tool to support decisions on the feasibility of potential projects and to drive-forward the identification and selection of options in complex, uncertain scenarios.”

Istria’s Risk Method links all risk-related activities directly to the attainment of corporate goals and objectives. As Chief Executive Colin Wheeler observes, “Increasingly, companies and government bodies are realising that risk management can provide tangible benefits in improving the ‘bottom-line’ or enhancing service delivery. All too often, however, the approaches used to introduce and operate risk management have no clear link to the overall strategic aims of the organisations. Istria’s method maintains a clear, auditable link to strategic and operational success criteria; this focuses the risk management effort on supporting the attainment of corporate goals and objectives.”

US Army Uses @RISK to Address Schedule and Cost Risk in Acquisitions

@RISK is used at AMSAA to help senior level officials avoid risks of schedule and cost overruns in equipping and sustaining weapons and materiel for U.S. soldiers in the field.

The US Army Materiel Systems Analysis Activity (AMSAA) conducts critical analyses to provide state-of-the-art analytical solutions to senior level Army and Department of Defense officials. Analyses by AMSAA support the equipping and sustaining of weapons and materiel for U.S. soldiers in the field, and inform plans for the future. @RISK is used at AMSAA to help senior level officials avoid the risks of schedule and cost overruns. New @RISK models integrate schedule and cost consequences, and employ Monte Carlo simulation to give decision-makers the best information possible.

Integrated Model Addresses Schedule and Cost Risks at AMSAA

One of the top priorities of the U.S. Army is to make better decisions in their acquisition programs. When considering any new materiel, decision-makers want to know the risk, cost, performance and operational effectiveness of each new acquisition. In this case, risk is the likelihood of not meeting the target time with schedule and cost consequences.

The US Army Materiel Systems Analysis Activity, known as AMSAA, conducts critical analyses to equip and sustain weapons and materiel for soldiers in the field and future forces. The Army is charged with determining the best possible choice among several acquisition options, taking care to examine alternatives in tradespace, sensitivity, and cost and schedule risk mitigation. Currently, with the help of @RISK, AMSAA conducts separate schedule and technical risk assessments, which are not connected to the cost risk assessment (approaches are in silo). However, AMSAA Mathematician and Statistician John Nierwinski decided to use @RISK to integrate schedule and cost consequences in order to provide a single risk rating that decision-makers can efficiently use to inform the overall decision. “This is cutting edge stuff,” says Nierwinski. Discussions are in process on ways to implement this in the future and gradually phasing out of the silo approach to risk evaluation. This modeling approach has not yet been applied to any acquisition studies; however, some of its submodels have been applied. Integrated risk ultimately translates to integrated schedule and cost that can be traded off with performance and operational effectiveness.

Monte Carlo Simulation Exposes the Risk of Project Overruns

The first step in Nierwinski’s analysis requires a schedule network model, with technology development, integration, manufacturing, and other events. Completion time for these events are assessed using data or subject matter experts (SME). If SME’s are used it is common to use a triangular time distribution with a minimum, maximum and most likely number of months to progress from Milestone (MS) B to MS C. @RISK uses this schedule network model as the main driver of the Monte Carlo process, which is an integrated risk model. For each iteration output of the @RISK model, a consequence determination is made using cost and schedule if the time exceeded the MS C target date. A cost model is used based on the technology cost information that is available. The main cost items are due to the fact that schedule delays can increase variable costs, such as labor expenses. Acquisition variable costs are typically estimated from database or expert information. Hence, if a program goes over the milestone date by a number of months, then the additional cost to the project will be the number of months times the variable cost per month.

With schedule and cost variables in place Nierwinski then creates integrated outputs from the model, which will then, with several thousand iterations, create scenarios that involve an overrun or underrun, depending on the iteration. For overrun situations there are two sets of consequences, cost and schedule. Each set of consequences range from 1-5 (best-case to worst-case scenarios). A given simulated run will yield a cost and a schedule consequence. The maximum of the two consequences is selected for each simulated run. This is called the max consequence. If the target time is not exceeded for a given simulated run, then the max consequence is set to none or 0.

"This is cutting edge stuff. ... @RISK enables us to build various kinds of risk models quickly, with lots of flexibilities."John Nierwinski
AMSAA Mathematician and Statistician

Getting a Single Risk Rating

After running the integrated model, @RISK gives an output that tells the likelihood of not meeting the schedule, and the collection of maximum consequences from schedule overrun scenarios.

As the figure above indicates, ‘0’, is the chance that there will be no schedule overrun. Nierwinksi then translates this finding into a matrix to view the risk distribution in table form:

“This allows you to see your probabilities of being in each of these consequence ‘buckets’,” says Nierwinksi. Because this data is a probability distribution, it is easy to determine the probability of not meeting the schedule on time by subtracting the probability of meeting the milestone from 1, (1 – 0.18= 0.82) meaning there is an 82% likelihood of missing the schedule date.

Color-Coding Risk

Nierwinski then applies this distribution to a pre-established DOD color-coding system known as the DOD risk reporting matrix to determine the transformed risk distribution in the lower right hand corner of the figure below. The risk reporting matrix is typically used to report a risk level within a program. The level of risk is reported as low (green), moderate (yellow), or high (red) based on the mapping of the likelihood and consequence to a single square on the risk reporting matrix.

To compute probabilities in the transformed risk distribution, Nierwinski divides probabilities for consequence 1–5 by the probability of missing the schedule date, 0.82 (for example, consequence 1 = .25 / .82 = .30). The transformed consequence level probability is the conditional probability of the consequence level given a schedule overrun. The likelihood of 0.82 corresponds to a likelihood level of 5 in the risk reporting matrix (see figure above) therefore colors are green, yellow, red, red, and red for each consequence. Once Nierwinski formats the risk distribution, he determines an expected color by multiplying the probability of obtaining each color by 1, 2 or 3 (numerical rating: green = 1, yellow = 2, and red = 3). The expected color rating in the example above is:

1(.304) + 2(.12) + 3(.15 + .12 + .302) = 2.27

Next, he transforms the discrete scale of green/yellow/red colors to a continuous numerical scale. Since colors range from green (1) to red (3), the numerical difference is 2. Therefore, a range of 1 to [1 + 2/3 = 1.667] is green; a range of 1.667 to [1.667 + 2/3 = 2.333] is yellow; and 2.333 to 3 is red. Therefore, in our example, a 2.27 rating implies a yellow or medium risk rating for the alternative.

@RISK Brings Flexibility to Modeling Material Acquisitions at AMSAA

“Once we’ve established a risk rating for a certain materiel option, let’s say it’s a particular alternative for a kind of tank, we can do all sorts of studies (i.e. tradespace, what-if scenarios, risk mitigations)—we can examine what would happen to the risk if we were to swap out its engine for a cheaper one,” says Nierwinski. “This can change a lot of things. We can study how the risk rating changes: for example, it may go from high risk to low risk.”

Furthermore, we can extrapolate risk into integrated cost and schedule output, make joint probability statements, and conduct tradeoff decisions with performance and operational effectiveness. @RISK informs this acquisition decision making process.

Nierwinski says that @RISK was instrumental in modeling the inherent risk and uncertainty with materiel acquisitions that AMSAA faces. “@RISK enables us to build various kinds of risk models quickly, with lots of flexibilities.”

Schedule Risk Management in a Manufacturing Company

Updated: Sep. 18, 2023

This case study shows an example of how to manage schedule risk that could affect the realization of different strategic and tactical goals of a manufacturing company.

Addressing Schedule Risk

Different factory units need regular maintenance. During the maintenance work the production has to be shut down, which causes a reduction fall in the company’s income and profit.

The goal of applying risk management was to determine how a manufacturing company can ensure the highest level of income / profit by assessing and tackling different risks that occur during maintenance work.

The Szigma IntegRisk® method, developed by Hungarian Risk Management Company SzigmaSzervíz Ltd, uses Microsoft Project® 2013 and @RISK (risk analysis add-in for Microsoft Excel and Project) for the analysis (now known as ScheduleRiskAnalysis from a 2022 feature update included in DecisionTools Suite).

The Szigma IntegRisk® method is an integrated risk assessment, treatment and monitoring technique for supporting management decisions on strategic, tactic and operational level.

Dr. István Fekete, Managing Director for SzigmaSzervíz, believes that a combination of @RISK and Szigma IntegRisk® facilitates an integrated approach to quantifying schedule risks in case there are not historical data available.

"Undertaking risk management activity opens up the possibility of meeting the original deadline of maintenance works and avoiding the € 5M loss."


The project managers had prepared a schedule for maintenance work with the help of Microsoft Project®. The length of the simplified project schedule’s critical path was calculated. In this example, the original duration before risk assessment is 26 days. (See Figure 1.) This same approach can be used with the new ScheduleRiskAnalysis which also features the ability to upload your Primavera P6 or Microsoft Project files to @RISK.

Figure 1: Simplified project schedule

The next step was to assign potential schedule risk sources/risk events for each project activity in a series of workshops. A risk database compiled on the basis of several years of experience was used (included in Szigma IntegRisk® method). An example can be seen in Table 1.

Scenario analysis was performed as the next step to describe a series of impacts and the probability of their occurrence for each identified risk event. (An impact refers to a risk event that can affect the original duration of a project activity). Scenario analysis is a useful tool to describe the perception of different experts and produces more reliable input data for Monte-Carlo simulation, if historical data are not available.

Selection of the critical risks was based on the previously defined threshold values. If the calculated mean value and/or standard deviation overrun the threshold, that risk is labelled as critical risk.

Following that, risk treatment actions were created for all critical risks. (See Table 1)

Table 1: Example of risk assessment

The next step was to run Monte Carlo simulation using @RISK. During the simulation, lognormal distributions have been included by using mean value and standard deviation from the scenario analysis (see Table 1). SzigmaSzerviz’s experience shows that lognormal distribution is the best way to describe the nature of the revised activity duration once identified risks have occurred. This identifies the risk events that might cause huge deviation, but have a very low probability of occurrence compared to original activities’ duration. As a result, the probability distribution of the modified length of critical path was calculated. (See Figure 2.)

Figure 2: Probability distribution of the length of critical path

After finishing Monte Carlo simulation, a Tornado-diagram was generated to show the activities most likely to be responsible for delays to the project. The identified risk events assigned to most of these activities might cause the biggest deviation compared to the original length of critical path so the risk treatment actions for these activities are first carried out. (See Figure 3)

Figure 3: Tornado diagram

As a result of Monte Carlo Simulation the difference between mean and original value of the length of critical path is roughly 4 days. (26 versus 30, 258 see Figure 2). If the duration of maintenance activities is longer than planned the restart of the production is delayed which causes significant income/profit deficit for the manufacturing company. For this reason it is vital to model the impact of the results of the risk assessment on production and income/profit plan of the company, as illustrated in Figure 4.

Figure 4: Essence of the developed model

The result demonstrates that, if the maintenance work is delayed by 4 days from the scheduled date due to inadequately managed risks, the profit before tax might decrease with HUF (24,981 – 23 606) = 1.375 Bn (€ 5M). However, undertaking risk management activity opens up the possibility of meeting the original deadline of maintenance works and avoiding the € 5M loss.

According to Mr. Fekete the key benefits of @RISK are as follows:

About SzigmaSzervíz Ltd

SzigmaSzerviz Ltd., established in 2006, is the first Hungarian company engaged in integrated risk management. Szigma IntegRisk® risk management system and software is unique on the international market. This system gives an effective method for decision-makers, to support decisions ranging from strategic planning, to project management and annual planning, to internal auditing. SzigmaSzerviz Ltd. aims to foster risk awareness in organizations. Szigma IntegRisk® is not just software, but a complex business solution, which can provide a significant competitive edge to companies in the current economic environment.

New ScheduleRiskAnalysis with DecisionTools Suite

Solve schedule problems like one demonstrated in this SzigmaSzerviz Ltd. case study with the new ScheduleRiskAnalysis in DecisionTools Suite. Apply Monte Carlo simulation to Microsoft Project and Oracle Primavera P6 models, generate probabilistic Gantt charts, and easily calculate and report the Critical Index – all within Microsoft Excel! With probabilistic Gantt charts, you can see the likelihood of various durations and finish dates for tasks and entire projects. Plus, with critical indices, you can easily locate the tasks that matter most to your project’s critical path. Learn more about ScheduleRisksAnalysis.

Experience @RISK and DecisionTools Suite

To see how @RISK and DecisionTools Suite can help your business, request a free demo or download our free trial today.

Rotman School of Management Students Learn to Make Key Financial Decisions Using Monte Carlo Simulation

Asher Drory of the University of Toronto’s Rotman School of Management uses @RISK in his graduate-level Financial Management course.

Understanding how to use Monte Carlo simulation to account for risk in decision-making is quickly becoming a required skill for today’s business leaders, says Asher Drory, Adjunct Professor of Finance at University of Toronto’s Rotman School of Management.

“Many leading corporations are now using Monte Carlo simulation in their business cases,” Professor Drory says. “Students who want a leg up with such corporations should seek out all opportunities to get experience in working with Monte Carlo simulation.”

In his Financial Management course, Drory uses @RISK to teach some 200 graduate students each year how to use Monte Carlo simulation in analyzing working capital and capital budgeting decisions. Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities that will occur for any choice of action.

For example, Drory’s classes use @RISK and Monte Carlo simulation to look at:

*How forecasts of financial statements are needed to determine future funding requirements in working capital decisions. *How forecasts of future free cash flows are required and risk must be assessed in capital budgeting analysis.

"All key financial decisions such as investing, operating and financing decisions can benefit from Monte Carlo simulation."Asher Drory
Rotman School of Management, University of Toronto

Separately, Drory and his students use Palisade’s PrecisionTree software in modeling decision tree analysis for new product development. The students have access to the entire DecisionTools Suite which is loaded on all of the computers in the Rotman Finance Laboratory.

“All key financial decisions such as investing, operating and financing decisions can benefit from Monte Carlo simulation,” says Prof. Drory, who has taught at the University of Toronto for 21 years. “I ran across @RISK about 5 years ago when I was looking for PC-based Monte Carlo simulation tools. @RISK has a straightforward and easy-to-use interface.”

Thales uses @RISK to Measure Feasibility of New Business Tenders

Thales uses @RISK to quantify complex markets in of mission-critical electronic information systems for aerospace, defence, and security around the world.

Thales is a leading provider of mission-critical electronic information systems for aerospace, defence, and security markets around the world. With operations in 60 countries and 70,000 employees, it develops products for dual markets in recognition that civil and military systems benefit from many of the same technologies and innovations.

Risks and Challenges Addressed with @RISK

Thales operates in a highly competitive environment, with technologically advanced countries continuously providing tough opposition when it tenders for contracts. It is imperative therefore that Thales keeps up with the relentless pace of technology development—something made additionally challenging by the sophistication of the products in question. In addition, the critical and sensitive nature of its customers’ businesses demand that the equipment Thales develops and produces is rugged, robust, and failsafe.

Bringing products of this calibre to market is costly both in terms of time and resource. Consequently, for every opportunity to compete for new business (which in itself can be a long, and therefore expensive, process), Thales must be confident that it has a reasonable chance of success. It must also take into consideration that, while winning a contract is favourable, the long-term cost of then maintaining its market share can actually be prohibitive to pitching for the prospect in the first place. For example, once developed, sophisticated electronic systems for aircraft require continual upgrading—the cost of which may outweigh the original benefit (and profit projections) of the initial deal.

Thales uses @RISK software from Palisade to assist it in making these business-critical decisions. @RISK is an Excel add-in using Monte Carlo analysis to show all potential scenarios, as well as the likelihood that each will occur. @RISK enables Thales to calculate the competitiveness of complex markets, measure probabilities for project costs, quantify rate of return, and even account for the effects of cumulative business, thereby providing decision-makers with the most complete picture possible.

@RISK Quantifies Complex Markets

For example, Thales might need to decide whether to bid for developing a radar system for a particular type of combat aircraft. By developing a distribution model with @RISK, Thales can input unknown quantities such as production forecasts for that aircraft and account for them with distribution functions. Thales also inputs ‘known’ quantities, such as which countries and organisations are currently flying the same machine, and is then able to forecast the potential market for the product once it is completed. On the civil aviation front, the 25-year lifespan of aeroplanes offers the potential for several equipment upgrades during that time. For example, in-flight entertainment systems have changed dramatically over the past quarter of a century, with personalised systems that allow passengers to watch their individual choice of film now providing airlines with competitive advantage when selling tickets. Again, combining @RISK’s sophisticated prediction capability with human expertise and research enables Thales to ascertain whether the potential market is sufficient to warrant competing for a contract.

@RISK Weighs Up Project Cost vs. Probability

With the market potential ‘measured’, Thales needs to balance the overall cost for bringing the completed project to market against the probability of winning the business. Figures fed into the @RISK model include costs for staffing, new office openings, licensing, etc, as well as the adjustments needed for different countries and regions, such as providing instructions in different languages. Thales must also include a sum for ‘unknown’ or ‘unexpected’ costs.

To then calculate the probability of breaking even on a project, Thales must factor in the cost of preparing a tender with the probability of winning the business. Although bidding against three other operations could be perceived as offering a 25 percent chance of success, Thales believes it makes better business sense to err on the side of caution when making this estimation.

"@RISK combines its powerful risk analysis capability with the added benefit of being easy and intuitive to use, and applicable for almost any task. As a result it is a key strategic tool for Thales, assisting us in our process of reaching informed business-critical decisions."Adam Ogilvie-Smith
Senior Consultant, Thales Management Consultancy

@RISK Measures Rate of Return

Adam Ogilvie-Smith, Senior Consultant at Thales Management Consultancy, explains: “In theory it would be tempting to try and win every piece of business that we are offered. But in the sophisticated and complex markets in which we operate, closer inspection of each case reveals this often is not possible, practical, or desirable. @RISK’s complex risk analysis capabilities provide us initially with a figure that is essentially an internal rate of return on the funds used to tender for and win a project—that is, will financing a business pitch and the subsequent product development bring an equivalent rate of return as investing the same amount of capital in a bank over the same period of time? In addition, the @RISK model helps us to determine the probability of securing the contract, and therefore whether it is strategically viable overall to pitch for the business.”

@RISK Accounts for Cumulative Business

Commercial agreements rarely occur in isolation. Thales’ @RISK decision models therefore ascend to another level by taking into account the cumulative effect of winning new business, for example including conditions such as winning project A being a prerequisite for pitching for project B. It also enables Thales to work with scenarios such as the initial probability of securing contracts in countries X and Y being factored as 40 percent and 30 percent respectively, but winning the contract in country X then making it easier to succeed in the next territory—country Y.

Taking this one step further, Thales can also calculate when it makes commercial sense to use this tactic strategically—for example, will winning a piece of business in one region be beneficial when tendering for another contract? @RISK helps Thales understand whether securing a contract with one region will be a route in to doing business in another.

@RISK Enables Business-Critical Decisions

Ogilvie-Smith concludes: “Our commercial success is determined by our ability to quantify both our internal operations, as well our position in the wider environment in which we operate—which is both dynamic and sensitive. It is essential therefore that we use a versatile and robust modelling tool that can handle the complexity of our queries. @RISK combines its powerful risk analysis capability with the added benefit of being easy and intuitive to use, and applicable for almost any task. As a result it is a key strategic tool for Thales, assisting us in our process of reaching informed business-critical decisions.”

DecisionTools Optimizes Precious Metal Refining

Metallurgical giant Met-Mex Peñoles uses the DecisionTools Suite for Six Sigma Design of Experiments. Because silver and gold are so expensive, process optimization allows analysts to test innovations, avoiding costly trial runs.

DecisionTools Suite in Six Sigma Design of Experiments

Because of the costliness of its raw materials, the metallurgical giant Met-Mex Peñoles, the world’s largest refiner of silver and Mexico’s largest refiner of gold, tries to avoid expensive pilot projects. To cut down on the number of trial runs, the company simulates the refining process by using the DecisionTools Suite in Six Sigma Design of Experiments. This allows the company to work on process optimization and sacrifice a minimum of gold and silver to its experiments.

According to Ignacio Quijas, technology manager for Peñoles, “When you are working with silver and gold, pilot projects to test innovations in the manufacturing process are very costly—and risky. Using @RISK to simulate changes in process design allows us to answer some difficult questions without actually running trials of the process.”

To offer some perspective on the exacting standards Peñoles must meet, Ignacio points out that, for instance, a 100-ounce silver bar must weigh at least 100 ounces—however, the price of the bar does not increase if the bar weighs slightly more than the specification. The additional silver is simply passed along free to the customer and is a production cost.

Each step in the manufacturing processes for gold and silver value-added products creates room for additional error, and the way Peñoles optimizes its process is to reduce the variability of the errors across the manufacturing steps. To build its Six Sigma simulation, Peñoles inputs the physical measurements of the errors and also feeds into the model the specifications and tolerances of its manufacturing equipment, different physical operations, random processing errors, and cost analyses that are pinpoint precise. “We are measuring the amount of gold an silver that turns up when we do every operation and can result in loses,” Ignacio reports.

"When you are working with silver and gold, pilot projects to test innovations in the manufacturing process are very costly—and risky. Using @RISK to simulate changes in process design allows us to answer some difficult questions without actually running trials of the process."Ignacio Quijas
Technology Manager, Met-Mex Peñoles

His primary simulation tool is @RISK. “The functionality gives you so much more vision.” But because of the need for precision in his simulation, Ignacio also makes extensive use of @RISK distribution fitting and TopRank. He likes their capacities for graphic representation, which he uses to explain the intricacies of his simulations to colleagues.

“The ability to communicate aspects of the simulation is important,” he says, “because these very detailed models serve the same function as early trial runs.”

Yes, he said, Peñoles does still rely on pilot projects, but only after DecisionTools has accounted for every speck of silver and gold that might result in the production losses or recycled materials that increase cost.

Guiding Environmental Consultants for Fortune 500 Companies

A consultant with Triangle Economic Research, an Arcadis company, Tim Havranek works with Fortune 500 clients to identify and quantify their potential environmental liabilities and to simulate the least costly routes to meeting their responsibilities.

Large industrial companies operating out of many different locations and facilities often have numerous actual or potential environmental legacies that linger for decades as financial liabilities. Longtime DecisionTools® user Tim Havranek has made a successful career out of helping companies manage their “environmental risk portfolios” cost-effectively. A consultant with Triangle Economic Research, an Arcadis company, Havranek works with Fortune 500 clients to identify and quantify their potential environmental liabilities and to simulate the least costly routes to meeting their responsibilities. Many of the complex cases that he and his associates at TER work on involve hundreds of millions of dollars, multiple stakeholders, and a powerful amount of modeling. As he has for years, Havranek relies on @RISK and PrecisionTree® to compare the scenarios and the decision paths that guide his clients’ decisions.

A Typically Complicated Case

In one recent case, a major industrial manufacturer sold approximately 15 of its active plants to another manufacturer. The terms of these sales included the provision that the original corporate owner would retain responsibility for historical environmental impacts. As time passed, environmental claims against the original corporate owner continued, and the corporation sought appropriate means of reducing cost and risks, such as receiving regulatory closure and/or selling the properties and liabilities to other parties.

Also, the historical environmental impacts at times potentially limited the ways that the new owners could manage and expand the properties. This often led to disagreements. Such disagreements were anticipated during the sale, and the purchase agreement included an arbitration clause to address issues as they arose.

Three Routes to Resolution

The corporation identified three possible solutions:

  1. Pay for the transfer of liabilities to the current owners of the plants (cash out)
  2. Buy back the properties
  3. Continue under the asset purchase agreement and the system of arbitration it provided.

Havranek used Triangle’s time-tested procedure for framing the model. He met with all the stakeholders to identify all known cost elements, inherent uncertainties, and future potential liabilities for each of the three alternatives. The model included more than 100 unknowns. In order to pinpoint those issues on which the company would need to prevail in arbitration, Havranek and his team performed sensitivity analyses on the cost drivers identified by the framing meeting participants. The model was then run using @RISK and PrecisionTree.

"I am always trying to streamline my models. To simplify simulations you need the flexibility that proprietary tools don’t always offer. These tools have that flexibility without any sacrifice of power. @RISK and PrecisionTree have all the power you need."Tim Havranek
Triangle Economic Research

Outside Verification

The model had three output cells, one for each alternative. The outcome was intriguing: the least costly alternative was to stay with the asset purchase agreement and arbitrate as needed. The model indicated an expected value savings of more than $30 million. An outside actuarial group verified and validated the model using proprietary actuarial software. In the end, the actuarial group’s projections agreed not only with Triangle’s inputs and assumptions but also with its findings.

Simplifying the Complex

Although other companies may turn to proprietary software to parse environmental risks, Havranek sees no reason to use custom software to accommodate the many complex inputs he includes in his models. He likes the convenience of working in Excel and being able to share his results with clients. But most important, he says, “I am always trying to streamline my models. To simplify simulations you need the flexibility that proprietary tools don’t always offer. These tools have that flexibility without any sacrifice of power. @RISK and PrecisionTree have all the power you need.”

@RISK Models $50B Acquisition Project by the U.S. Government

The U.S. General Services Administration (GSA) provides contracts to U.S. Agencies for telecommunications and information technology. The newest set of services GSA facilitated are called Enterprise Infrastructure Solutions (EIS). GSA recently completed a project to acquire these services for Agencies’ use for the next 15 years, valuing at $50B. The GSA used @RISK throughout the acquisition project, which was performed on-time and on-budget. @RISK helped GSA executives understand the impacts of their decisions on schedule and cost within the framework of quantitative risk management.


GSA provides contracts to U.S. Agencies for telecommunications and information technology services, with the newest set of contracts being Enterprise Infrastructure Solutions (EIS). EIS includes local, national and international voice and data services, optical services, wireless, satellite, security, cloud services, contact centers, building/campus networks, equipment and professional services.

The Hunt for Quality Contractors

In 2014 GSA began competitively acquiring EIS contracts, with the aim of providing government agencies EIS for up to 15 years for a total award value of $50B. A key goal was to award a robust set of contractors from which agencies could choose their services.

This EIS acquisition project was ambitious and complex, including hundreds of tasks performed over two years by teams of government staff and consultants. Team members included acquisition specialists, contracting officers, engineers, business operations professionals, and financial analysts. Moreover, the project depended on each bidding contractor understanding the requirements and developing responsive proposals.

"Project simulation [with @RISK] enabled us to provide the agencies firm schedules. They could then stage their resources in a timely way to select their EIS vendor to obtain the earliest and greatest possible savings to the taxpayer." GSA EIS Acquisition project manager

@RISK Clarifies Consequences

To help manage the project, the GSA EIS Acquisition project manager and his team used @RISK. “The software helped GSA executives understand the quantitative risk of their decisions impacting both the cost and schedule of the project,” said the program manager.

Using Microsoft Project, the program manager and his team developed the schedule and resource loading in the traditional way and aligned with an Earned Value Management (EVM) approach to project management.

Once the schedule and resource loading was completed, the team imported the project into @RISK. The project management team then worked with the task teams to choose the probability distributions and parameters for each meaningful task. They next correlated the tasks, and then assigned probabilities to branches in the project. The team leadership then created a probabilistic calendar for resource availability.

Finally, the teams developed a thorough risk register of events that could impact schedule, and identified the tasks within the project that could be impacted by the risk events. GSA executives provided the required confidence level.

@RISK performed Monte Carlo simulations, usually with 5000 iterations. “The performance of @RISK was so fast and steady that we were able to perform the simulations in real time with GSA executives,” reported the program manager. “This allowed senior executives to see the Probability Distribution Function (PDF) of a milestone achievement based upon their decisions regarding scheduling tasks and resources, and escalating actions on solutions.”

By July of 2017, GSA had achieved this goal both on time and on budget, making 10 EIS awards.

“The initial @RISK simulations help set realistic expectations for milestone achievement,” explained the program manager, “and periodic simulations helped the team leadership and GSA executives understand where the risks were and provided an opportunity to test alternative mitigation strategies.”

Senior decision makers were also pleased with @RISK. “Project simulation enabled us to provide the agencies firm schedules,” reported one senior executive. “They could then stage their resources in a timely way to select their EIS vendor to obtain the earliest and greatest possible savings to the tax payer.”

Published: Dec. 17, 2021
Updated: Sept. 1, 2023


Manage Uncertainty in Project Schedules 

Start analyzing your schedule risk using Monte Carlo simulation with @RISK's ScheduleRiskAnalysis (SRA). SRA shows you virtually all possible outcomes in your schedule – and tells you how likely each is to occur. Plus, it works within the familiar Excel environment and lets you perform risk modeling on project files created in Primavera P6 and Microsoft Project. 

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