Construction & Engineering
Improving Project Outcomes through Cost and Reliability Analytics
From airports to skyscrapers and rail to levees, Palisade solutions keep projects of all sizes on-track.
From construction and engineering multinationals to consultancies to government agencies, anyone in the construction sector knows that they face more risk than ever before. As an industry operating in global supply chains, ever-changing geopolitical environments, and diverse economies, engineering and construction companies are exposed to a wide range of constantly-changing uncertainties that can negatively impact bottom lines and shareholder value.
Large infrastructure projects suffer from significant undermanagement of risk in practically all stages of the value chain and throughout the life cycle of a project. In our view, most overruns are foreseeable and avoidable. Many of the problems we observe are due to a lack of professional, forward-looking risk management.”
McKinsey & Company
“A risk-management approach to a successful infrastructure project”
@RISK and DecisionTools Suite for Construction & Engineering
A majority of leading companies in the construction and engineering sector rely on @RISK and DecisionTools Suite software solutions to meet these challenges head-on, all from the familiar spreadsheet environment. @RISK and the DecisionTools Suite bring robust analytics such as Monte Carlo simulation, optimization, decision trees, sensitivity analysis, and predictive data analysis to Microsoft Excel. This enables users of all levels to contribute to better decision-making – with or without data, and with or without a statistics background.
How Construction and Engineering Leaders Use Palisade
Get an accurate, probabilistic estimate of materials and labor costs throughout the entire project. Understand the likelihood of going over budget under a variety of scenarios.
Products: @RISK, TopRank, StatTools, NeuralTools
Identify high-risk areas, prepare actionable plans, and set aside contingency budgets so your project can adapt when the unexpected happens. Optimize resources so you don’t hold up some projects while overbudgeting for contingency on others.
Products: @RISK, TopRank, PrecisionTree, RISKOptimizer, Evolver
Gain insight into confidence levels around the risk of equipment failure or safety violations. Identify areas of weakness to ensure quality results and prevent failures.
Products: @RISK, TopRank, StatTools, NeuralTools
Mitigate exposure to commodity price volatility through an optimized options and futures strategy that accounts for uncertainty.
Products: @RISK, TopRank, PrecisionTree, RISKOptimizer, Evolver, NeuralTools
From optimizing funds across capital projects to regulatory compliance and everything in between, Palisade solutions are used to address a wide range of construction and engineering challenges.
Includes @RISK and all products mentioned above
Monte Carlo simulation allowed us to determine the probability of performance with a known level of confidence. @RISK provided all the tools to accomplish the Monte Carlo simulation and communicate the results in a manner that could be easily understood.”
Customer Success Stories
Kevin Bordelon
Senior Director of Operations, Zachry Industrial
The methods for obtaining confidence levels around the probability values are inconceivable without @RISK, and to accept the new analysis methods you have to have confidence in the results.
Kim Kozak
Senior Project Manager, Patrick Engineering
The final results of Palisade’s @RISK models help us, and our clients, understand where projects could go – not where they will go. And we know that if a risk does occur, we’re well prepared as we’ve already identified it.
Sean de la Rosa
Project and Enterprise Risk Manager, Murray & Roberts
@RISK offers the powerful simulation capability that is required to model complex risk problems. Despite this, the simulations are relatively easy to perform and require minimal in-depth mathematical expertise.
John Zhao
Risk Analyst, Riskcore Ltd / Kinder Morgan
In a megaproject, risks should not only be identified through a qualitative process but also quantified via a stochastic quantitative process. @RISK easily solved the problem by offering a variety of risk distribution functions for simulating a large number of key variables simultaneously.