Palisade Decision Tools Suite 5.7 23
Nadia Grubb
DecisionTools Suite is software for decision management and risk analysis in business and operational projects, developed by Palisade. No matter what industry you work in, this software helps you make confident decisions. Using DecisionTools Suite, you can assess and determine the risk of the project and optimize your decisions. This program has tools for Excel and Microsoft Project and allows you to perform analysis within the spreadsheet.
DecisionTools Suite plays an important role in increasing the quality of decision making in projects and helps teams to think more clearly, act decisively, and make decisions more easily. The software has various tools for simulating the Monte Carlo method, forecasting and statistical analysis, decision trees, predictable neural networks, advanced optimization, and information retrieval and information mapping. You can also use this software for financial and cash flow analysis , multi-step decision modeling, resource optimization, and company risk management.
This new feature to export to PDF and print SRA Gantt Charts enables customization of Standard and Probabilistic Gantt Chart exports, including project dates, scales, and industry standards like paper sizes, page margins, orientation, and scale.
The new @RISK ScheduleRiskAnalysis allows you to define schedule risk models in Excel for project files created in Primavera P6 or Microsoft Project, by applying all the power and flexibility of @RISK Monte Carlo simulation.
Please Note: Spanish documentation was partially completed as of the release date. The product will automatically start delivering Spanish documentation content as soon as it becomes available online.
Now when testing or predicting PN/GRN nets there are also warnings for the number of test and prediction cases whose values are not between the minimum and maximum of the corresponding training variables. These warnings can be found in the Preview dialogs, Reports and as a note in the Live Prediction cell for numeric independent variables.
This book takes a pragmatic business and economics view towards evaluating competing investment alternatives and/or capital project strategies. It provides a practical step-by- step process using a structured decision analysis framework to evaluate, understand, quantify, and measure project invesment strategies in light of multiple stakeholder objectives and success criteria. This process assists in helping stakeholders (internal and external) achieve a shared understanding of project issues and to facilitate convergence towards a mutually acceptable solution. The approach considers available choices, identified uncertainties, constraints, necessary tradeoffs, and preferences so as to identify solutions that maximize overall benefits while minimizing overall costs and risk. A real world case study is presented in the early chapters and the process steps are demonstrated through application to this case study.
Recent advances in technology allow for investment strategies to be evaluated against multiple criteria within one integrated platform. This book guides the reader in performing multi-criteria decision analysis, including the use of Monte Carlo simulation, within an MS Excel environment using native MS Excel and as well as add-in programs such Palisades Decision Tools suite. Example model structures, screen shots, formulas, and output results are provided throughout the book using an illustrative case study.
- Written from the perspective of a practitioner for the benefit of practitioners
- Includes the use of Monte Carlo simulation, within an MS Excel environment as well as add-in programs such Palisades Decision Tools suite.
The Climate Risk and Decisions Group in the Western Water Assessment works to understand how weather and climate data can aid social responses to weather and climate variability. We build adaptation simulation models both as a research approach and as tools to help decision-makers use climate and other environmental data for more effective response to changing conditions.
Risk and decision analysis is a mostly normative set of methods for making choices under uncertainty. We apply decision analysis as a research tool by giving more weight to studying the decision context and structure at the front end, and post-hoc, forensic decision analysis at the back end, as illustrated here, to test theories and research hypotheses. Our approach aims to create realistic decision models that can be simulated under historical and projected conditions, subjected to extensive sensitivity analysis, and that can be used and revised by actual decision makers. We are developing a set of decision models aimed at understanding, and simulating, the decision process for response to climate variation among resource managers like farmers, ranchers, flood managers, and others.
Besides gaining new insights into the process of climate adaptation, our challenge is to simulate the decision process so that researchers and managers can examine the implications of multiple factors and decisions, and thus get a feel for how sensitive choices are to uncertain conditions. Decision analysis as a learning tool can:
The test-bed is comprised of a suite of decision models designed to simulate climate impacts in agriculture and infrastructure and allow testing of various responses using principles of decision analysis such as maximum expected utility, decision scaling and sensitivity analysis, game theory, and options analysis. The test-bed includes working models (FarmAdap and StreamTemp) available for download from this webpage. Models under development include a simulation of rancher decision-making during drought and a pathways model to judge the performance of flood control and stormwater systems under a changing climate.
FarmAdap simulates costs, production, and net income for a 2,000 acre drylandwheat farm on the U.S. Great Plains, with the goal of modeling the impacts of climate variation, especially extreme events and rapid climate change, and the timing of farmer adaptation. Climate change is input via off-sets to the yield distribution from which the farm draws each year in a 30 year simulation. The model is calibrated on actual yield data for central North Dakota wheat production, plus a set of scenarios of gradual or rapid change as well as extreme events. Yield data could be input from, for example, a historical time series of a particular farm, county or climate division, or future yields simulated by a combination of climate scenario and crop yield modeling.
The model is designed in two formats. The DynamicFarmAdap version simulates change over time so that the farmer might chose to adapt at any time given changing conditions and some rules about when to adapt.
You can run a simple version of DynamicFarmAdap on Analytica's "Cloud Player". We have loaded the cost, yield, and price data, and if you simply run the model (by clicking either of the "Calc" buttons) you will get results for all the scenarios and be able to scroll through "slices" to see all the output. We have given the farmer the choice of switching methods from continuous cropping (common in the region) to an alternate fallow system (common further west in drier areas, and generally producing a slightly higher yield) if the continuous cropping yields decline too much. You can set the income threshold for this switch in the user interface. We start at $-50,000 for reasons described in the accompanying paper. Results then give you the future incomes for the non-adaptive (stays with continuous cropping) and the adaptive (switches) farmer. You can view the details of the model by clicking on the "Model" node in the center.
You can also download the dynamic model here and change inputs and run it (even without access to the full software), by downloading the Analytica Free 101 player. Once you have down-loaded the free Analytica player and downloaded the model from this site, you can run it and explore the results in more detail, and you can change some of the parameters for custom simulations.
NPVFarmAdap is not a dynamic simulation as with the Analytica version described above, instead it calculates the net present value (NPV) of different cropping strategies for each climate scenario (see the worksheet tabs) over a 30-year simulation period. This spreadsheet model was developed in the @Riskdecision tool from Palisade Software where you can download a trial version of the DecisionTools Suite add-in for Excel.
The CropSwitch model suite uses actual crop production budgets to calculate the relative performance of alternative crops; the version here was designed to test the likely emergence of winter wheat under a warming climate in the spring wheat production region of North Dakota. The three models in the suite include: Crop Switch Base which calculates of outcomes for spring wheat, spring wheat fallowed, and winter wheat, for a base year (production data for 2014, as shown in the model inputs data table to the right). A decision tree is appended below the @Risk model to allow for two-way sensitivity analysis, a way to test for statistical dominance of a decision over a range of factors, in this case the yield benefit of a switch and the differential costs of insurance (for runs including crop insurance). The model incorporates a risk register that forces complete crop loss into the simulation runs at a specified binomial probability to simulate winter kill for winter wheat and drought loss for spring wheat. It also includes a crop insurance calculator in which winter wheat coverage, not currently available, is simulated to emerge in the future. Crop Switch Risk Tree calculates risk tolerance via an exponential utility function built into the tree. The tree can be set to calculate, for a given R value (set in cell B4): (1) Expected Value (EV); (2) Expected Utility (EU); or (3) Certainty Equivalent (CE). Tables 1, 2 and 3 calculate a risk premium. Crop Switch Time allows calculation of outcomes for spring wheat and winter wheat over a 30-year scenario or "Representative Agricultural Pathway" (RAP). It compares net present values (NPV) of crop switching at different points in the simulation, with winter kill reduced slowly or quickly (e.g., slow or fast climate warming), and with the "emergence" of a insurance instrument at a point in the time series determined by a probability of winter kill set by the user.
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