Project Economics Analysis Tool (PEAT)<\/strong><\/p>\n The remaining sections illustrate how the IRM process can be run seamlessly to provide \u00a0a\u00a0comprehensive risk\u2010based decision analytics framework through the use of the Project Economics\u00a0Analysis Tool (PEAT) software. PEAT has over 20 related registered patents and patent\u2010pending\u00a0technologies and incorporates all the elements of IRM in a simple\u2010to\u2010use software. See the About the\u00a0DVD for more details on obtaining a trial version of the software and see the actual DVD for additional\u00a0getting started videos and PEAT documentation. Figure 2 illustrates the PEAT software utility. This software was designed to apply IRM\u00a0methodologies (Monte Carlo risk simulation, strategic real options, stochastic and predictive forecasting,\u00a0business analytics, business statistics, business intelligence, decision analysis, and portfolio\u00a0optimization) to project and portfolio economics and financial analysis. The PEAT utility can house\u00a0multiple industry\u2010specific or application\u2010specific modules such as oil and gas industry models (industry\u00a0specific) or discounted cash flow model (application specific). The utility can house multiple additional\u00a0types of models (industry or application specific) as required. The user can choose the module desired and create a new model from scratch, open a previously saved model, load a predefined example, or exit\u00a0the utility. As shown in Figure 2, multiple modules exist in PEAT, and you can see Chapter 16 for PEAT\u2019s\u00a0Enterprise Risk Management module and Technical Note 6 in Dr. Johnathan Mun\u2019s Modeling Risk, Third\u00a0Edition, for an example of how the Dynamic Project Management module works. There are some\u00a0proprietary modules that were customized for companies and will not be discussed (e.g., Saudi Aramco,\u00a0Northrop Grumman, Cubic, Arco, 3M, and others). For illustrative purposes, we focus on the Discounted\u00a0Cash Flow (DCF) module. For the reader to follow along the analysis, select the DCF module and click the\u00a0Load Example to obtain a pre\u2010generated model and example<\/p>\n <\/p>\n <\/p>\n Figure 3 illustrates the main PEAT software where its menu items are fairly straightforward (e.g.,\u00a0File | New or File | Save items that are self\u2010explanatory). The software is also arranged in a tabular\u00a0format. There are three tab levels in the software, and a user would proceed from top to bottom and left\u00a0to right when running analyses in the utility. For instance, a user would start with the Discounted Cash\u00a0Flow (Level 1) tab, select Custom Calculations or Option 1 in (Level 2), and begin entering inputs in the 1.\u00a0Discounted Cash Flow (Level 3) tab. The user would then proceed to the 2. Cash Flow Ratios (Level 3),\u00a0and then on to the 3. Economic Results (Level 3) subtab, and so forth. When the lowest level subtabs are\u00a0completed, the user would proceed up one level and continue with Option 2 (Level 2), Option 3 (Level 2),\u00a0Portfolio Analysis (Level 2), and so forth. When these tabs at Level 2 are completed, the user would\u00a0continue up a level to the Applied Analytics (Level 1) tab, and proceed in the same fashion. All Level 1\u00a0tabs are identical regardless of the module (Discounted Cash Flow Model or Oil and Gas Model, etc.)chosen as illustrated in Figure 2, except for the first tab (Discounted Cash Flow).<\/p>\n <\/p>\n Discounted Cash Flow Model<\/strong><\/p>\n The Discounted Cash Flow section shown in Figure3 is at the heart of the analysis\u2019 input \u00a0assumptions.\u00a0Users would enter their input assumptions\u2014such as starting and ending years of the analysis, the\u00a0discount rate to use, and the marginal tax rate\u2014and set up the project economics model (adding or\u00a0deleting rows in each subcategory of the financial model). Additional time\u2010series inputs are entered in\u00a0the data grid as required, while some elements of this grid are intermediate computed values. The entire\u00a0grid can be copied and pasted into another software application such as Microsoft Excel, Microsoft\u00a0Word, or other third\u2010party software applications, or can be viewed in its entirety as a full screen pop\u2010up. Users can also identify and create the various options, and compute the economic and financial\u00a0results such as net present value (NPV), internal rate of return (IRR), modified internal rate of return\u00a0(MIRR), profitability index (PI), return on investment (ROI), payback period (PP), and discounted\u00a0payback (DPP). This section will also auto\u2010generate various charts, cash flow ratios and models,\u00a0intermediate calculations, and comparisons of the options within a portfolio view, as illustrated in the\u00a0next few figures. As a side note, the term options is used in PEAT\u2019s DCF module to represent a generic\u00a0analysis option, where each option can be a different asset, project, acquisition, investment, research\u00a0and development, or simply variations of the same investment (e.g., different financing methods when\u00a0acquiring the same firm, different market conditions and outcomes, or different scenarios or\u00a0implementation paths). Therefore, instead of naming these tabs projects, which may denote a more\u00a0restrictive view, the more flexible terminology of option is adopted instead.<\/p>\n Procedure<\/strong><\/p>\n When the user is in any of the Options tabs, the Options menu will become available, ready\u00a0for users to add, delete, duplicate, or rename an option or rearrange the order of the Option\u00a0tabs by first clicking on any Option tab and then selecting Add, Delete, Duplicate, Rearrange,\u00a0or Rename from the menu. All of the required functionalities such as input assumptions,\u00a0adding or reducing the number of rows , selecting discount rate type, or copying the grid are\u00a0available within each Option tab. The input assumptions entered in these individual Option\u00a0tabs are localized and used only within each tab, whereas input assumptions entered in the\u00a0Global Settings (to be discussed later) tab apply to all Option tabs simultaneously. Typically,\u00a0the user will need to enter all the required inputs, and if certain cells are irrelevant, users\u00a0will have to enter zeros. Users can also increase or decrease the number of rows for each\u00a0category as required. The DCF Starting Year input is the discounting base year, where all\u00a0cash flows will be present valued to this year. The main categories are in boldface, and the\u00a0input boxes under the categories are for entering in the line item name\/label. Users can click\u00a0on Copy Grid to copy the results into the Microsoft Windows clipboard in order to paste into\u00a0another software application such as Microsoft Excel or Microsoft Word. Finally, the Full\u00a0Screen button pops up the data grid to a full screen to facilitate the viewing of the model in\u00a0its entirety without the need to scroll to the left\/right or up\/down. The grid will be\u00a0maximized to full view, which facilitates the taking of screen shots. Figure 4 illustrates the Cash Flow Ratios that are automatically calculated. This tab is where\u00a0additional balance sheet data can be entered (current asset, shares outstanding, common equity, total\u00a0debt, etc.) and the relevant financial ratios will be computed (EBIT, Net Income, Net Cash Flow,\u00a0Operating Cash Flow, Economic Value Added, Return on Invested Capital, Net Profit Margin, etc.).\u00a0Computed results or intermediate calculations are shown as data grids. Data grid rows are color coded\u00a0by alternate rows for easy viewing. Users can enter the input assumptions as best they can, or they can\u00a0guess at some of these figures to get started. The inputs entered in this Cash Flow Ratios subtab will be\u00a0used only in this subtab\u2019s balance sheet ratios. The results grid shows the time series of cash flow\u00a0analysis for the option for multiple years. These are the cash flows used to compute the NPV, IRR, MIRR,\u00a0and so forth.<\/p>\n <\/p>\n Figure 5 illustrates the Economic Results of each option. This Level 3 subtab shows the results from\u00a0the chosen option and returns the NPV, IRR, MIRR, PI, ROI, PP, and DPP. These computed results are\u00a0based on the user\u2019s selection of the discounting convention, if there is a constant terminal growth rate,\u00a0and the cash flow to use (e.g., net cash flow versus net income or operating cash flow). An NPV Profile table and chart are also provided, where different discount rates and their respective NPV results are\u00a0shown and charted. Users can change the range of the discount rates to show\/compute by entering the\u00a0From\/To percent and clicking on Update, copy the results, and copy the profile chart, as well as use any\u00a0of the chart icons to manipulate the chart\u2019s look and feel (e.g., change the chart\u2019s line\/background color, chart type, chart view, or add\/remove gridlines, show\/hide labels, and show\/hide legend). Users can\u00a0also change the variable to display in the chart. For instance, users can change the chart from displaying\u00a0the NPV profile to the time\u2010series charts of net cash flows, taxable income, operating cash flows,\u00a0cumulative final cash flows, present value of the final cash flows, and so forth. Users can then click on the Copy Results or Copy Chart buttons to take a screen shot of the modified chart that can then be pasted\u00a0into another software application such as Microsoft Excel or Microsoft PowerPoint. The Economic Results subtabs are for each individual option, whereas the Portfolio Analysis tab\u00a0(which is shown later as Figure7) compares the economic results of all options at once. The Terminal\u00a0Value Annualized Growth Rate is applied to the last year\u2019s cash flow to account for a perpetual constant\u00a0growth rate cash flow model, and these future cash flows, depending on which cash flow type chosen,\u00a0are discounted back to the base year and added to the NPV to arrive at the perpetual valuation.<\/p>\n <\/p>\n Figure 6 illustrates the Information and Details tab. Users would select this tab for entering justifications for the input assumptions used as well as any notes on each of the options. For numerical calculations and notes, use the Custom calculation tab (discussed later; see Figure 8) instead.Users can also change the labels and Categories of the Information and Details tab by clicking on Categories and editing the default labels. The formatting of entered text can be performed in the Description box by clicking on the various text formatting icons.<\/p>\n <\/p>\n Static Portfolio Analysis and Comparisons of Multiple Projects<\/strong><\/p>\n Figure 7 illustrates the Portfolio Analysis of multiple Options. This Portfolio Analysis tab returns the\u00a0computed economic and financial indicators such as NPV, IRR, MIRR, PI, ROI, PP, and DPP for all the\u00a0options combined into a portfolio view (these results can be stand\u2010alone with no base case or computed\u00a0as incremental values above and beyond the chosen base case). The Economic Results (Level3) subtabs show the individual option\u2019s economic and financial indicators, whereas this Level2 Portfolio Analysis\u00a0view shows the results of all options\u2019 indicators and compares them side by side. There are also two\u00a0charts available for comparing these individual options\u2019 results. The Portfolio Analysis tab is used to\u00a0obtain a side\u2010by\u2010side comparison of all the main economic and financial indicators of all the options at\u00a0once. For instance, users can compare all the NPVs from each option in a single results grid. The bubble\u00a0chart on the left provides a visual representation of up to three chosen variables at once (e.g., the y\u2010axis\u00a0shows the IRR, the x\u2010axis represents the NPV, and the size of the bubble may represent the capital\u00a0investment; in such a situation, one would prefer a smaller bubble that is in the top right quadrant of the chart). These charts have associated icons that can be used to modify their settings (chart type, color,\u00a0legend, etc.).<\/p>\n <\/p>\n Custom Modeling<\/strong><\/p>\n Figure 8 illustrates the Custom calculation tab also available for making the user\u2019s own custom\u00a0calculations just as they would in a Microsoft Excel spreadsheet. Clicking on the Function F(x) button will\u00a0provide users with a list of the supported functions that can be used in this tab. A manual Update\u00a0Calculations can be clicked to update the worksheet\u2019s calculations. Other basic mathematical functions\u00a0are also supported, such as =, +, \u2010, \/, *, ^. If users use this Custom calculations tab and wish to link some\u00a0cells to the other option input tabs (e.g., Option 1), they can select the cells in the Custom calculations\u00a0tab, right\u2010click, and select Link To. Then they would proceed to the location in the option tabs and\u00a0highlight the location of the input cells they wish to link to, right\u2010click, and select Link From. Any\u00a0subsequent changes users make in the Custom calculations tab will be updated in the linked input\u00a0assumption cells.<\/p>\n Procedures<\/strong><\/p>\n The following are some additional procedural notes on using the Custom tab:<\/p>\n As an illustrative example, in the Custom calculations tab, a user may enter the following: 1, 2, 3 into\u00a0cells A1, B1, C1, respectively. Then in cell D1, enter =A1+B1+C1 and click on any other cell and it will\u00a0update the cell and return the value 6. Similarly, users type in =SUM(A1:C1) to obtain the same results. A\u00a0list of preset functions can be seen by clicking on the F(x) the button. As another example, in the Custom calculations tab, a user may enter the following: 1, 2, 3 into cells\u00a0A1, B1, C1, respectively. Then, select these three cells, right\u2010click, and select Link To. Proceed to any one\u00a0of the option tabs, and in the Discounted Cash Flow or Input Assumptions subtabs, select three cells\u00a0across (e.g., on the Revenue line item), right\u2010click, and select Link From. The values of cells A1, B1, C1 in\u00a0the Custom Calculations tab will be linked to this location. Users can go back to the Custom Calculations\u00a0tab to change the values in the original three cells and they will see the linked cells in the Discounted\u00a0Cash Flow or Input Assumptions subtabs change and update to reflect the new values.<\/p>\n <\/p>\n Weighted Average Cost of Capital<\/strong><\/p>\n Figure 9 illustrates the Discount Rate tab where the Weighted Average Cost of Capital, or WACC,\u00a0calculations are housed. This is an optional set of analytics whereby users can compute the firm\u2019s WACC\u00a0to use as a discount rate. Users start by selecting either the Simple WACC or Detailed WACC Cost\u00a0Elements. Then, they can either enter the required inputs or click on the Load Example button to load a\u00a0sample set of inputs that can then be used as a guide to entering their own set of assumptions and\u00a0additional settings. Figure 10 illustrates the Beta calculations. This is another optional subtab used for computing the\u00a0beta risk coefficient by pasting in historical stock prices or stock returns to compute the beta, and a\u00a0time\u2010series chart provides a visual for the data entered. The resulting beta is used in the Capital Asset\u00a0Pricing Model (CAPM), one of the main inputs into the WACC model. Users start by selecting whether\u00a0they have historical Stock Prices or Stock Returns, then enter the number of rows or periods of historical\u00a0data they have, and paste the data into the relevant columns and click Compute. Users can also click on\u00a0Load Example to open a sample data set. The beta coefficient result will update and users can use this\u00a0beta as an input into the WACC model.<\/p>\n <\/p>\n <\/p>\n Applied Analytics: Tornado and Scenario<\/strong><\/p>\n Figure 11 illustrates the Applied Analytics section, which allows users to run Tornado Analysis and\u00a0Scenario Analysis on any one of the options previously modeled\u2013\u2013this analytics tab is on Level 1, which\u00a0means it covers all of the various options on Level 2. Users can, therefore, run tornado or scenario\u00a0analyses on any one of the options. Tornado analysis, as we already know, is a static sensitivity analysis\u00a0of the selected model\u2019s output to each input assumption, performed one at a time, and ranked from most\u00a0impactful to the least. Users start the analysis by first choosing the output variable to test from the\u00a0droplist.<\/p>\n Procedure<\/strong><\/p>\n Users can change the default sensitivity settings of each input assumption to test and decide\u00a0how many input variables to chart (large models with many inputs may generate unsightlyand less useful charts, whereas showing just the top variables reveals more information through a more elegant chart). Users can also choose to run the input assumptions as unique\u00a0inputs, group them as a line item (all individual inputs on a single line item are assumed to\u00a0be one variable), or run as variable groups (e.g., all line items under Revenue will be assumed to be a single variable). Users will need to remember to click Compute to update\u00a0the analysis if they make any changes to any of the settings. The sensitivity results are also\u00a0shown as a table grid at the bottom of the screen (e.g., the initial base value of the chosen output variable, the input assumption changes, and the resulting output variable\u2019s\u00a0sensitivity results). The following summarizes the tornado analysis chart\u2019s main\u00a0characteristics:<\/p>\n <\/p>\n Figure 12 illustrates the Scenario Analysis tab, where the scenario analysis can be easily performed\u00a0through a two\u2010step process: identify the model input settings and run the model to obtain scenario\u00a0output tables. In the Scenario Input Settings subtab, users start by selecting the output variable they wish\u00a0to test from the droplist. Then, based on the selection, the precedents of the output will be listed under\u00a0two categories (Line Item, which will change all input assumptions in the entire line item in the model\u00a0simultaneously, and Single Item, which will change individual input assumption items). Users select one\u00a0or two checkboxes at a time and the inputs they wish to run scenarios on, and enter the plus\/minus\u00a0percentage and the number of steps between these two values to test. Users can also add color coding of\u00a0sweetspots or hotspots in the scenario analysis (values falling within different ranges have unique\u00a0colors). Users can create multiple scenarios and Save As each one (enter a name and model notes for\u00a0each saved scenario).<\/p>\n <\/p>\n Figure 13 illustrates the Scenario Output Tables to run the saved Scenario Analysis models. Users\u00a0click on the droplist to select the previously saved scenarios to Update and run. The selected scenario\u00a0table complete with sweetspot\/hotspot color coding will be generated. Decimals can be increased or\u00a0decreased as required, and users can Copy Grid or View Full Grid as needed. The following are some\u00a0notes on using the scenario analysis methodology:<\/p>\n <\/p>\n Monte Carlo Risk Simulation<\/strong><\/p>\n Figure 14 illustrates the Risk Simulation section, where Monte Carlo risk simulations can be set up and\u00a0run. Users can set up probability distribution assumptions on any combinations of inputs, run a risk\u00a0simulation tens to hundreds of thousands of trials, and retrieve the simulated forecast outputs as charts,\u00a0statistics, probabilities, and confidence intervals in order to develop comprehensive risk profiles of the options.<\/p>\n Procedure<\/strong><\/p>\n In the Set Input Assumptions subtab, users start the simulation analysis by first setting simulation distributional inputs here (assumptions can be set to individual input assumptions in\u00a0the model or as an entire line item). Users click on and choose one option at a time to list the available input assumptions. Users then click on the probability distribution icon under the\u00a0Settings header for the relevant input assumption row, select the probability distribution to use,\u00a0and enter the relevant input parameters. Users continue setting as many simulation inputs as required (users can check\/uncheck the inputs to simulate). Users then enter the simulation\u00a0trials to run (it is suggested to start with 1,000 as initial test runs and use 10,000 for the final\u00a0run as a rule of thumb for most models). Users can also Save As the model (remembering to provide it a name). Then they click on Run Simulation. Finally, in this tab, users can set simulation assumptions across multiple options and Simulate All Options at Once, apply a Seed Value to replicate the exact simulation results each time it is run, and Edit or Delete a previously saved simulation model. Although the software supports up to 50 probability distributions, in general, the most commonly used and applied distributions include Triangular, Normal, and Uniform. If the user has historical data available, he or she can use the Forecast Prediction (discussed in a later section) tab to perform a Distributional Fitting to determine the best\u2010fitting distribution to use as well as to estimate the selected distribution\u2019s input parameters. Multiple simulation settings can be saved such that they can be retrieved, edited, and modified as required in the future. Users can select either Simulate All Options at Once or Simulate Selected Option Only, depending on whether they wish to run a risk simulation on all the options that have predefined simulation assumptions or to run a simulation only on the current option that is selected.<\/p>\n <\/p>\n Simulation Results, Confidence Intervals, and Probabilities<\/strong><\/p>\n Figure 15 illustrates the Risk Simulation Results subtab. After the simulation completes its run, the utility will automatically take the user to the Simulation Results tab. The user selects the output variable to display using the droplist. The simulation forecast chart is shown on the left, while percentiles and simulation statistics are presented on the right.<\/p>\n Procedure<\/strong><\/p>\n Users can change and update the chart type (e.g., PDF, CDF), enter Percentiles (in %) or Certainty Values (in output units) on the bottom left of the screen (remembering to click Update when done) to show their vertical lines on the chart, or compute\/show the Percentiles\/Confidence levels on the bottom right of the screen (selecting the type: Two Tail, Left Tail, Right Tail, then either entering the percentile values to auto\u2010compute the confidence interval or entering the confidence desired to obtain the relevant percentiles). Users can also Save the simulated results and Open them at a later session, Extract Simulation Data to paste into Microsoft Excel for additional analysis, modify the chart using the chart icons, and so forth. The simulation forecast chart is highly flexible in the sense that users can modify its look and feel (color, chart type, background,\u00a0gridlines, rotation, chart view, data labels, etc.) using the chart icons. If users entered either a percentile or certainty value at the bottom left of the screen\u00a0and clicked Update, they can then click on custom text properties in the chart icon, select\u00a0the Vertical Line, type in some custom texts, click on the Properties button to change the\u00a0font size\/color\/type, or use the icons to move the custom text\u2019s location. Users can also\u00a0enter in custom percentile or numerical values to show in the chart. As a side note, this\u00a0Simulation Results forecast chart shows one output variable at a time, whereas the\u00a0Overlay Results compares multiple simulated output forecasts at once.<\/p>\n <\/p>\n Probability Distribution Overlay Charts<\/strong><\/p>\n Figure 16 illustrates the Overlay Results tab. Multiple simulation output variables can be compared at once using the overlay charts. Users simply check\/uncheck the simulated outputs they wish to compare and select the chart type to show (e.g., S\u2010Curves, CDF, PDF). Users can also add percentile or certainty lines by first selecting the output chart, entering the relevant values, and clicking the Update button. As usual, the generated charts are highly flexible in that users can modify them using the included chart icons (as well as whether to show or hide gridlines) and the chart can be copied into the Microsoft Windows clipboard for pasting into another software application. Typically, S\u2010curves of CDF curves are used in overlay analysis when comparing the risk profile of multiple simulated forecast results.<\/p>\n \nAnalysis of Alternatives<\/strong><\/p>\n Figure 17 illustrates the Analysis of Alternatives subtab. Whereas the Overlay Results subtab shows the simulated results as charts (PDF\/CDF), the Analysis of Alternatives subtab shows the results of the simulation statistics in a table format as well as a chart of the statistics such that one option can be compared against another. The default is to run an analysis of alternatives to compare one option versus another, but users can also choose the Incremental Analysis option (remembering to choose the desired economic metric to show, its precision in terms of decimals, the Base Case option to compare the results to, and the chart display type).<\/p>\n Dynamic Sensitivity Analysis<\/strong><\/p>\n Figure 18 illustrates the Dynamic Sensitivity Analysis computations. Tornado analysis and scenario analysis are both static calculations. Dynamic sensitivity, in contrast, is a dynamic analysis, which can only be performed after a simulation is run. Users start by selecting the desired option\u2019s economic output. Red bars on the Rank Correlation chart indicate negative correlations and green bars indicate positive correlations for the left chart. The correlations\u2019 absolute values are used to rank the variables with the highest relationship to the lowest, for all simulation input assumptions. The Contribution to Variance computations and chart indicate the percentage fluctuation in the output variable that can be statistically explained by the fluctuations in each of the input variables. As usual, these charts can be copied and pasted into another software application.<\/p>\n <\/p>\n <\/p>\n Strategic Real Options Analysis<\/strong><\/p>\n Figure 19 illustrates the Options Strategies tab. Options Strategies is where users can draw their own\u00a0custom strategic maps, and each map can have multiple strategic real options paths. This section allows\u00a0users to draw and visualize these strategic pathways and does not perform any computations. (The next\u00a0section, Options Valuation, actually performs the computations.) Users can explore this section\u2019s\u00a0capabilities, but viewing the Video on Options Strategies (see Figure 30 for the Knowledge Center videos)\u00a0to quickly get started on using this very powerful tool is recommended.<\/p>\n \nProcedure<\/strong><\/p>\n Users can also explore some preset options strategies by clicking on the first icon and selecting any one of the examples (click on the first icon and select Examples). Below are details on this Options Strategies tab:<\/p>\n Real Options Valuation Modeling<\/strong><\/p>\n Figure 20 illustrates the Options Valuation tab and the Strategy View. This section performs the\u00a0calculations of real options valuation models. Users must understand the basic concepts of real options\u00a0before proceeding. This Options Valuation tab internalizes the more sophisticated Real Options SLS\u00a0software as described and explained in Chapter 13 of Modeling Risk, Third Edition. Instead of requiring\u00a0more advanced knowledge of real options analysis and modeling, users can simply choose the real\u00a0option types, and the required inputs will be displayed for entry. Users can compute and obtain the real\u00a0options value quickly and efficiently, as well as run the subsequent tornado, sensitivity, and scenario\u00a0analyses.<\/p>\n Procedure<\/strong><\/p>\n Users start by choosing the option execution type (e.g., American, Bermudan, or European),\u00a0selecting an option to model (e.g., single phased and single asset or multiple phased\u00a0sequential options), and, based on the option types selected, entering the required inputs\u00a0and clicking Compute to obtain the results. Users can also click on the Load Example to load\u00a0preset input assumptions based on the selected option type and option model. Some basic\u00a0information and a sample strategic path are shown on the right under the Strategy View.\u00a0Also, a tornado analysis and scenario analysis can be performed on the option model and\u00a0users can Save As, Delete, or Edit existing saved models. The Sensitivity Analysis subtab runs\u00a0a static sensitivity table of the real options model based on updated user inputs and settings.\u00a0The Tornado Analysis subtab develops the tornado chart of the real options model. The\u00a0interpretation of this tornado chart is identical to those previously described. The Scenario\u00a0Analysis subtab runs a scenario table of the real options model based on updated user\u00a0settings (input variables to analyze as well as the range and amount to perturb).<\/p>\n <\/p>\n Stochastic Forecasting and Prediction Modeling<\/strong><\/p>\n Figure 21 illustrates the Forecast Prediction module. This is a sophisticated business analytics and\u00a0business statistics module with over 150 functionalities. The user starts by entering the data in Step 1\u2019s\u00a0data grid (user can copy and paste from Microsoft Excel or other ODBC\u2010compliant data source, manually\u00a0type in data, or click on the Options | Example button to load a sample dataset complete with previously\u00a0saved models). The user then chooses the analysis to perform in Step 2 and, using the variables list\u00a0provided, enters the desired variables to model given the chosen analysis (if users previously clicked\u00a0Example, users can double\u2010click to use and run the saved models in Step 4 to see how variables are\u00a0entered in Step 2, and use that as an example for their analysis). The user then clicks Run in Step 3 when ready to obtain the results, charts, and statistics of the analysis that can be copied and pasted to another\u00a0software application. Users can alternatively Save or Edit\/Delete their models in Step 4 by giving them a\u00a0name for future retrieval from a list of saved models, which can be sorted or rearranged accordingly.\u00a0Users may also explore the power of this forecasting module by loading the preset Example, or they can watch the Getting Started Videos (see Figure 30) to quickly get started using the module and review the\u00a0user manual for more details on the 150 analytical methods.<\/p>\n <\/p>\n Procedure<\/strong><\/p>\n The following steps are illustrative of the Forecast Prediction procedures:<\/p>\n Figure 22 illustrates the forecast prediction\u2019s Visualize and Charts subtabs. Depending on the model\u00a0run, sometimes the results will return a chart (e.g., a stochastic process forecast was created and the\u00a0results are presented both in the results and charts subtabs). The charts subtab has multiple chart icons\u00a0users can use to change the appearance of the chart (modify the chart type, chart line colors, chart view, Figure 23 illustrates the forecast prediction\u2019s Command Console. Users can also quickly run multiple\u00a0models using direct commands here. It is recommended that new users set up the models using the user\u00a0interface, starting from Step 1 through to Step 4. To start using the console, users create the models they\u00a0need, then click on the Command subtab, copy\/edit\/replicate the command syntax (e.g., users can\u00a0replicate a model multiple times and change some of its input parameters very quickly using the\u00a0command approach), and when ready, click on the Run Command button. Alternatively, models can also\u00a0be entered using a console. To see how this works, users can double\u2010click to run a model and go to the\u00a0console. Users can replicate the model or create their own and click Run Command when ready. Each line\u00a0in the console represents a model and its relevant parameters. The figure also shows a sample set of\u00a0results in the Statistics subtab.<\/p>\n <\/p>\n <\/p>\n Portfolio Optimization<\/strong><\/p>\n Figure 24 illustrates the Portfolio Optimization\u2019s Optimization Settings subtab. In the Portfolio\u00a0Optimization section, the individual options can be modeled as a portfolio and optimized to determine\u00a0the best combination of projects for the portfolio. In today\u2019s competitive global economy, companies are\u00a0faced with many difficult decisions. These decisions include allocating financial resources, building or\u00a0expanding facilities, managing inventories, and determining product\u2010mix strategies. Such decisions\u00a0might involve thousands or millions of potential alternatives. Considering and evaluating each of them\u00a0would be impractical or even impossible. A model can provide valuable assistance in incorporating\u00a0relevant variables when analyzing decisions and in finding the best solutions for making decisions.\u00a0Models capture the most important features of a problem and present them in a form that is easy to\u00a0interpret. Models often provide insights that intuition alone cannot. An optimization model has three\u00a0major elements: decision variables, constraints, and an objective. In short, the optimization methodology finds the best combination or permutation of decision variables (e.g., which products to sell or which\u00a0projects to execute) in every conceivable way such that the objective is maximized (e.g., revenues and\u00a0net income) or minimized (e.g., risk and costs) while still satisfying the constraints (e.g., budget and\u00a0resources).<\/p>\n The options can be modeled as a portfolio and optimized to determine the best combination of\u00a0projects for the portfolio in the Optimization Settings subtab. Users start by selecting the optimization\u00a0method (Static or Dynamic Optimization). Then they select the decision variable type of Discrete Binary\u00a0(choose which Options to execute with a Go\/No\u2010Go Binary 1\/0 decision) or Continuous Budget Allocation (returns % of budget to allocate to each option as long as the total portfolio is 100%); select the Objective (Max NPV, Min Risk, etc.); set up any Constraints (e.g., budget restrictions, number of projects restrictions, or create customized restrictions); select the options to optimize\/allocate\/choose (default selection is all options); and when completed, click Run Optimization. The software will then take users to the Optimization Results tab.<\/p>\n <\/p>\n Figure 25 illustrates the Optimization Results tab, which returns the results from the portfolio\u00a0optimization analysis. The main results are provided in the data grid, showing the final Objective\u00a0Function results, final Optimized Constraints, and the allocation, selection, or optimization across all\u00a0individual options within this optimized portfolio. The top left portion of the screen shows the textual\u00a0details and results of the optimization algorithms applied, and the chart illustrates the final objective\u00a0function. The chart will only show a single point for regular optimizations, whereas it will return an\u00a0investment efficient frontier curve if the optional Efficient Frontier settings are set (min, max, step size)\u00a0in the tab.<\/p>\n <\/p>\n Customized Optimization Modeling<\/strong><\/p>\n Figure 26 illustrates the Advanced Custom Optimization tab\u2019s Optimization Method routines, where users\u00a0can create and solve their own optimization models. Knowledge of optimization modeling is required to\u00a0set up models, but users can click on Load Example and select a sample model to run. Users can use\u00a0these sample models to learn how the optimization routines can be set up. Users click Run when done to execute the optimization routines and algorithms. The calculated results and charts will be presented on\u00a0completion. When users set up their own optimization model, it is recommended that they go from one\u00a0tab to another, starting with the Method (Static, Dynamic, or Stochastic Optimization) and based on the\u00a0selected method, input the simulation seeds or simulation trials, or the number of stochastic\u00a0optimization iterations to perform. The Optimized Results will be shown after the model is run, where\u00a0the resulting optimized decision variables are presented as a data grid. Perhaps the best way to get\u00a0started for a new user is to load an existing set of example models to run. The user\u2019s own custom model\u00a0can be checked using the Verify process to determine if the model is set up correctly.<\/p>\n \nFigure 27 illustrates the Advanced Custom Optimization\u2019s Decision Variables setup, where decision\u00a0variables are those quantities over which users have control; for example, the amount of a product to\u00a0make, the number of dollars to allocate among different investments, or which projects to select from\u00a0among a limited set. As an illustrative example, portfolio optimization analysis includes a go or no\u2010go\u00a0decision on particular projects. In addition, the dollar or percentage budget allocation across multiple\u00a0projects also can be structured as decision variables. Users can click Add to add a new Decision Variable.\u00a0Users can also Change, Delete, or Duplicate an existing decision variable. The Decision Variables can be\u00a0set as Continuous (with lower and upper bounds), Integers (with lower and upper bounds), Binary (0 or\u00a01), or a Discrete Range. The list of available variables is shown in the data grid, complete with their\u00a0assumptions (rules, types, starting values). Users can view the Detailed Analysis of the optimization\u00a0results.<\/p>\n <\/p>\n Figure 28 illustrates the Advanced Custom Optimization\u2019s Constraints setup,which describes\u00a0relationships among decision variables that restrict the values of the decision variables. For example, a\u00a0constraint might ensure that the total amount of money allocated among various investments cannot\u00a0exceed a specified amount or, at most, one project from a certain group can be selected; budget\u00a0constraints; timing restrictions; minimum returns; or risk tolerance levels. Users can add a new\u00a0constraint, and change or delete existing constraints. When adding a new constraint, the list of available\u00a0Variables will be shown. Users can simply double\u2010click on a desired variable and its variable syntax will\u00a0be added to the Expression window. For example, double\u2010clicking on a variable named \u201cReturn1\u201d will\u00a0create a syntax variable \u201c$(Return1)$\u201d in the window. Users can enter their own constraint equation(s).\u00a0For example, the following is a constraint: $(Asset1)$+$(Asset2)$+$(Asset3)$+$(Asset4)$=1 where the\u00a0sum of all four decision variables must add up to 1. Users can keep adding as many constraints as\u00a0needed without any upper limit. The optimization results on occasion will also return a chart, for\u00a0example, when an investment efficient frontier (Modern Portfolio Theory) is modeled in the\u00a0optimization routines. Additional chart functionalities are available in the charts.<\/p>\n <\/p>\n Figure 29 illustrates the Advanced Custom Optimization\u2019s Objective, which provides a mathematical representation of the model\u2019s desired outcome, such as maximizing profit or minimizing cost, in terms of the decision variables. In financial analysis, for example, the objective may be to maximize returns while minimizing risks (maximizing the Sharpe\u2019s ratio or returns\u2010to\u2010risk ratio). Users can enter their own customized objective in the function window. The list of available variables is shown in the Variables window on the right. This list includes predefined decision variables and simulation assumptions. An example of an objective function equation looks something like:<\/p>\n ($(Asset1)$*$(AS_Return1)$+$(Asset2)$*$(AS_Return2)$+$(Asset3)$*$(AS_Return3)$+ Users can use some of the most common math operators such as +, \u2010, *, \/, **, where the latter is the\u00a0function for \u201craised to the power of.\u201d<\/p>\n The Advanced Custom Optimization\u2019s Statistics subtab will be populated only if there are simulation\u00a0assumptions set up . The Statistics window will only be populated if users have previously defined\u00a0simulation assumptions available. If there are simulation assumptions set up, users can run Dynamic\u00a0Optimization or Stochastic Optimization (Figure 26); otherwise users are restricted to running only\u00a0Static Optimization. In the window, users can click on the statistics individually to obtain a droplist. Here\u00a0users can select the statistic to apply in the optimization process. The default is to return the Mean from\u00a0the Monte Carlo Risk Simulation and replace the variable with the chosen statistic (in this case the\u00a0average value), and Optimization will then be executed based on this statistic.<\/p>\n <\/p>\n Knowledge Center<\/strong><\/p>\n Figure 30 illustrates the Knowledge Center\u2019s Step\u2010by\u2010Step Procedures, where users will find quick getting\u00a0started guides and sample procedures that are straight to the point to assist them in quickly getting up\u00a0to speed in using the software. Users click on the Previous and Next buttons to navigate from slide to\u00a0slide or to view the Getting Started Videos. These sessions are meant to provide a quick overview to help users get started with using PEAT and do not substitute for years of experience or the technical\u00a0knowledge required in the Certified in Risk Management (CRM) and Certified in Quantitative Risk\u00a0Management (CQRM) programs. The Step\u2010by\u2010Step Procedures section highlights some quick getting\u00a0started steps in a self\u2010paced learning environment that is incorporated within the PEAT software. There\u00a0are short descriptions above each slide, and key elements of the slide are highlighted in the figures for\u00a0quick identification.<\/p>\n <\/p>\n The Knowledge Center\u2019s Basic Project Economics Lessons section provides an overview tour of some\u00a0common concepts involved with cash\u2010flow analysis and project economic analysis such as the\u00a0computations of NPV, IRR, MIRR, PI, ROI, PP, DPP, and so forth. In addition, the Knowledge Center\u2019s\u00a0Getting Started Videos subtab is where users can watch a short description and hands\u2010on examples of\u00a0how to run one of the sections within this PEAT software. The first quick getting started video is\u00a0preinstalled with the software while the rest of the videos may be downloaded at first viewing. Before\u00a0downloading any materials, users should ensure they have a good Internet connection to view the online\u00a0videos. The videos may be entirely preinstalled or entirely downloadable.<\/p>\n The Knowledge Center has additional functionalities that users can customize. The Knowledge\u00a0Center files (videos, slides, and figures) may be available in the installation path\u2019s three subfolders:\u00a0Lessons, Videos, and Procedures. Users can access the raw files directly or modify\/update these files and\u00a0the updated files will show in the software tool\u2019s Knowledge Center the next time the software utility\u00a0tool is started. Users can utilize the existing files (e.g., file type such as *.BMP or *.WMV as well as pixel\u00a0size of figures) as a guide to the relevant file specifications they can use when replacing any of these\u00a0original Knowledge Center files. If users wish to edit the text shown in the Knowledge Center, they can\u00a0edit the *.XML files in the three subfolders, and the next time the software tool is started, the updated\u00a0text will be shown. This file format is small in size and, hence, more portable when implementing it in\u00a0the PEAT software tool installation build, such that users can still e\u2010mail the installation build without\u00a0the need for uploading to an FTP site. There are no minimum or maximum size limitations to this file\u00a0format.<\/p>\n Figure 31 illustrates the PEAT Dashboard. After all the models are run (simulations, tornado, scenarios, etc.), users can access the Dashboard to create the settings required to generate the dashboard. Multiple dashboards can be saved and re\u2010run as required for presentation to senior management.<\/p>\n <\/p>\n Figure 32 illustrates the Excel report generated in PEAT. After running all the models (simulations,\u00a0tornado, scenario, etc.), users can click on Report | Report Configuration and then select or deselect the\u00a0relevant analyses to run. When Run Report is clicked, the selected models will run and their results (data\u00a0grids, models, charts, and results) are pasted into Microsoft Excel and Microsoft PowerPoint.<\/p>\n![\"\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/02\/Page-3-Image-13.jpg\")
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\netc.). Also, when a variable header in the data grid is selected, users can click on the Visualize button or\u00a0right\u2010click and select Visualize, and the data will be collapsed into a time\u2010series chart.<\/p>\n![\"FIGURE](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-29-Image-222.jpg\")
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\n$(Asset4)$*$(AS_Return4)$)\/sqrt($(AS_Risk1)$**2*$(Asset1)$**2+$(AS_Risk2)$**2*$
\n(Asset2)$**2+$(AS_Risk3)$**2*$(Asset3)$**2+$(AS_Risk4)$**2*$(Asset4)$**2)<\/p>\n![\"FIGURE](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-36-Image-311.jpg\")
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