Our methodology helps you
manage complex business
interactions and understand the
performance implications
of your decisions
In “The Right Approach,” we outlined the sequence of work Portfolio Decisions uses when building and analyzing a portfolio model. This section, “Unique Methodology,” is written for the technical team who may want to know more about our methodology. Special attention is given to the data collection and analysis steps.
Our approach incorporates five steps, each customized to the client’s unique situation:
FRAMING
Framing is a process designed to help us understand the client’s business needs and expectations. We use interviews to collect information required to customize the portfolio process to address each client’s specific issues. Framing may be a formal, multi-week process involving many interviews and meetings, or it may be an informal process of talking with a few individuals. This all depends on the business needs to be addressed, the organizational complexity, the scope of the initial portfolio model, and the established patterns of communication within the company.
DATA COLLECTION
When we talk about the data needed to populate a portfolio model, we are referring to economic and performance data for each opportunity to be considered for inclusion in the portfolio. This information can either be in the form of time series (tracking metric performance over time), point indicators (such as NPV or cumulative resources), or both. This input data is not calculated in Perspectives™; it usually comes from the client’s own economic evaluations, which most companies perform for project appraisal and approval. Input can also include information about when each opportunity can be started, how many of each type is available (if the opportunity is a generic description), what are the company's economic assumptions, etc. Some of the required metrics are industry specific, and some will be common to most companies and corporations.
Depending on the analysis required, some supplemental information (reserve add schedules, manpower, and other resource requirements) may need to be added. Portfolio Decisions staff can help you design a program for generating this data with a minimum of extra work for your staff.
The collected data will look something like the "project 1" below. The numbers across the top refer to time periods in the project’s life:
Each metric is forecast for as many time periods as necessary (the graphic is cut off at 4 years; the data can extend for the whole life of the project). Point indicators are simply placed in the first year. In this case, there is one outcome, and so it has a weight of 100%. Note that the Net Present Value (NPV) is also included.
Projects may be described using a single outcome (deterministic data) or using multiple outcomes (stochastic data), with each outcome having an associated weight or probability of occurrence. A project with multiple outcomes might look something like the "Project 3".
Multiple outcomes are used to represent projects where the outcome is uncertain. They are commonly used when projects have a high chance of failure or when projects might be assumed to succeed to a certain point but there is uncertainty as to the level of that success. Stochastic data can be used to represent technical risk and cost, financial and political uncertainties, to name a few.
Some people worry that if they don’t have their projects described in a multiple outcome format they can’t do portfolio management. This isn’t true; the multiple outcomes are used for the probabilistic analysis only. Portfolio analysis can be performed with all stochastic input, all deterministic input, or with a combination of the two. As always, our counsel is to use what you have and then enhance the system as you see and understand the need.
It takes some experience to determine what data is critical, what is data is useful but not completely necessary, and what data is not needed at all in a portfolio analysis. Even when clients elect to do the “heavy lifting” of initial data collection using their staff, it is in close consultation with a consultant from Portfolio Decisions. Having a deep understanding of data requirements as it is gathered can save you weeks of work.
Getting the Data into Perspectives
Data can be typed, pasted, or imported into a Perspectives database from an Excel spreadsheet in a specific format. If your data is spread over multiple sources in a number of formats, Portfolio Decisions has a proprietary tool, called Foundations™ that can read multiple data formats, process the data for consistency (currency, units, etc.), and import it seamlessly into Perspectives.
Limiting Project Selections
Once project data have been imported into the model, we next set up rules about when the projects can be selected and, if a type project can be selected multiple times, how often.
To accomplish this, we require a minimum of two pieces of information:
- How many times a project can be chosen in total over the course of analysis and
- How many times a project can be chosen in each year of analysis.
In addition, we may specify a minimum number for either time frame.
Using this system, we can specify that a project can be chosen only once in a specific year, only once but in one of a number of years, not at all for a number of years, several times a year for a number of years, etc. We can also require that a project be chosen at some point or in a particular year. The Portfolio Decisions consultant works closely with your staff to populate the selection grid in such a way that your company realities are reflected, but maximum flexibility is retained.
MODEL CONSTRUCTION
At this point, we begin to use the input metrics as the variables to calculate computed metrics and even to set up whole additional pages of computations. For example, we can calculate anything from simple ratios like Operating Cost per Unit of Production, to more complex metrics like Return on Capital and Changes in Debt, to entire pages of financial statements such as an Income Statement or a Balance Sheet.
Dependencies
Before using optimization to make a new set of selections, we set up dependencies among projects where appropriate. There are a wide variety of dependencies available in Perspectives, and further dependencies can be easily custom coded. Your Portfolio Decisions consultant will work with you to set up the dependencies that are appropriate for your business problems.
Setting up Targets and Optimizing
In order to analyze the portfolio using optimization, we need to set up targets for some of your metrics and choose a single value to maximize or minimize. For example, we might want to look at the maximum value that can be realized if we limit capital to $200MM per year growing at 5% for ten years and require Net Income to maintain a level of $150MM a year for ten years, Return on Capital to be greater than 9%, and Debt to stay under $1.5B.
Probability Ranges
Once a portfolio has been chosen, we can use Monte Carlo simulation to investigate the range of possibilities and/or the probabilities of meeting targets. The simulation uses the multiple outcome data supplied in the project data descriptions in calculating its ranges. The information thus obtained is used for risk mitigation techniques.
ANALYSIS
Once the frame has been determined, data loaded, model constructed, dependencies established and targets set, analysis can begin. As a very simple example, let’s say we want to investigate the effects of capital spending on Net Income in 2013.
To start, we might maximize Net Income over a ten year period given capitalc expenditures of $200 MM growing at 5% per year, and a Return on Capital requirement of 9%. The following performance results:
This performance is a result of the selections shown on the left. The figure in each cell represents the number of that type of project that was selected to begin in the year designated by the column header.
Let’s take this analysis one step further, and see what the effect on performance might be of decreasing capital spending to $150MM per year, held flat. The performance for this case is in the charts on the right.
The associated selections are on the left.
Let’s look at this portfolio compared to the previous one. The performance of the portfolio with less capital is shown by the red line; the performance of the portfolio with escalated capital expenditures is shown in blue.
Decreasing spending has had a material negative impact on Production, Cash Flow, and Net Income after about 5 years. Until that time, performance is only slightly compromised, indicating that most of the projects in the portfolio have rather long lead times. Decreasing spending has actually increased both Return on Capital and the probability of achieving the Return on Capital targets. This makes sense, as decreasing spending while maximizing Net Income will result in more efficient capital allocation on a dollar spent per dollar of Net Income basis.
The table on the right shows the difference in selections between the two cases. A red number indicates a decrease in selection in the unescalated case when compared with the escalated case, and a black number indicates an increase. A blank means no change.
As you might expect, since there was less money available, most of the changes are decreases or delays in selection. But there are increases as well, particularly in Projects 18 and 19, which are accelerated from 2013 to 2009-2010. Decreasing money didn’t just decrease projects; it changed the projects that were the best investments under the changed circumstances.
Which is the best portfolio? That depends upon the overall company goals and would require more information and more analysis than we can show in this example. However, we have learned that below $200MM we start to give up performance as we decrease spending, even if we are spending a bit more efficiently.
SYNTHESIS AND COMMUNICATION
The foregoing was a very simple example; it is difficult to communicate concepts if we layer in the complications that surround most real world portfolio problems. To apply portfolio concepts to real world problems would require a program similar to the following:
Quite a bit of analysis needs to be performed. Rarely is a company looking at only the interplay between two variables. The relationship among a number of metrics needs to be investigated over a realistic time frame. Both expected value and probabilistic views need to be taken into account. The metrics and their interactions often need to be evaluated under a number of different economic and political scenarios.
This analysis needs to be synthesized and summarized in a few clear and easily distinguishable scenarios. It is often difficult for technically trained people, who have worked a problem for weeks or months, to let go of the detail and distill the most critical insights gained from a wealth of analysis, but it is essential to do so. Portfolio Decisions personnel have years of experience identifying the big learnings from a complex analysis.
Insights gained need to be communicated clearly, without bias or prejudice. Months of work are usually judged in a two- or three-hour meeting; it is critical that the value of the analysis be communicated without jargon or unnecessary detail in a manner that tells the whole truth and not just the comfortable parts of the truth. The critical issues must be presented in a manner such that the executives actually hear the message and consider the information in their decision making process.
Make recommendations and take action. The analysis needs to result is some action if the system is to remain healthy. It is important for the technical team to understand that taking action does not always mean that decision makers will implement the exact recommendations that the team has made. There are always considerations that don’t get modeled, for whatever reason. Portfolio management is a tool that decision makers can add to those they already have to allow them to make better decisions. If portfolio analysis results in an improved strategic conversation, and if that conversation results in better decisions, the technical team has done its job.
One final point needs to be considered: When companies struggle with portfolio management, it is usually because of issues related to people and culture, not data or software. Many companies have purchased software, built portfolio models, and produced excellent analyses that have never impacted decisions in any way. Analysts must realize that, while rigorous portfolio analysis is required for success, it is not, in itself, sufficient.
Great analysis that does not impact the decision makers’ thought processes is simply an interesting academic exercise. In today’s economic times, companies cannot afford the luxury of academic exercises. This brings us back to “The Right Approach”. Implementation requires that the decision makers be fully engaged in the process, not just supportive. Supportive executives will provide input and pay the bills. Engaged executives actively participate in the portfolio process and genuinely care about the results and the implications of what they can learn. As you go forward, are your decision makers prepared to engage?