Variables operativas

Project portfolio management (PPM) began in the field of modern portfolio theory, which was developed by Henry Markowitz, who was later awarded a Nobel Prize for his work (Wysocki, 2009). It was only during the 1990s that his theories were diversified from investments to projects (Wysocki, 2009). Today, the dominant themes of research in PPM are in the fields of New Product Development (NPD) and Technology and Innovation Management (TIM) (Hobbs, 2012).

A useful definition of project portfolio management is the following: “[It] includes establishing the investment strategy of the portfolio, determining what types of projects can be incorporated in the portfolio, evaluating, and prioritizing proposed projects, constructing a balanced portfolio that will achieve the investment objectives, monitoring the performance of the portfolio, and periodically adjusting the contents of the portfolio in order to achieve the desired results” (Wysocki, 2009:536).

A number of tools are available for PPM (see Cooper et al. (2001) for a review). A visually appealing and widely used tool is the portfolio mapping technique. This technique is described here and is the technique employed in the current study.

Early versions of portfolio mapping emerged in 1970 in a series of popular publications by the Boston Consulting Group (Day, 1977). This framework was rapidly adopted by companies – by 1972 the approach was employed by more than

100 companies (Day, 1977). The original portfolio maps distinguished between four types of projects: Stars, Cash Cows, Dogs and Problem Children. The axes of the original maps focused on market growth rate (relative to GNP growth) and market share dominance (share relative to largest competitor). Furthermore, the original maps emphasised business units rather than projects (Cooper et al., 2001) and focused on existing businesses with known performance, strengths and weakness in contrast to the new portfolio maps that emphasise new products and projects (Cooper et al., 1997).

More recent versions of portfolio maps (e.g. Matheson et al., 1989; Matheson and Menke, 1994; Cooper, 2005; Wysocki, 2009), also known as risk reward maps or bubble diagrams, contain Net Present Values of individual projects on the -xaxis, and a measure of the probability of success of each project on the y-axis (Figure 16), although there are variations on this theme. This framework has the ultimate aim of prioritising investment funds to particular projects.

Figure 16 Modified BCG products/services matrix

Note: BCG classification: ?, star, cash cow, dog; Cooper classification: oysters, pearls, bread and butter, white elephant. Size of circles indicate amount of resources committed to each project.

Source: Based on Cooper (2005), Wysocki (2009)

The definition of the different categories of projects is as follows (Matheson and Menke, 1994; Cooper et al., 1997; Cooper, 2005):

A. Oysters

These are long-shot projects with high expected payoffs but also high risk. These are projects where technical breakthroughs will pave the way for solid payoffs. Oysters require long periods of cultivation in the hopes of obtaining breakthroughs in products or processes. Significant incentives need to be given to researchers working

1

Net present value Prob. Of

success

High

Low

Dog/

White elephant

? Oysters Cash cow/

Bread and butter

Star/

Pearls

in this area. Company X in the example above is investing quite substantially into one such project.

B. Pearls

These are potential star products: projects with a low risk that are expected to yield a high reward. Company X has two, with relatively low investment going into them (Figure 16). Pearls require a more entrepreneurial approach. Budgets are not the key issue, since the high payoffs make virtually any budget permissible. The main focus is on development time and the promotion of flexibility to explore ways of commercialising the product.

C. Bread and butter

These are small, simple projects with a high likelihood of success but low reward.

They include many fixes, extensions, modifications and updating of projects, or incremental improvements to a product or process. Standard project management principles apply to these projects, namely deadlines, budget parameters and traditional performance incentives.

D. White elephants

These are projects that consume resources and are unlikely to produce commercial value. Most companies have some of these projects and they are often difficult to kill, but Company X has too many of them. White elephants require further research in order to determine whether they are worth saving or require restructuring (for example, by using existing technology rather than new technology to develop).

In addition to this information, the different bubbles may be colour coded to reflect different information: for example, project status (development, imminent or launched) or project category. In this way, the bubble diagram has the advantage of conveying a range of information to decision-makers at one time (Cooper et al., 1997) and also display portfolio balance (Cooper et al., 2001). The disadvantages of the framework include that it may be difficult to interpret and provide too much information for strategic decision-makers. In spite of these weaknesses, portfolio maps remain popular for business strategy. In a survey of different organisations, 40.6% of businesses reported using portfolio maps and they are highly recommended by managers as an effective tool for yielding correct portfolio decisions (Cooper et al., 2001).

Resource allocation is dependent on the current business climate, the market share and position of the enterprise, and a number of other factors (Wysocki, 2009). In a stable industry, such as clothing manufacturing, the majority of the resources will be committed to the bread and butter projects in order to maintain market share. Some resources will be committed to pearls, and even fewer in the more risky oyster category. If the industry is in a more volatile sector, such as IT, it is likely that it will commit more resources to the pearls and the oysters, and fewer resources to the bread and butter projects. Another, but related, way of looking at portfolio mapping is through linking with the economics of innovation discussed in the previous section. A Smithian organisation (after Adam Smith’s innovation through division of

labour) will have an emphasis on projects on the left-hand side of the portfolio map (Figure 17A). The main focus of such innovations is incremental innovations to increase and maintain market share. An organisation of the Koestlerian tradition (innovation through associative thinking) will lead to more radical innovations that produce potentially higher payoffs, but at the same time also introduce potentially greater risks. An organisation focusing on radical innovations will tend to have more projects on the right-hand side of the portfolio map (Figure 17B). It is important to emphasise that no single process is likely to be optimal in all circumstances and different processes are applicable in different contexts.

Figure 17 Portfolio mapping characterised by different types of innovation

The risk-reward framework for portfolio mapping is the most popular framework used by businesses (Table 28). However, a number of other axes are also used.

Table 28 Axes used in popular portfolio maps

Rank Type of chart Axis #1 Axis #2 %

Technical feasibility Market attractiveness (growth potential, consumer appeal, overall

Strategic focus or fit Business intent, NPV, financial fit, attractiveness

8.9 7 Cost vs Benefit Cumulative reward ($) Cumulative development costs ($) 5.6 Rank ordered; last column shows percentage breakdown of portfolio map usage (as a percent of businesses using portfolio maps).

Source: Cooper et al. (2001)

Net present value

3.5 Chapter summary and conclusions

It is useful to close this chapter by summarising how each stage in the risk assessment process was followed in the current study (Table 29).

Table 29 Steps in the risk analysis process

Stage Study methodology Outcome

1. System dynamics model Develop system dynamics model that study sites throughout South Africa where restoration is occurring.

2. Choose a strategy for the portfolio

The financial criterion used for the study was

The criterion that had most impact on the success or failure of the project is the price of ecosystem benefits.

4. Obtain a forecast for a variable of interest in terms of a probability distribution

Monte Carlo simulation is employed based on the set of restoration decision criteria that maximised the NPV of the project system dynamics model

Output is a set of uniform distributions for each of the project NPVs. Monte Carlo simulations based on single and multiple decision criteria are used to derive these distribution functions 5. Generate the simulated

distribution of the financial criterion

Calculation Summary statistics are generated, and risk management theory is used

Portfolio maps are developed for each of the risk reward profiles generated in the model.

This chapter focussed on the applied aspects of methodology by examining the risk analysis framework utilising system dynamics modelling. The framework was shown to be appropriate for transdisciplinary research integrating ecological, economic and hydrological aspects of restoration. In the next chapter, the system dynamics model and Monte Carlo simulations are validated, and some results presented.