Measuring strategic vertical alignment of project portfolio execution using a rho-squared coefficient

theory, application, and an exploratory case study

Assistant Professor, Technology Management

Utah Valley University, Orem, Utah

Abstract

This paper examines the concept of strategic alignment of projects, focusing on the alignment between strategic priorities and actual project work. The initial theoretical and practical justification for using a rho-squared coefficient in measuring the degree to which actual project work in a project portfolio “aligns” with strategic priorities is provided. In addition, a case study to illustrate the practical utility and elegance of the approach is presented.

Introduction

With global competition, struggling economies, and tighter windows of opportunity has come increased interest in more effective ways to evaluate project opportunities that compete for scarce resources. In today's business environment, organizations cannot afford to waste precious time, money, and effort on work that does not strongly align with the corporation's strategic objectives. Thus, the concept of “strategic alignment,” broadly conceived, has taken on increased importance in the research on and practice of project management. But what does “strategic alignment” mean? And what are the implications of strategic alignment for how work is actually carried out in the organization? This paper conceptualizes alignment in terms of the degree of “fit” between project portfolio priorities, which manifest strategic objectives, and actual work on projects being executed in the organization. This paper applies a rho-squared coefficient as an appropriate and elegant measure of alignment so conceived, and demonstrates how the coefficient is used in a simple case study.

More specifically, as interest in project portfolio management has increased, project priority lists are becoming a staple in organizations that have even the most basic levels of project management discipline. These project priority lists are ostensibly used by project teams to decide on which projects to work—so they are working on “first things first.” Once strategic priorities are established and published (if they are published at all) and work is executed, there is often no real verification that actual work was ever carried out according to those priorities, manifesting a “knowing-doing gap” (Pfeffer & Sutton, 2000). The “alignment coefficient” proposed in this paper is a metric designed to help solve this problem by providing insight into the degree to which actual project work aligns with corporate objectives as manifest in the strategic priorities.

Conceptualizing and Measuring Strategic Alignment of Project Management

Over the past decade or so, there has been a great deal published about “strategic alignment,” notwithstanding the fact that there is surprisingly little consensus on how to operationalize and measure it (e.g., Thamhain, 2007; Kaplan & Norton, 2001a). Since the early 1990s, experts have insisted that alignment of the organization's activities with its strategies creates competitive advantage (e.g., Powell, 1992; Porter, 1991; 2008). Similarly, the efforts of Project Management Institute in “making project management indispensable for business results” has also recently emphasized the importance of project management in ensuring that organizational activities are efficiently and effectively executed according to strategic directives and priorities. As such, it is problematic that nowhere is this lack of operational and measurement consensus regarding strategic alignment truer than when it comes to the role of project management in strategic alignment. This state of affairs has led some researchers to conclude, “Unlike other well-established business functions, project management has not yet built the theory and criteria of alignment with the business strategy, as in fact recognized by PMI's OPM3®” (Shenhar, 2007, p. 4). Thus, whereas the notion of strategic alignment of project management is intuitively appealing, it does not appear to be easily amenable to measurement (Thamhain, 2007; Mullaly & Thomas, 2009; Hanson, Melnyk, & Calantone, 2011).

What researchers and managers call “strategic alignment” is defined in different ways in various literature. Often, definitions depend on whether it is being defined for research (testing theories) or managerial (practical) purposes, and whether it is being defined broadly to include “precursor conditions,” such as “consensus,” or outcome measures of organizational performance. For example, in general terms, alignment occurs when all the company's interests and actions are directed toward company goals. More specifically, the degree to which employees and management agree on what is most important for the organization to succeed has been called “strategic consensus or organizational fit.” On this view, strategic alignment is conceived as a latent construct, best measured by a complex array of indices, including subjective perceptions of agreement among various internal and external stakeholders (Hanson, Melnyk, & Calantone, 2011).

Frequently, a notion of alignment is conceptualized as the degree of correspondence among and between such horizontally related functions as R&D, production, human resources, and information technology, with arguably the most research focusing on how IT/IS are “aligned” with business strategy (e.g., Mekawy, Rusu, & Ahmed, 2009; Bergeron, Raymond, & Rivard, 2004). Closely related to this is a notion of strategic alignment conceived as correspondence among and across elements of the organizational hierarchy, typically focusing on vertical elements such as the corporate level, business units, functional departments, and program or project portfolios (sometimes referred to as “external fit”; see, e.g., Papke-Shields & Malhotra, 2001).

Cutting across these vertical and horizontal conceptions of alignment is project management, further complicating what we mean by strategic alignment. Project management has long been seen as the integrative function in organizations, horizontally integrating vertical functional silos and their specialties to leverage value-creating synergies (A Guide to the Project Management Body of Knowledge [PMBOK® Guide]—Fourth Edition, Project Management Institute [PMI], 2008). Indeed, “Project management is an integrative undertaking requiring each project or product process to be appropriately aligned and connected with the other processes to facilitate coordination” (p. 38). (See also Section 2.4.2 – Organizational Structure, p. 28; cf. 1987 PMBOK® Guide, Section 3: “Framework: An Integrative Model”).

With such conceptual complexity facing them, it is perhaps understandable that only relatively recently have researchers started to explore the concept of strategic alignment as it applies to project management (e.g., Morris & Jamieson, 2004; Srivannaboon & Milosevic, 2006). To investigate this area further, PMI has funded research projects to look at the way corporate strategy is translated into project strategy (Morris & Jamieson, 2004). As Shenhar and colleagues (Shenhar, 2007; Shenhar & Dvir, 2007) conceptualized it, “alignment of project management and business strategy is an internal collaborative state where project activities continually support the achievement of enterprise strategic goals” (p. 7).

Moreover, this “internal collaborative state” must facilitate project selection and portfolio management, controlling projects during execution and providing upward information to the enterprise for business strategy adaptation and formulation. Milosevic and Srivannaboon (2007) explained that this line of investigation was interested in how the “project strategy” of individual projects should be “aligned” with the overall organizational/business strategy. Thus, using Porter's basic business strategies (cost leadership, differentiation, and segmentation), they reasoned that each individual project should emphasize different project components in order to be “aligned” with the business strategy. Although studies like this provide comprehensive conceptual frameworks, they still do not provide any reliable way to measure alignment between strategic priorities and project effort, per se. The authors concede that, for future study, “What is also needed is a large sample study that focuses on the quantitative correlations of various strategy types [e.g., Porter's] and project management elements. The point here is to find which strategies need which project elements to contribute to project success.” (p. 52)

Additionally, the proposed conceptual framework for project strategic alignment components are at three levels: The Enterprise Level (Corporate/Business Unit), the Individual Project Level (“Strategic Alignment Elements”), and Metrics (Enterprise Success Metrics and Project Strategic Success Metrics) (see Figure 1-1, p. 8). On this view, alignment is the degree of correspondence between and among the four elements at the Enterprise Level (Business Strategy, Typical Business Strategies, Portfolio Management/Project Selection, and Alignment Strategy to Guide Projects), and the six elements at the Individual Project Level (Project Charter, Project Strategy and Strategic Focus, Project Spirit/Culture, Project Organization, Project Processes, Project Tools), as well as Success Metrics defined at both levels. Of course, this multifaceted conceptualization has implications for how alignment is measured. Milosevic, Martinelli, and Waddell (2010) identified five metrics related to strategic management, portfolio management, program management, and project management levels:

Strategic Management and Program Portfolio Management Level:

Alignment of programs to business-unit strategic goals, measured by the percentage of total program portfolio that is compatible with documented business-unit strategic goals.

Projected future income from program road map, measured by the fraction of future net income by year projected from programs on the program road map over multiple years; the probability times the net income for accomplishing each program goal.

Program Portfolio Management Level:

Program portfolio distribution, measured by fractions of the total program portfolio among various dimensions that are important to program stakeholders.

Program Portfolio Management and Program Management Level:

External customer satisfaction survey measuring the average value of ratings given by key external customers, on a Likert scale of 1 to 5, with 5 being the highest value and 1 being the lowest value.

Program Portfolio Management, Program Management, and Project Management Level:

Percent of the program milestones accomplished, measured as the percent of all program milestones in the portfolio of programs achieved within appropriate time (pp. 315–316).

These proposed measures of various dimensions of strategic, program, portfolio, and project alignment are intuitive and useful. None of these metrics, however, measures the vertical alignment between strategic management (priorities) and actual project effort in a way that integrates vertically across all levels. Put another way, none of these metrics answers the question: “How aligned is actual project effort with strategic priorities? Are we working on ‘first things first’?”

A study by Hanson et al. (2011) comes close to addressing these questions. They defined and measured alignment, particularly as operationalized in the performance measurement and management system (PMMS). Because performance metrics should be aligned with strategy, they explored the process by which a firm strives to attain and maintain consensus and alignment. These researchers recognized that, with changing and “emergent” strategies, actual work can easily stray from adequate alignment with the new strategy. As such, Hanson et al. proposed a theoretical model with a latent construct of alignment, measured by a set of indicators of alignment. In other words, on their view, “fit is a pattern of covariation or internal consistency among a set of underlying, theoretically related variables” (p. 435). Similarly, Thamhain (2007) examined critical success factors for R&D projects and the role of strategic alignment, with “alignment” conceptualized in a complex, multifaceted way involving subjective perceptions based on “inputs from the people of the organization, assessing their likes, dislikes, preferences and performances” (p. 168). More relevant to our purposes, these studies point out that the distinction and relationship between alignment and actual effort is important so that employees are all “pulling in the right direction.” Thus, although the intent of this research was to develop a survey instrument that could be used to assess the state of alignment and effectiveness of the performance measurement system, their model did not measure alignment directly.

In a related study, Mullaly and Thomas (2009) reasoned that the strategic management literature has struggled with how to recognize, measure, and even understand the concept of “fit” between strategy, structure, and environment. They compared the “fit” construct against concerns with value direction, providing insights into how both dimensions improve overall organizational understanding. They demonstrated that, although each of these dimensions independently provides useful information, it is their intersection that provides a means of interpreting not just current realities but appropriate future actions. In short, in the initial stages of analysis, fit was taken to reflect an integration of two separate dimensions of value: satisfaction and alignment.

In a more explicit examination of the relationship between business strategy and project portfolio management, Meskendahl (2010) explored a conceptual framework that enabled examination of the linkage between business strategy, project portfolio management, and business success. Meskendahl recognized the “gap between strategy formulation and implementation” and sought to bridge that gap. Earlier research had found some supporting evidence of a positive relationship between isolated concepts, but so far there was no coherent and integral framework covering the whole cycle from strategy to success. Therefore, the existing research on project portfolio management is extended by the concept of strategic orientation. A comprehensive conceptual model considering strategic orientation, project portfolio structuring, project portfolio success, and business success was developed. Measuring overall portfolio success in this way, however, still does not address the “knowing-doing” gap between strategic priorities and project execution and effort.

Solak, Clarke, Johnson, and Barnes (2010) tried to capture the full range of complexity in project portfolios by examining how to optimize R&D project portfolios under endogenous uncertainty. They identified key components required for an effective portfolio management approach, including these:

Capturing financial returns and risks of assets;

Modeling interdependencies;

Determining prioritization, alignment and selection of projects;

Modeling organizational constraints; and

Dynamically reassessing the portfolio.

Once again, alignment was conceptualized as between strategic priorities and selected projects, but not between actual project execution. This study did point out the importance of accounting for resource allocation decisions. Still, allocating resources to projects does not guarantee alignment “where the rubber hits the road.” As Solak et al. (2010) recognized, the dynamics of project selection in research and development (R&D) projects, given the uncertainties and resource limitations over the planning period, although impressive in its sophistication, this approach still fails to conceive of alignment in a way that accounts for actual work performed on projects, regardless of how well-optimized the resource allocation is.

Blichfeldt and Eskerod (2008) confirm this idea. They argued that, although companies manage project portfolios concordantly with project portfolio theory, they may experience problems in the forms of delayed projects, resource struggles, stress, and a lack of overview. Based on a research project compromised of 128 in-depth interviews in 30 companies, they concluded that a key reason why companies do not do well in relation to project portfolio management (PPM) is that PPM often only covers a subset of on-going projects, whereas projects that are not subject to PPM tie up resources that initially were dedicated to PPM projects. In this way, the actual work being carried out in an organization is frequently not aligned with strategic objectives and priorities through the PPM system.

Driving Strategic Objectives throughout the Organization: The Balanced Scorecard Approach

Strategic planning, based on missions, visions, and SWOT Analysis, doesn't actually do much unless it gets driven down into the organization and transformed into practical, actionable initiatives and projects. As such, one of the most important factors in project success is selecting the best projects to undertake based on strategic plans. In additional to using a SWOT analysis, organizations often follow a detailed planning process for project selection that incorporates balanced scorecards (Kaplan & Norton, 2001) to select, define, and measure projects that put the strategic plan into action “where the rubber hits the road.” Many practitioners find it useful to identify a four-stage planning process for project selection:

Strategic planning: This stage will produce the determination of organizational strategy, goals, and objectives.

Business area analysis: This stage will produce the analysis of how various business areas can help achieve strategic goals.

Project planning: This stage will produce the identification of potential projects to help meet strategic goals.

Resource allocation: This stage will produce the allocation of resources to selected projects.

Not all companies follow such a discrete process, but the leaders of most companies try to disseminate their strategic plans down through the organization so that what actually happens “where the rubber hits the road” is consistent or “aligned” with those strategic plans. This is not easy to do. For this reason, a great deal of research and thought over many decades has gone into trying to figure out the best way to ensure that strategy gets properly communicated down through the organization, and that actual work being done in the organization is aligned with strategy. Arguably, the most widely used is the balanced scorecard.

Some time ago, Kaplan and Norton (1996) introduced the balanced scorecard based on the premise that an exclusive reliance on financial measures in a management system is insufficient. Financial measures are lag indicators that report on the outcomes from past actions. Exclusive reliance on financial indicators could promote behavior that sacrifices long-term value creation for short-term performance (Porter, 1991). The balanced scorecard approach retains measures of financial performance—the lagging outcome indicators—but supplements these with measures on the drivers, the lead indicators, of future financial performance. They described the role for strategy maps and balanced scorecards to develop performance objectives and measures linked to strategy. Organizations use their scorecards to align key management processes and systems to the strategy. The relationship of the balanced scorecard (BSC) to other financial and cost measurement initiatives, such as shareholder value metrics and activity-based costing, and quality programs, have been researched and established by Kaplan and Norton (see 2001b).

Although Kaplan and Norton provide a theory of organizational functioning that conceives of a multifaceted concept of “alignment” in an organization, and this conception is rich with measurement concepts and opportunities, nowhere do they identify how to measure alignment itself, particularly alignment between strategy, planning, resource allocation, and then resource execution. Kaplan and Norton also do not identify project management as a key component of the balanced scorecard approach, but the role of project management is implied. Nevertheless, the presumption is that if resources are “allocated” to work on specific, aligned projects and tasks, then their actual work will also be automatically “aligned.” But this is often not the case.

Prioritizing Projects According to Strategic Objectives

An organization can view project portfolio management as having five levels, from simplest to most complex as follows:

Putting all projects in one list;

Prioritizing the projects in the list;

Dividing projects into several categories based on types of investment;

Automating the list; and

Applying modern portfolio theory, including risk-return tools that map project risks.

As such, many managerial (more practice-oriented) approaches to project portfolio management recommend the creation of a project priority list by first creating a “prioritization model” (Wiegers, 2000; Kendall & Rollins, 2003; Rajegopal, McGuin, & Waller, 2007; Sanwal, 2007; Rad & Levin, 2007). This involves identifying strategic “drivers” based on the vision, strategic plan, and objectives of the organization. These drivers are then weighted based on which of them play a greater role in the decision-making than others. Weighting factors for each of the drivers are typically assigned by allocating 100 points among the drivers so that the most important drivers receive the greatest weight. Next, a “rating scale” is defined for each driver; for example, for a “technology integration” driver, a four-point scale might be defined with point values of 1, 3, 5, and 7 defining “no technology integration,” “some technology integration,” “significant technology integration,” and “key technology integration,” respectively (cf. Wiegers, 2000).

In the example in Figure 1, note the drivers (“business goals, impact, and competitive urgency,” each with subcategories), and the rating scales used for each of the drivers. These prioritization models are applied straightforwardly: a steering committee or governance board typically rates each candidate project with respect to each prioritization driver (as illustrated in Figure 1). The rating along the defined scale for each driver is multiplied by the corresponding weighting factor for that driver, and the sum of the cross-products for all drivers yields a priority score for each project. Assuming the strategy has been appropriately articulated in the drivers, the highest-scoring projects most closely align with the organization's strategic objectives, and hence they should be the top priorities. To validate the model, it is often recommended that governance boards analyze several recently completed projects and compare the model-derived scores with subjective, after-the-fact evaluations of each of those calibration projects (see Wiegars, 2000; Rajegopal et al., 2007). The candidate projects are listed in descending order by the computed score, and “force ranked,” meaning that there can be no duplication of priorities (Kendall & Rollins, 2003), to yield a “prioritization sequence” for how the projects should be executed: the project priority list, illustrated in Table 1.

Prioritization model applied to an example project (from Kendall & Rollins, 2003, p. 164)

Figure 1: Prioritization model applied to an example project (from Kendall & Rollins, 2003, p. 164).

Table 1: Illustration of a project priority list.

Project# Project Description Priority/Opportunity Score (1-100) Priority Rank
15070 ERP Implementation 93 1
20724 Data Center Construction 91 2
20723 Global IS Upgrade 88 3
20721 International Product Compliance 87 4
26466 Sales Fulfillment System Implementation 86 5
20725 New Satellite Service Pilot 85 6
10948 Corporate University Construction 82 7
15000 Sarbanes-Oxley Compliance 79 8
29668 Research Facility Remodel 73 9
20344 Central Plant Road & Water Line Repair 71 10

Kendall and Rollins (2003) summarized the typical thinking:

There are two distinct parts to managing prioritization. One is to establish a prioritization model that all functional leaders buy in to. The second is to ensure that work is released to functional areas according to the priorities, resolving major resource conflicts and improving project flow throughout the organization. (p. 161)

Closing the Loop Between Strategic Priorities and Project Execution: Are We Working on “First Things First?”

Although these approaches to prioritization are extremely important to any organization trying to focus on “first things first,” there must be more than two parts to managing prioritization. These approaches typically do not go far enough in closing the loop to ensure that the actual efforts being put into projects are aligned with the strategic objectives represented by the project priority list. In other words, in the day-to-day turbulence of executing multiple projects in an organization, the project priority list often gets ignored, misused (e.g., to beat people up with), or otherwise derailed so that, in the final analysis (an analysis that is rarely done), there is still more time and effort going into lower priority projects!

By definition, the concept of alignment suggests that the resources of the organization should be expended on project work in proportion to the value each project provides to the organization. If you are spending (investing) more time, effort, and money in lower priority projects, then in practice, where the rubber hits the road, your project execution efforts are “out of alignment” with strategic objectives. A relatively simple and straightforward way to assess the degree to which actual work is being completed on “first things first” projects is to compute a correlation between projects as ranked by the prioritization drivers (the priority list), and projects as ranked by actual work completed during a given time period. A high correlation would suggest a high degree of alignment, and vice versa.

If we add a useful degree of statistical rigor to our concept of alignment, we use the “coefficient of determination.” The coefficient of determination is used after least squares regression with a constant linear model and equals the square of the correlation coefficient between the observed and modeled (predicted) data values (Bowerman & O'Connell, 2008). The coefficient of determination is a measure of “goodness of fit” for the estimated regression equation; it can be interpreted as the proportion of the variation in the dependent variable that can be explained by the estimated regression equation. In principle, Spearman's rho is simply a special case of the Pearson product-moment coefficient in which the data are converted to ranks before calculating the coefficient. Second, nonparametric procedures discard information. For example, if we convert severely skewed interval data into ranks, we are discarding the actual values and only retaining their order. Because vital information is discarded, nonparametric tests are less powerful (more prone to Type II errors) than parametric methods. This also means that nonparametric tests typically require comparably larger sample sizes in order to demonstrate an effect when it is present.

Because correlation is a descriptive measure of the strength of a linear relationship between two variables, the square of the correlation coefficient essentially tells us the proportion of variability in the rankings of projects by actual work that can be explained by the priority list rankings. In our case, we might think of it this way: we are essentially trying to “predict” the rankings of projects by actual work using the ranking of projects by strategic drivers. Thus, when it comes to project prioritization, the “coefficient of determination” becomes the “alignment coefficient,” and is simply the square of the correlation coefficient between the priority rankings and the actual work rankings. On a scale between 0 and 1, the alignment coefficient can be expressed as a percentage, so that it is easily and intuitively interpreted: an alignment coefficient of 0.80 simply means “80% alignment” as illustrated in Table 2.

Project # Actual Hours Worked Work Rank Priority Rank
20723 10,273.2 1 3
15070 7,041.0 2 1
20724 6,451.5 3 2
20721 4,990.0 4 4
20725 3,319.5 5 6
26466 2,358.3 6 5
10948 1,689.0 7 7
20344 1,406.0 8 10
29668 1,232.5 9 9
15000 1,213.6 10 8
=C0RREL(D3:D12, E3:E12) Correlation 0.9030
[square the correlation] Alignment Coefficient % 82%

Table 2: Illustration of how to compute an “alignment coefficient.”

In this example, the total number of hours actually worked on each project is summed; then projects are ranked from highest to lowest according to how much actual work has been completed. The original priority list rankings are included, and a simple spreadsheet formula is used to compute the correlation. Squaring the correlation and rendering it as a percentage creates the alignment coefficient, and tells us that the actual work completed on this set (portfolio) of projects is in “82% alignment” with strategic objectives. It is important to remember, however, that not all projects are the same size, and actual hours worked on a larger, but lower priority project, may place it ahead of a smaller, higher priority project in the calculations. As such, by simply adjusting actual hours worked for the size of the project gives us comparisons that make even more sense—project #2 may be the largest project in terms of hours of effort estimated and, therefore, may have more hours reported against it than project #1, which is an overall smaller project, but a higher priority. Thus, we simply divide the total number of hours reported on each project for a period by the total estimated number of hours for the project (a measure of the “size” of a project), then rank the projects according to the ratio of hours worked to the size of the project. Because we are computing the correlation and the alignment coefficient based on the rankings and not on the priority scores or the actual hours/ration of hours worked, these transformations do not affect the alignment coefficient and its interpretation.

The interpretation of the alignment coefficient is intuitive and meaningful. As shown in Table 2, project #20723 had the most hours worked, yet was ranked only third in priority, whereas the highest ranked project (#15070) had almost 3,000 fewer hours invested in it during the time period under consideration. In itself, this situation may not be cause for alarm. An alignment coefficient of 82% is quite good (based on consulting experience), and can be interpreted not just as “percent alignment,” but it more specifically tells us that for every unit of effort that should be invested in “first things first,” according to the priority criteria, 82% is “aligned” with strategic objectives. If we put a dollar amount on these work units, the interpretation is straightforward, suggesting that for every dollar we have spent (on actual work), US$0.82 was “aligned” with strategic objectives. In evaluating performance on an entire portfolio of projects, such a metric is comparable to the cost performance index (CPI) in earned value management (see PMI's Practice Standard for Earned Value Management, 2005).

A powerful use of the alignment coefficient is in tracking alignment over time, perhaps as a simple, quantitative measure of the performance and value of a PMO. Because the alignment coefficient can be interpreted as a “percent alignment,” increases or decreases in the alignment coefficient can quantify progress and improvement in an organization's overall project portfolio capability and maturity. Strategic goals can be set and measured for an organization and its PMO to achieve, for example, “10% improvement in project alignment by year end.” As illustrated in Figure 2, a company began tracking alignment at the beginning of a year. Early on, it was clear much work was being done on projects that were not high on the priority list. As this work was re-directed to higher priority projects, the alignment coefficient regularly improved, with the exception of the month of May, when a number of compliance projects required work even though they were not highly strategic.

The coefficient of determination (r-squared) is the square of the correlation coefficient. Its value may vary from zero to one. It has the advantage over the correlation coefficient in that it may be interpreted directly as the proportion of variance in the dependent variable that can be accounted for by the regression equation. For example, an r-squared value of .49 means that 49% of the variance in the dependent variable can be explained by the regression equation. The other 51% is unexplained.

Alignment coefficient showing progress over time

Figure 2: Alignment coefficient showing progress over time.

Conclusions

In tough economic times, organizations can ill-afford to waste precious time, money, and effort on work that fails to strongly align with the corporation's strategic objectives. Although prioritizing projects is essential, the “knowing-doing gap” (Pfeffer & Sutton, 2000) between what organizations “know” they “should” be doing, and what actually gets done, can be costly without a way to measure and track “alignment” over time. This study contributes to research and practice in several ways. This is the first use of the “rho-squared coefficient” as an “alignment coefficient” that is parsimonious, intuitive, and elegant. It is a metric designed to help provide insight into the degree to which actual project work aligns with corporate objectives as manifest in priorities. Thus, this coefficient is ideally suited to provide a measure of vertical alignment from the top of the organization's strategic plans, down through to where the “rubber hits the road.” Moreover, the alignment coefficient is simple to compute, intuitive to interpret, and provides another way to quantify and track the contribution PMOs can make to organizational productivity and strategic execution.

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©2012 Project Management Institute

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