Strategic alignment of projects

Abstract

Each organization was found to fulfill a major aim defined in its strategy and achieved through its projects allocated within its portfolio, from which the importance of projects' alignment with strategy arises. A lot of alignment models exist, but they have many gaps resulting in inaccurate results.

This paper resolves these gaps by creating a new model used at the starting process of a project's evaluation and selection, to guarantee undertaking only appropriate ones, and avoiding wasted investments. The newly developed model integrates both quantitative financials and qualitative strategic issues to be equally and concurrently evaluated and analyzed in one tool.

First the importance of strategic alignment is illustrated, and then the current models' gaps are explained. After an introduction to the basic structure of the new model, an explanation of its detailed components will be provided to clarify its new features for resolving current gaps.

Finally, the paper concludes with recommendations for future research aiming for further strategic improvements.

Introduction

The business environment is growing more complex and needs to include considerations other than just cost and profit. In addition, organizations tend to take on more than just one project, since they have more labor, larger capital, more diverse skills, a variety of products and services, and some even work in more than just one industry. For these reasons, strategic alignment of projects and their evaluation and selection process become more complicated and require more updated analytical and managerial skills and models than before.

Current models contain many flaws. The major one is the separation of quantitative financials from qualitative strategic issues, where each is evaluated and analyzed in isolation, resulting in an unbalanced weighting system favoring financials over qualitative strategic issues, which widens the distance between project alignment and strategy. The new model introduces a new tool that integrates both financial and strategic issues by evaluating and analyzing them concurrently in equal weighting.

A simulated numerical case study is presented for further clarification of the method's usage and application. The simulated case study will also demonstrate new features within a new tool which combines four functionalities into one to replace the usage of four different tools. This finding is proved and tested using mathematical calculations and derivations explained in the paper.

Another added feature is the creation of a process integrating strategy and portfolio into one using merging techniques. These merging techniques rely on the organizational structure, culture, resources, and strategy formulation processes. The issue of analyzing projects individually without considering their overall impact on companies' strategies is addressed and resolved as well.

The paper includes real life examples of failed projects, in addition to simulated implementation codes and testing methods with new observations, in order to maintain the reader's involvement.

The Importance of Projects' Strategic Alignment

Many corporations are suffering from project failures, reaching up to billions of dollars in losses. A global research report done by BIA Canada showed that the major reason for these failures is the misalignment of projects with organizational strategy (Stanleigh, 2010, para. 1).

One of the most profound examples for such failed projects is the Royal Dutch Shell in their Siberian liquefied gas facility, Sakhalin Energy, that had a cost doubling from $US10 billion to US$20 billion due to lack of the project's strategic alignment. (Stanleigh, 2010, para. 2)

The report also found that 68% of organizations worldwide had no techniques for project prioritization or linking tools to corporate strategy. (Stanleigh, 2010, para.7)

Another survey done by PricewaterhouseCoopers in 2004 that included 10,640 projects from 30 countries with a total value of $US7.2 billion, showed that only 2.5% of projects achieve 100% success due to their right strategic alignment (Stanleigh, 2010, para. 3).

A new study done by Calleam Consulting Ltd. on more than 70 failed large projects (Calleam, 2014a, para. 2), such as “787 Dreamliner” Boeing Project in 2013 that had a cost increase of $US18 billion above the original $5 billion (Calleam, 2013, para. 2), and the “The Promise” projects for Avon in 2014 that had a cost increase from $US100 million to $US125 million (Calleam, 2014b, para. 2), also revealed that one of the major mistakes causing their failure was the misalignment with strategy, where there was a failure to understand what these projects are really trying to achieve. Their vision and goals were archived without being used for subsequent decision making (Calleam, 2014b, para. 3–6).

According to Tony Grundy, two major reasons are contributing to this loose link among projects and business strategy. The first is that corporate strategy is not known to those who are at the project level, since top management hides it for commercial sensitivity and want to maintain their power. In addition, project managers still don't consider it important for them to know the detailed business strategy. The second reason is that the strategy itself is not clear and its ideas are not integrated or consistent (Grundy, 2001, p. 16).

Hence, the increasing importance of projects' strategic alignment has arisen. This has necessitated considering project management as a means for strategy implementation, shifting the relative importance of strategy to project management from a 90:10 to at least 50:50 concern (Grundy, 1998, p. 43).

Hence, project management needs to be altered from the traditional notion of only delivering on time and on budget to become a process for strategic change; this requires including more complex interdependent and less tangible qualitative factors (Grundy, 1998, p. 43).

All of the above requirements needed to achieve strategic alignment are done at the start in the evaluation and selection process, and hence this process derives its significance from being the place for this achievement.

Existing gaps in current models used for projects evaluation and selection

Many current models exist, but they share similar characteristics and concepts, and hence they have the same gaps and weaknesses that reduce the strategic alignment efficiency.

Current models consist of multiple steps for handling the alignment process, with a minimum of eleven steps, and sometimes more, which add more complexity to the process and increases the possibilities for errors and mistakes. In addition to the multiple steps, a lot of tools are used during these steps, and many of them are duplicated in their usages, with each tool performing just one discrete and isolated functionality.

Handling of the portfolio processing is also separated from project data processing, where each is performed alone with no integration, having separate models with separate steps and tools for each.

The major critical flaw found in all these models is the separation of qualitative strategic issues from quantitative financial considerations, where there is no technique available for unifying them into one integrated process that requires quantifying the qualitative strategic issues.

In addition to separating portfolio handling from projects handling, portfolio handling is also separated from business strategy handling by dealing with each in isolation, then trying in a disconnected technique to combine them in an inefficient input-output relationship.

Another separation exists in the handling process of constraints; again, each is processed alone without regard to the aggregate impact for all of the constraints together. Projects and constraints combination is also missing, which would hide the constraints' impact on projects.

Projects are separated as well in their analysis, where the benefit of each is studied alone, thus losing the required projects' synergy that create the maximum collective benefits all together, and not by individual benefits.

Basic Scope and High Level Structure of New Model

Exhibit 1 below illustrates the basic scope of the whole new proposed model discussed in the paper.

Exhibit 1: Basic Scope and Structure (High Level Structure of New Model)

Strategy and Portfolio Integration, the Business Strategy Portfolio Alignment Model

As mentioned previously in the gap analysis in existing models, portfolio handling is separated from business strategy handling by dealing with each in isolation, then trying in a disconnected technique to combine them in an inefficient input-output relationship.

For this reason, and in order to solve this strategy-portfolio separation, there is a need for creating a model achieving this integration goal. This newly developed model is called the Business Strategy Portfolio Alignment (BSPA) model.

In order to create this model, an explanation of the stand-alone business strategy model is explained, and the same is performed for the portfolio management stand-alone model. The two models are then merged into one to create the new BSPA model.

Business Strategic Management Model

The major business strategic management model consists of four stages, which are: Environmental Scanning (Strategic Analysis), Strategy Formulation (Planning), Strategy Implementation (Execution), and Evaluation and control (Wheelen, 2012, p. 62).

This paper will focus on the strategic alignment during the scanning and formulation stages. Re-evaluation of selected projects during the execution and controlling phases is out of this paper's scope.

Environmental Scanning (Strategic Analysis)

Before an organization can start any strategic actions or make any decisions, its external and internal environments should be scanned and analyzed, because this enables identification of the strategic factors existing within these environments, and the specification of the strategic position of the firm among others in the market (Wheelen, 2012, p. 64).

The environmental scanning incorporates a SWOT analysis, where the strengths and weakness (SW) are scanned through an internal analysis of the organization's structure, culture, and resources, and the opportunities and threats (OT) are scanned through an external analysis, which is done through three environments: the natural physical, the societal, and the task (industry) environment. Exhibit 2 summarizes all the environmental scanning elements (Wheelen, 2012, p. 64).

Exhibit 2: Environmental Scanning and Strategic Analysis Model

Strategy Formulation (Planning) Process

Strategy formulation defines four sequential basic entities, which are: the mission, objectives and goals, strategies and plans, and policies (Wheelen, 2012, p. 51).

An organization starts by setting a vision, which is the future desired image of the organization. It should be achievable, since it will guide the organization for its fulfillment. The mission is the means by which the vision is achieved. It defines the activities, the basic roles to engage with, and specifies what the organization does and why it exists (Wheelen, 2012, p. 65).

Objectives and goals define how to achieve a mission, and what needs to be done. Goals are not time bound, and they are formulated at the corporate management level, while objectives are time bound, used to achieve goals, and are formulated at any organizational level (Wheelen, 2012, p. 66).

Objectives should be optimal, compatible with resources, and can be achieved by using them in association with time needed for accomplishment. Objectives should also be easily quantified, and based on research (Wheelen, 2012, p.61).

Strategies and plans define how objectives can be achieved. Each organization has three basic levels: the corporate level, the business level, and the functional level. At each level, different types of strategies are analyzed, evaluated, and selected. At the corporate level, there are the stability, growth, and retrenchment strategies. At the business level there are the overall cost leadership, the differentiation, and the focus strategies. And at the functional level, there are the financial, operations, human resources, purchasing, marketing, logistics, IT, and R&D strategies (Wheelen, 2012, pp. 67–68).

The last entity is the policies which are the actions and decisions that aim at facilitating the implementation of plans and strategies, such as using a new technology, training, motivation, etc. (Wheelen, 2012, p. 69).

Portfolio Management Standards

The Project Management Institute (PMI) developed a guide to a portfolio management standard highlighting the major tools and techniques to be used. The Standard for Portfolio Management – Third Edition defines three iterative non-sequential process groups: the Defining, Aligning, and the Authorizing and Controlling, and five Knowledge Areas: the Portfolio Strategic Management, the Governance Management, the Performance Management, the Communication Management, and the Risk Management (PMI, 2013, p. 31).

As for the part concerned of this paper's scope, the evaluation and selection process, only a number of selected processes will be involved and discussed from the first three Knowledge Areas: the Portfolio Strategic Management, the Portfolio Governance Management, and Portfolio Performance Management.

The Defining and Aligning process groups for the three selected knowledge areas are all included except for one process, the manage strategic change, since it is considered out of the paper's scope, so nine processes are going to be considered, which are:

Merging Business Strategic Management Model with Portfolio Management Standards

In the previous two sections, two major stages of business strategic management were presented, the environmental scanning and the strategy formulation, and then nine processes of the portfolio management standards were discussed as well. Each of these stages and processes has its own inputs, outputs, tools, and concepts, and many of them share a lot of these attributes, which would result in a redundant, more complicated, and longer method when using each in isolation. For this reason, the idea of merging both into one was created in order to eliminate any process duplication.

The merging process would analyze each selected stage process in both of the strategic management model and the nine selected processes portfolio management standards, categorize them according to their attributes, match their similarities and group them in order to integrate similar processes and tools, and create new intermediate interfaces unifying similar processes to be done simultaneously.

The detailed process of this merging technique is out of the scope of this paper, but the resulted high level model will be presented, which shows how both organizational strategy and portfolio management are done simultaneously in unified processes.

The Generated “Business Strategy Portfolio Alignment” (BSPA) Model

The merging process of both the business strategic management model and the portfolio management standards of the selected nine processes has resulted in the following model, which is named the business strategy portfolio alignment (BSPA) model, as shown in Exhibit 3 below.

Exhibit 3: The Business Strategy Portfolio Alignment, BSPA, Model

The New Tool of Combined Four Functionalities

For the process of projects evaluation and selection, four major functions must be performed, which are: analyze project data and constraints, interpret analysis results, select appropriate options, and then refine them.

In current models, for each function, a specific tool is used, resulting in using four different tools, causing the process to become longer and more complicated with redundancy.

A new tool is presented here which combines the four functionalities into one. The tool is further demonstrated using a simulated numerical case study. This tool relies on a special type of linear programming algorithm called the Ghasemzadeh and Archer model (Ghasemzadeh, & Archer, 2000, pp. 73–88). This model makes it possible to combine multiple projects with their multiple constraints, both the quantitative and qualitative, in addition to the organization's available resources that are set according to its unique culture, structure, weighting, and importance criteria.

This tool also enables the quantification of qualitative strategic issues, which then makes it possible to integrate them with the already quantified financial issues. This process is further clarified in the simulated case study

The result of this tool provides the best combination of projects that maximize synergetic benefit when they all are combined all together, and not just by each individual benefit.

Simulated Numerical Case Study: Case Description

An organization had planned its strategy, and had done market research for available new projects to be undertaken. In its strategy, the organization had defined its major quantitative and qualitative strategic issues and set their maximum capability value. These issues are considered constraints for the projects to be evaluated and selected (Hamdan, 2012, p. 21).

The quantitative constraints were the project cost and number of labor working hours. The qualitative constraints were the project priority, its quality level, risk level, historical information usage level, newly acquired experience level, communications complexity, and technology usage level (Hamdan, 2012, pp. 21–22).

Project cost and labor hours are already quantifiable and were set to maximum numerical value. Qualitative issues are then quantified in order to be integrated and used in the model. The quantification of the qualitative strategic issues (constraints) is set as the following: (Hamdan, 2012, pp. 21–22)

• Priority is either set to 1 or 0. If (1) then project MUST be included regardless of any other constraints.

• Quality is measured by the number of standards to be used.

• Risk is measured by overall score of possibility and impact.

• Historical information usage is measured by the number of data records used.

• Newly acquired experience level is measured by the number of new skills to be learned.

• Communications complexity is measured by the number of channels to be used.

• Technology level is measured by the number of devices to be used.

In this case study, the above constraints were set to their maximum value that should not be exceeded by the aggregated sum of the selected projects all together. These values were set as the following: Project cost: US$ 10 million, Labor hours: 2 million hours, Quality: 1,000 standards, Risk: score of 600, Historical information: 10,000 data record, Newly acquired experience level: 5,000 skill, Communications complexity: 10,000 channels, Technology level: 3,000 devices (Hamdan, 2012, pp. 21–22).

From the market research analysis done by the organization, it was found that there are 20 available projects that the organization can select from. For each of these projects, the quantitative and qualitative constraints mentioned above were collected and analyzed for each project from the 20 available projects (Hamdan, 2012, pp. 21–22).

Next, the formulation and implementation of the linear programming model that will combine all the organization's maximum values and the whole 20 projects data will be performed.

Linear Programming Formulation and Implementation

In the Ghasemzadeh and Archer model, the objective function is formed in such that each project value is either going to be 1 or 0. If the value is (1), then the project is selected, if it is (0), it's not selected. (Hamdan, 2012, p. 22, Ghasemzadeh & Archer, 2000, pp. 76–77).

Using LINGO software, a code will be written, including the objective function, the maximum constraints' values, and the constraints' formulas for all the 20 projects. For detailed coding, please refer to the article Strategic Selection of the Most Feasible Projects using Linear Programming Models (Hamdan, 2012, pp. 21–26).

The Results

After solving the numerical in LINGO, it gave the results of each project assigned a value of either 0 or 1. Projects with the value of (1) are going to be selected. In this case study, projects (A, G, H, J, L, M, and R) have the value of (1), so they will be selected as they are the best combination that satisfies the required constraints, while all the other projects with the value of (0) will be disregarded. (Hamdan, 2012, p. 25)

Uncertainty Refinement

For every tool used and results obtained, uncertainty always exists with error percentage. This uncertainty should always be regarded with care and handled, thus modifying the results accordingly.

Sensitivity analysis is the major discipline used for this refinement. The most accurate and common technique is the Monte-Carlo analysis and simulation, which is used when it is impossible or hard to define a closed form expression to be applied to the results. The Monte-Carlo analysis and simulation is a set of computational algorithms using a process of running repeated random samples many times to calculate the probabilities and results of these samples to reach the best fit probability distribution related to the required results.

In this paper, after discussing the new tool of the combined four functionalities, it is important to apply uncertainty refinement to it to guarantee its accuracy. So, Monte-Carlo was planned to be applied to the new tool, but for various reasons, it had some restrictions preventing this application. Hence, manual testing calculations for uncertainty refinement were performed on the new tool, which had led to exploring new results, observations, and findings. Next, we will discuss the trials of applying Monte-Carlo simulation on the new tool, the manual testing calculation, the results, and the new findings.

Restrictions for Monte Carlo Analysis Usage

As mentioned previously, the Monte Carlo simulation was planned to be applied on the new tool, but a number of restrictions prevented implementation.

First, for Monte-Carlo simulation to be applied on a tool's result, it first needs a direct input-output formula relationship used by the tool. In linear programming methods, there are no direct input-output formulas, especially in the Gazemzadeh-Archer model, where this type of analysis is done through several complicated steps using software and a direct one way formula. The second restriction is that in the Gazemzadeh-Archer model, the output is not a numerical result, but binary (0-1) data, which cannot be used for Monte-Carlo because it requires actual numerical data.

Monte-Carlo is always performed using specialized software due to its complexity even when handling simple formulas. The same applies for linear programming, even the simpler ordinary form of it. So even if the above two restrictions were non-existent, still there is no specialized interface code that could integrate both the linear programming complexity with the Monte-Carlo added complexity as well.

Manual Calculations Implementation and Results

As a result of not being able to apply the Monte-Carlo analysis or any other kind of sensitivity analysis on the new tool, manual testing calculations for individual variables was performed.

The manual testing was done on the same case study presented in this paper that has nine variables and 20 projects. For this case study, the number of possibilities is 9 to the power of 9, with each having unlimited variations, so to simplify the testing, up to two variables were tested.

The two variables resulted in a number of variations, where in each trial, an error percentage was applied to the variable, and then the software was run to obtain final results. From the results obtained, it was observed that when increasing the values of error percentages, there was no effect at all on the final result. But, when decreasing the values of error percentages, the uncertainty was covered and included up to 25%.

Observations Justification

For the above observations obtained from the manual testing calculations, an analysis and study were performed to justify and explain them. The analysis resulted in the following:

1) Linear programming already includes slack embedded in it.

2) Linear programming functions by finding the “Best Fit” solution.

3) Linear programming already uses iterations in its functionality.

4) Increased uncertainty has no effect because the constraint is already maximum value and won't even be over-crossed.

5) Slack is inserted in project data, for this reason, only portfolio data matters, and hence was tested.

New Finding

From the above justifications, it is obvious that linear programming has a shared functionality with the Monte-Carlo simulation or any other sensitivity analysis. Hence, the Gazemzadeh and Archer linear programming model already includes sensitivity analysis and uncertainty handling up to 25%

Current Model vs. New Proposed Model

All of the previous sections in this paper compromise part of the new proposed model for the strategic alignment of projects for the evaluation and selection process. So, to create the complete model, all these components are gathered and assembled to develop the final model, which is presented opposite to the current model in order to illustrate the differences and added benefits it has over the current one.

The current model starts with the organizational strategic management process performed by executives and top management to define the corporate strategy. The defined strategy is then transferred to the portfolio managers who use it in their portfolio plans.

The portfolio data are then submitted to further processing with various tools. The first process is the data analysis using a specialized analysis tool, and resulting in analyzed data. The analyzed data is then processed by an interpretation tool to obtain interpreted results. The interpreted results are then handled by a selection tool to get selected results, which finally get into an uncertainty refinement tool to acquire the portfolio criteria to be applied on the project's processing.

All the above steps have only covered portfolio handling so far without any inclusion of projects, which would start next with multiple steps as well. Now, after setting the portfolio criteria, the project's handling starts by applying these criteria to projects.

Upon these criteria, the projects' data are then analyzed using an analysis tool, resulting in analyzed data to be interpreted by an interpretation tool, which is then transferred to selection tool to filter selected projects, which finally have to go through an uncertainty refinement tool to get final decisions for the whole process.

From the above, the length, complexity, redundancy, and isolation handling is clear in the current models.

In the new model, all these gaps were resolved, where it starts with the new Business Strategy Portfolio Alignment (BSPA) model that already merges business strategic management with portfolio management to immediately generate the required criteria for projects evaluation and selection. The criteria is then applied to projects data, which then are subject to be processed with the new single tool that performs the four combined functionalities (analyze, interpret, select, refine) at once, and immediately getting into the final decision.

So, it is obvious how the new model eliminated the length, complexity, redundancy, and isolation handling found in the existing ones. An illustrative diagram of both models is presented in Exhibit 4.

Features of New Model

As demonstrated from the previous illustration, the new model has new features that fill the gaps in existing models. These features are:

Conclusion

The business environment is rapidly changing with very short life cycles. This creates a new burden on corporations to chase after these changes at a quicker pace, or to otherwise get out of the market. For this reason, lengthy and complicated processes causing decisions and implementation delays are no longer accepted, and the need for quicker more efficient techniques are highly required. This paper has highlighted a new proposed technique for filling this basic need in the hope of helping corporations keep up the required pace.

The model used here provides a quick method to select the optimal projects combination; still the responsibility of this model handling and implementation is divided among higher executives, portfolio managers, operations managers, and project managers, which creates a new requirement to coordinate and gather all these responsible people to apply the new model.

This is considered to be a new challenge for the project management office (PMO), and hence further research is required for the role of the PMO in implementing this model.

Exhibit 4: Current Model versus New Proposed Model for the Strategic Alignment for Projects for the Evaluation and Selection Process