Integration Management: Learning to see the Big Picture


Michal Iluz, Izack Cohen and Avraham Shtub

Faculty of Industrial Engineering and Management

Technion—Israel Institute of Technology

Haifa 32000, ISRAEL

Corresponding author: E-mail:

Tel.: +972-4-829-4454, Fax: +972-4-829-3172







Integration Management: Learning to see the Big Picture



We developed and tested a teaching methodology for Project Integration Management. The PMBOK clearly states the need for such a methodology: "The project management processes are usually presented as discrete processes with defined interfaces while in practice they overlap and interact in ways that cannot be completely detailed in the PMBOK Guide". We report the results of a controlled experiment where Simulation Based Training (SBT) was used to train students and practitioners in Project Management. Results indicate that the participants evaluated the SBT environment highly and many of them even indicated that they would use it for decision support in their real projects.



Key words

Project Management, Simulation, Training, Integration Management, "Project Team Builder (PTB)"












1.         Introduction

Project managers face complex decisions on a daily basis. In order to meet or exceed stake holders' needs and expectations, project managers must plan their projects and keep them on track in uncertain and dynamic environments. Many decisions must take into account several knowledge areas simultaneously and the impact of the uncertain, dynamic and constrained environment on the interactions among these knowledge areas. Although the Body of Knowledge used as a basis for project management may define and illustrate each knowledge area, the need to integrate the different project aspects into one "big picture" presents a real challenge. The Project Management Body of Knowledge (PMBOK, 2008) defines this need for Project Integration Management: "Project Integration Management entails making choices about resource allocation, making tradeoffs among competing objectives and alternatives, and managing the interdependencies among Project Management knowledge areas. The project management processes are usually presented as discrete processes with defined interfaces while in practice they overlap and interact in ways that cannot be completely detailed in the PMBOK Guide". While most of the tools and techniques listed in the PMBOK are well defined and relatively easy to teach, it is extremely difficult to teach how to make tradeoffs among competing objectives and alternatives, and how to manage the interdependencies among Project Management knowledge areas.

Integration Management is a continuous effort performed throughout the project life cycle in a dynamic (ever changing) and stochastic (subject to uncertainty) environment.

This paper presents a Simulation Based Training (SBT) for teaching Project Management. The specific tool used for this research, the "Project Team Builder (PTB)", won the Project Management Institute (PMI) Product of the Year Award in 2008 and since then has been used as a research lab and teaching tool in several leading universities throughout the world.

This study focuses on teaching students and practitioners how to plan, execute, monitor and control projects in a dynamic-stochastic environment. A controlled experiment in which students and practitioners managed a simulated version of a real project is reported along with statistical analysis of data collected before and after the experiment.

In the next section we discuss Simulation Based Training (SBT) and its use for training in different areas. In Section 3 we describe the Project Team Builder (PTB) and its use for training project teams and project managers. Section 4 details the experiment design and Section 5 reports its results. In Sections 6 and 7 we check our hypotheses and draw conclusions, respectively. The final section summarizes the research and offers future research directions.

2.         Experiential Learning and Simulation Based Training (SBT)

Experiential learning tools that integrate the traditional case study approach with computer simulation are based on the assumption that training simulators are an efficient way of gaining, assimilating and retaining new knowledge, i.e., learning by doing (Dempsy et al., 1997). The term Simulation Based Training (SBT) is frequently used to describe the application of simulators for training. Reports on the application of SBT in different areas are published frequently.

Simulation Based Training, as defined by Salas et al. (2009), is a "synthetic practice environment that is created in order to impart these competencies (i.e., attitudes, concepts, knowledge, rules, or skills) that will improve a trainee's performance."

Simulation Based Training is used in diverse areas: medicine (Park et al., 2007; De Giovanni et al., 2009; Fraser et al., 2009); driver training (Ivanic et al., 2000); Production Manager training (Ruohmaki, 1995; Shtub, 1999); and, pilot training (Rolfe & Staples, 1988).

The literature describes several Project Manager training simulators (Pinto & Parente, 2003; Steele, 2009; Ioana & Collofello, 1999; Pfahl et al., 2001; Vanhoucke et al., 2005; Davidovitch et al., 2006, 2008, 2009, 2010).

One reason for the popularity of SBT is its perceived advantages over other training methods. Salas et al. (2009) list the advantages of SBT for management education:

1.      SBT is superior to other training strategies for imparting complex applied competencies.

2.      SBT can lead to learning in a reduced timeframe.

3.      SBT provides a more complex and realistic learning environment than other training strategies.

4.      SBT more rapidly allows for reality to be simplified and manageable.

5.      SBT provides a (relatively) risk-free environment for learning and experimentation.

6.      SBT is an ideal method for training infrequently engaged but critical skills.

7.      SBT can be quite affordable.

8.      SBT is (usually) simple to learn and operate.

9.      SBT is a form of learner-controlled training.

10.  SBT is inherently more engaging than other training methods.

This article presents research findings on the application of SBT to project management in a controlled environment. The simulator used in this study – the Project Team Builder (PTB) – is described in the following section.   

3.         PTB - Project Team Builder

The Project Team Builder (PTB) is a training aid designed to facilitate the training of project managers in a dynamic, stochastic environment. It is based on the following principles:

·           A simulation approach—PTB simulates one or more projects. The simulation is controlled by a simple user interface and no knowledge of simulation or simulation languages is required.

·           A case study approach—PTB is based on the simulation of case studies. Each case study is a project or a collection of projects performed under schedule, budget and resource constraints in a dynamic stochastic environment. The details of these case studies are built into the simulation and all the data required for analysis and decision-making is easily accessed through the user interface. A user-friendly case study generator facilitates the development of case studies based on real or imaginary projects.

·           A dynamic approach—PTB uses case studies that are dynamic in the sense that the situation changes over time. A random effect is introduced to simulate the uncertainty in the environment and decisions made by the user cause changes in the state of the system being simulated.

·           A model-based approach—PTB has a built-in decision support system. This system is based on Project Management concepts. The model base contains well-known models for scheduling, budgeting, resource management and monitoring and control. These models can be consulted at any time.

·           A decision support approach—PTB has a built-in database. Data on the current state of the simulated system is readily available to the users. Furthermore, it is possible to use the data as input to the models in the model base to support decision-making.

·           An integrated approach—the different aspects of the project are integrated and tradeoffs are present between the different knowledge areas. For example, a change in the mode of operation for one activity may impact one or more of the following: the required resources, the cost, the duration, the level of risk and the performances of the resulting deliverables.

·           A user friendly approach—PTB is designed as a teaching and training tool. As such, its Graphic User Interface (GUI) is friendly and easy to learn. A typical user learns how to use PTB in less than an hour.

·           Integration with commercial project management tools—PTB is integrated with commercial project management software so that the users can analyze the scenario on the commercial project management software and support decisions with tools that are actually used in the users’ organizations.

·           Scenario flexibility—PTB separates the simulation engine from the scenario library. The result is flexibility provided by a scenario building program with which the trainer or teacher can build project scenarios based on real or imaginary projects. Although PTB contains a scenario library, the teacher or trainer has access to the scenario builder and can build any number of scenarios based on the training objectives and the required learning outcomes. The scenario builder can import real projects from commercial project management software and transform the information into a project scenario for the simulator.

·           A built-in learning history recording and inquiry mechanism—the user has access to past situations and decisions in the simulation and to the consequences of these decisions.   

In PTB the user decides when and how to perform each task – decisions that affect the project cost, schedule, and robustness as well as the performance of the system delivered by the project. PTB also enables project monitoring and control during the simulated execution phase of the project. PTB can be used as a stand-alone system as it contains models for scheduling, budgeting, resource management, cash management, monitoring and control. It can also be used with commercial project management software such as Microsoft Project, if training in the use of such software is part of the desired learning outcome.


4.         The Experiment

To study the contribution of SBT to the ability of trainees to integrate the different aspects of project management, a controlled experiment was designed. Project managers, systems engineers and graduate students participated in the experiment. All participants used the same simulator version running the same scenario. The scenario used in the experiment is based on a real project – the development and production of an airborne communication system. The project was presented to the participants and discussed by the (actual) project manager along with the real project plan in Microsoft Project.

In the simulated project the goal is to maximize the benefit of the newly developed airborne communication system while minimizing the cost and the duration of the project. A clear trade off exists between these three objectives and participants could impact the results by deciding what technologies to select and how to allocate resources to project activities.

Prior to the training session and at the end of the simulation session, participants filled out a questionnaire focusing on learning and decision making with PTB.    

4.1     Scenario Description

The scenario used is based on a real project:

-          The air force decided to replenish the communication systems in airborne platforms and in ground stations.

-          The new communication systems should have all the capabilities of the currently used system as well as some new capabilities.

The system block diagram is presented in Figure 1.









Figure 1: System block diagram

4.2       System Requirements

·         BW (Bandwidth) = 5MHz

·         Minimum SNR (Signal to Noise Ratio) = 4dB

·         System NF (Noise Figure) = 15dB

·         Frequency = 50 MHz

·         Required range for communication route R (Range) = 100Km

·         Volume = 6000 cm3 (30x20x10)

 Schematic structure of the system is presented in Figure 2, where

NF – noise

Pt – transmitter power


Figure 2: Schematic structure of the system


The PTB calculates the system's performance based on the user's decisions. The total benefit score is calculated as a weighted sum of the scores for each requirement. Each requirement is a function of one or more technological decisions and the user’s objective is to trade off the desired values of the requirements against minimizing cost and duration. Figure 3 depicts the data on system requirements presented to the user:


Figure 3: PTB system requirements screen

After the user has made all the decisions, a project plan is generated and presented in the form of a Gantt chart. The Gantt chart of the project as shown by the PTB simulator is presented in Figure 4.

Figure 4: PTB Gantt chart screen


4.3       The Participants

Three groups participated in the experiment: 16 very experienced project managers (with over 5 years of experience), 17 experienced project managers (with less than 5 years of experience), and 18 graduate students (taking a graduate course in Systems Engineering). Some participating project managers work as systems engineers. The very experienced group included 12 men and 4 women aged 48 to 65 with an average age of 57. Figure 5 illustrates the level of experience of the very experienced group.

Figure 5: The experience of the very experienced project managers group


The experienced project managers group included 12 men and 5 women aged between 28 and 62 with an average age of 46. Figure 6 illustrates the level of experience of this group.

Figure 6: The experience of the experienced project managers group

The graduate students group included 17 men and one woman aged from 29 to 47 with an average age of 37.



5.         The experiment's Results

The following legends are used to represent the three groups of participants:


*                  Very experienced


5.1       Simulation Results

Project duration: As seen in Figure 7, the average duration of the simulated project decreases as participants’ experience level increases.

Figure 7: One-way analysis of duration by participants’ experience level

Project profit (minimizing project cost is identical to maximizing its profit): As seen in Figure 8, the simulated project’s profit decreases as participants’ experience level increases; therefore, the simulated project’s cost increases as participants’ experience level increases.

Figure 8: One-way analysis of simulated project’s profit by participants’ experience level  

There are three clusters of compliance with performance: low (benefit under 20,000), moderate (benefit between 20,000 and 80,000) and high (benefit over 80,000) where the benefit is calculated by PTB as the weighted sum of the score of the communication system delivered by the project groups. A one-way ANOVA was performed in order to test the differences between the clusters. 97.4% of the variance is explained by the performance conformance distribution. In other words, almost all of the variance is explained. As can be seen in Figure 9, no clear relationship appears between the participant groups and the benefit level achieved in the simulation.

Figure 9: Clusters formed according benefit results

This result is reinforced using the K-means Analysis Statistical Toolbox in Matlab:


Figure 10: The three clusters formed according benefit results

The K-means analysis shows the three clusters formed according benefit results.




6.         Hypothesis testing

We tested several hypotheses regarding participants’ performance measured by benefit

Hypothesis 1: As performance level increases, cost increases.

Result: There is a significant correlation between performance and cost.   (Chi Square = 5.99, df=2, P<0.05): the better the performance, the higher the cost.

Figure 11: One-way analysis of cash by benefit group

Hypothesis 2: There is a positive correlation between the time it takes to run the simulation and the level of compliance with performance.

Result: A significant (F=4.46, df=2, P<0.05) positive correlation between the time it takes to run the simulation and the level of confirmation to performance. The more time invested in simulation training, the higher the performance levels.


Figure 12: One-way analysis of run time by benefit group


Hypothesis 3: The delivery of a system fully complying with all requirements result in higher project costs and lower profit.

Result: There is a significant positive correlation between the level of compliance and the project cost (F=7.14, df=2, P<0.05). Participants who delivered a fully compliant system completed the project at a higher cost (cost increased by almost 50%) than project managers who delivered a less compatible system.


Figure 13: One-way analysis of profit and compliance with performance

7.         Observations from Questionnaire Data


As discussed earlier, pre - and post- simulation questionnaires were used to collect data. Analysis of the data generated the following observations:


Observation 1: The duration of the simulation (simulation run time) and assessment of the simulator contribution to learning project management: as the average duration of simulation run increases (a longer training period with the simulator), so does the positive perception of its contribution (on a scale from 1 to 5, see Figure 14).

Figure 14: One-way analysis of run time by rate of simulation training contribution

Observation 2: There is a positive correlation between the performance level using the simulator and a positive evaluation of the simulator as a tool for training in Project Management. As performance level increases, so does the positive evaluation of the simulator as a tool for training in Project Management.


Figure 15: One-way analysis of benefit by rate of simulation training contribution


Observation 3: The declared wish to integrate the simulator with Microsoft Project (the software used to manage the real project) in the future for managing real projects increases with the evaluation of the simulator contribution. We found that as participants’ positive evaluation of the simulator increased, their declared intention to integrate it with the Microsoft project tool consistently increased (F=8.04, df=4, P<0.05).

Figure 16: One-way analysis of intention to combine Microsoft Project with the simulator in future projects by rate of simulation training contribution


Observation 4: More than 60% of participants feel that the SBT tool supports decision making. In addition, 70% believe that using the SBT tool before or while managing a project can support the decision making process.


Figure 17: Frequency of replies to the statement: From my experience with other tools, using the simulator helps in making decisions



Figure 18: Frequency of the replies to the statement: Use of the tool prior to or during the project may assist decision making in the project





The results of this study indicate that SBT can help in training and teaching Project Integration Management in a dynamic stochastic environment. SBT can be used for training project managers with different levels of experience as well as students learning the theoretical aspects of Project Management. Furthermore, we observed that SBT tool users think that if it can run simulation scenarios that are based on a real project, the tool can support project managers in the actual management of projects.

Our results point out that it may be valuable to train project teams using SBT approach while focusing on the real project assigned to the team. By simulating a scenario based on the real project, the team can learn how to integrate the different aspects of Project Management. More importantly, the very same learning process can improve teamwork aspects by training the team members in working and solving problems.

The design of SBT tools and their usage for training and decision support is a complex challenge, motivated by our results that indicate the potential for improved training, learning, and   most teamwork.



















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