Make sure you have regular project review meetings where you discuss project objectives and milestones to keep your stakeholders informed and consulted. Walk or dial into these meetings with a professional timeline, Gantt chart or project roadmap so that everyone has a clear overview of how the project is doing. Use templates to save precious time when creating project visuals for your next stakeholder meetings. Office Timeline helps you quickly turn complex project data into clear PowerPoint graphics that are easy to follow, but hard to forget.
The right template is a mix of various ingredients and there are many aspects to take into consideration: your industry, company size, or project complexity. You can access these templates as Excel and PowerPoint files and customize them to fit the specific needs of your project. For the PowerPoint templates, you can edit them manually by moving the shapes or use the Office Timeline Pro add-in to edit and update automatically.
It plugs natively into PowerPoint so you can make your project visuals with just a few clicks and save them as your own personalized templates to use time and time again. Get Template. Office Timeline is an award-winning project management tool for PowerPoint.
Login Products Office Timeline Add-in. Download Free Edition. While the model is extremely useful as is, there are some possible improvements that suggest themselves.
It seems reasonable that the model could be expanded to a program level, either by creating a model based on complexity drivers at the program level or by aggregating the complexity scores from the sub-projects that make up the program. In the first case, a program level model, it could prove difficult to gather meaningful historical data to validate the model and leverage the complexity score for predictive modeling.
Careful thought should be given to scenarios where many projects are dependent upon each other and just how that interdependence is expected to drive complexity at both the project and the program level. Last, we believe that this complexity modeling technique could be employed with projects that follow an agile methodology. Since the calculation of the complexity score is done at a high level, it does not depend on the specifics of the product backlog. Therefore it seems logical that it would be possible to calculate a complexity score early in the project even though the actual sprint backlog and stories are unknown.
Correlation of the complexity score with the total number of sprints needed to complete the project would forecast total project duration for a well-established agile team with a consistent and stable velocity.
No work in this space has been done to date, but the possibilities as an early forecasting tool warrant further research. Today project work is increasingly more complex and ambiguous. The complexity model discussed in this paper allows project scoping teams to quickly and accurately predict resource requirements and expected project duration. The model is straightforward to build, utilizes the expertise of SMEs, leverages historical project performance data, and most importantly is very quick and easy to use.
We not only generate a high confidence estimate for resourcing and overall duration, we have a much clearer and deeper understanding of the new opportunity. In the past, this work was typically done by a handful of SMEs in a random manner with mixed results and limited understanding of the work transmitted to the rest of the team.
We have found this tool to be powerful and integral to our pipeline management and are pleased to share this modeling technique with our PMI colleagues. This in-depth case study outlines a project to increase productivity with Saudi Arabian public petroleum and natural gas company, Saudi Aramco.
Article Innovation , Complexity 1 October Verreynne, Martie-Louise The time-bounded nature of large interfirm projects and technical interdependencies constrain innovation. Article Complexity , Decision Making 1 October By Brokman-Meltzer, Mor Perez, Dikla Gelbard, Roy Perceived complexity is a factor when project managers adopt suboptimal work plans, even when optimal plans are readily accessible.
White Paper Complexity 21 August By Wiewiora, Anna O'Connor, Peter This training program provides tools, recommendations, and actioanble strategies to assist project managers in dealing with complexities and ambiguities in their projects. Learning Library. How Intel models project complexity … A model you can actually use. This complexity model is based on known drivers of project effort and duration, backed up by historical data.
This paper will also include specific considerations, calculations, and scoring techniques for building the model. Abstract Teams at Intel are using a relative model to assess the complexity of new projects. Introduction As the world we live in becomes increasingly faster, smaller, and more interconnected, projects have become more complex and by extension more ambiguous. Constructing the Model The objective for creating this model was to be able to compare and contrast the effort required to complete unrelated projects.
Identifying the Complexity Drivers To begin constructing the model we first worked to identify what project characteristics correlated to significant effort. Developing The Relative Scale Developing the relative complexity scale was much more difficult to do and required several steps.
Step 1: Group the Characteristics into Logical Buckets After the brainstorming and interviews with the SMEs, the identified characteristics were bucketed into high-level groups such as General, Code, Validation, and Release. Exhibit 3 — Complexity scoring range expressed in natural language. Exhibit 4 - Complexity score correlated to project duration, effort, and resources.
This material has been reproduced with the permission of the copyright owner. Unauthorized reproduction of this material is strictly prohibited. A statistically significant relationship was found between an increase in overall complexity rating and greater challenge for schedule, budget, and scope; however, no statistically significant relationship was found between overall complexity rating and delivery of business benefits. Based on the pilot analyses, the Project Complexity Model exhibits moderate applicability in the workplace.
Therefore, the complexity model and instrument should be revised to improve workplace applicability and achieve higher reliability and validity estimates. Future research and model testing should use larger industry-specific participant groups for comparison and should continue to evaluate the impact of project complexity on project results and how to integrate complexity management into traditional project management approaches.
It is clear that projects are becoming more complex and that complexity matters. Project complexity is related to project performance outcomes; however, project and program managers may not be applying the advanced techniques necessary to be successful. Therefore, the project management industry is correct in pursuing a higher level of complex project management maturity.
The study indicated that the model may be useful, but is not yet comprehensive and complete. Based on the research findings, the model has been recalibrated. The key improvements to the Project Complexity Model 2. Ambler, Scott W. Agile Analysis. Chang, Alicia. Competency Standard for Complex Project Managers, Public Release Version 2. Donlan, Thomas G. Lesson of the Chunnel. Hass, K. Gelinas, Nicole. Lessons of Boston's Big Dig.
Kotter, John P. Getting to the heart of how to make change happen. Boston: Harvard Business School Press. Lippert, M. XP in complex project settings: Some extensions. Schweizerischer Verband der Informatikorganisationen. Communicating project management. Palmisano, Samuel J. Peteres, K. The Standish Group. Chaos Summary This in-depth case study outlines a project to increase productivity with Saudi Arabian public petroleum and natural gas company, Saudi Aramco.
Article Innovation , Complexity 1 October Verreynne, Martie-Louise The time-bounded nature of large interfirm projects and technical interdependencies constrain innovation.
Article Complexity , Decision Making 1 October By Brokman-Meltzer, Mor Perez, Dikla Gelbard, Roy Perceived complexity is a factor when project managers adopt suboptimal work plans, even when optimal plans are readily accessible. Resources and skills are key factors in the success of the project as well as how new technologies work when applied sometimes technologies are not mature enough to be implemented. Several studies confirm that iterative and agile methods project life cycles have more success than traditional approaches.
Therefore, the methodology used on project management must be present on any assessment of project complexity. From all the approaches used by the different recognized standards in project management, IPMA approach is the closest to a tool that can be used as a complexity quantitative measurement system, since it defines factors and suggests a measurement scale to measure the degree to which these factors affect the management complexity of the project.
While it is part of the project manager certification system, this tool is useful for measuring complexity in projects as it attempts to confirm that the project manager is capable of managing complex projects.
For the purpose of our study, a framework for IT project management complexity assessment is designed. This framework has as baseline the IPMA project management complexity assessment, adding or removing if necessary some complexity factors in order to build an assessment template for IT projects. The proposed factors to be included on the assessment of IT project complexity were extracted considering the literature review carried out in section before and taking into account the fact that IPMA complexity factors do not focus exclusively on the scope of IT projects but cover a wider range of projects.
To propose an assessment template in order to build a tool that measures IT project complexity taking into account the inherent complexity of these projects, first, some IT complexity factors that are not covered by IPMA assessment will be added to the baseline IPMA assessment knowledge and, then, this template was validated by experts. This tool was called Complexity Index tool because it will allow measuring the complexity level of a project at one point under a scenario of concrete project complexity.
This proposal was validated with a survey fulfilled by experts. The selection of the experts was an important issue. These experts were involved in the survey under the below channels: personal contact and social networks. We considered all the factors found in the literature as relevant factors in the complexity of IT projects see Table 2 , since all these factors were identified by several authors as inherent to the complexity of these projects.
All of them were included in the template developed to design the complexity assessment tool see columns 1 and 2 of Table 2.
The objective of the survey was to ask the experts to identify the main complexity factors that affect IT development projects. The sections below will describe more in detail the structure of the questions and their objectives.
Experts Profile Questions. Questions Related to Complexity Groups. Questions were raised about complexity groups, to find out those which were considered by experts as the ones which impacted the most the complexity of the project. The experts were asked about the groups in which new complexity factors were added.
These are objectives, requirements and expectations, interested parties and integration, leadership, teamwork and decisions, PM methods, tools and techniques, and technology. We considered that the other groups of complexity factors should be part of the tool since these are composed of complexity factors that affect any type of project. Therefore, questions related to complexity factors are, all, based on allowing practitioner to rank the factors within their complexity group and discard them if required.
These questions were used to find out which of these factors contributed most to the complexity within its group relative complexity. Of the total number of people that accessed the survey, 13 were not fully completed, so there were 37 responses in the end. Of these 50 responses, it was necessary to eliminate the thirteen partial answers since the study must be done with comparable items.
Profile of the Respondents. Most of the practitioners are from technology, banking, and financial services sectors see Figure 1. Only 1 respondent had 0 years of experience working on IT projects, since it was an end user, the answer was considered valid for the study.
The results in terms of level of expertise suggest that the experts were well qualified. The remaining percentage of experts is part of projects with a more technical and business oriented role.
After screening the profiles, we selected all the experts to participate in the survey. Complexity Factors Results. In this part, the specific survey questions related to complexity factors were analyzed. Table 3 shows the information of the percentages of the total of the answers and the average column that was used to compare between complexity groups. These average ratings showed the relative importance of each complexity group to the experts. Questions groups of factors.
Similar analysis was made within each complexity group in order to classify the factors that make up each group. Most of the new IT complexity factors suggested are in the first half of the relative complexity ranking.
As shown in the analysis of survey results, there are no factors considered out of the scope of the Complexity Index tool. Table 7 shows together all the factors studied in the survey and ranked by level of complexity.
In Table 7 the new IT complexity factors proposed are shown in italics. From the results obtained in the survey it can be concluded that all factor groups and all factors within each group should be included in the complexity measurement tool, since the experts thought that they are factors that affect the complexity of IT projects.
The tool was designed taking into account all the new complexity factors extracted from the literature and validated by experts. A table with all the factors to be included in the assessment tool is presented as shownin Table 8. The next step to develop was how to really measure the complexity based on the items shown in Table 8.
Following IPMA framework, complexity is measured against that of similar projects in the singular professional environment of the project manager, scoring each complexity factor. This value was chosen because it is between low complexity 2 and high complexity 3 see Section 2. Thus, the minimum score obtained for the evaluation of any project considered complex should be This supposes Therefore, the Complexity Index tool is based on the same score to define the Complexity Index.
The new tool is focusing on the measure of the complexity of IT projects including new specific factors in calculation of the Complexity Index.
Complexity Index tool is based on the complexity groups and factors validated by the experts as a result of the survey see Table 8. The template proposes assessing the complexity groups according to the 4 defined levels of complexity, considering the level of complexity of the factors that make up each group. The sum of the normalized values of each complexity group provides the final score of complexity of the project under evaluation.
The next section will describe the functionality of the tool for the complexity group assessment. The example shown in Figure 5 shows how a user of the tool can measure the complexity of a particular complexity group.
At the same time that the user changes the values of the assessment of any complexity group, the graph will reflect the new Complexity Index value.
This study case is divided into two parts; the first one is the description of the taxonomy of the IT project with the objective of understanding what is the starting point of the assessment. The duration of the project was two years; thus two scenarios were considered: scenario and scenario slight differences between these will be recognized in Section 4. The second part is the application of the Complexity Index tool to the project in and status.
The assessment was performed by the project manager of this project during these 2 years, who led this project towards success despite the complex environment. The project analyzed in the study case is a software development project in a banking sector company, with maintenance and support operations.
The project could be described as a consolidation layer of information from external systems, which will adjust the data to a predefined standard format, in order to report risk measures to downstream systems. The project is part of a wholesale banking system and customers are all over the world. Different suppliers are subcontracted to handle different aspects of the project. Human resources and the three main suppliers of the project were allocated in 5 countries. Project management was located in United Kingdom.
From the point of view of the main stakeholders of the project, we could describe its infrastructure as a synthesis of 36 upstream systems and 4 support systems that provide reference data and another 10 downstream systems to which risk measures need to be reported. On the other hand, there were audit systems reviewing the project; therefore, some ad hoc audit teams asked for new requirements. It is important to mention that each system is an external team, with its own organization chart. In many cases, these organizations are working with offshore teams.
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