1. Introduction:
The Business enterprises (whether it's a manufacturing firm, software firm or transactional firm) all over the world has same objective i.e. to reduce the cost of operations and production and fix their large amount markets share in their respective markets by creating unique services and products and earn higher profits. To achieve such objective the companies need to understand the importance of the quality maintenance and process improvement techniques.
According to Standish report, The Software development projects have a maximum no. of failure rate as compared to another industry's projects. And the major reason which told behind this is cost overrun and schedule overrun. In the fast changing environment of software industry it is difficult to give attention to each error (whether it is error in processes of life cycle or any technical software error) cause during project execution and mainly those small errors left behind to deal with them later but these small error keep re occurring due to the absence of any specialized or standard quality control or improvement techniques. And these small reoccurring errors keep increasing their size and when project reaches to close out phase then they become so big that rectification of those errors become necessary also it will increase "Cost of Quality" (The Cost of Quality is the expense a project, or organisation, must incur to ensure that quality will exist within projects) and increase the schedule too. In the absence of quality in the project and its deliverable the company has to pay the non conformance cost. The cost of non-conformance is the amount of money spent to redo work, refund the money to the customers, loss due to project failure, fines, and all the other negative things that can happen to an organization if the quality is not maintained or delivered through out the project life cycle and in its final deliverable. In the past few years many IT/software organization understand this problem and trying to workout some process or technique to get consistent quality control through out the project life cycle. To overcome or reduce the re-occurrence of these errors in the software projects this report will try to provide a frame work for software project management life cycle which integrate some quality control tools from six sigma (specialize in quality control measure activities).
The professional project managers across the world widely using the project management standards established in Project Management Book of Knowledge (PMBoK) (promulgated by the Project Management Institute). Both six sigma and the PMBoK's Project Management share some common goals and intent. Both of them try to reduce the failure, control costs and schedules, prevent defects and manage the risk. Professional project management treat every project as an unique therefore project goals achieve by encouraging sound practices on a project by project basis where six sigma is more concentrate toward solution of problem to their root cause and prevention of their recurrence in contrast of attempting to control potential cause of problem on a project by project basis.
1.1 Project Environment
The role of the project management team is critical when it comes to addressing the missing elements of software product development and implementation (David, Geoff, & Peter, 2004). Effective project management can carry on the development of software services namely, management services, operational services, and technical services (Grance et al., 2003). The integration of process control strength of project management and troubleshooting strength of six sigma help the organization to create a highly effective consistent, control and predictable process trouble shooting system. The integration of quality control techniques with project management life cycle guidelines helps to achieve the deliverable of the project in a cost-effective manner, and ensures that organizational objectives are also achieved.
1.2 Rationale Lifecycle
The quality control is inspection driven and if the quality of the project is not meeting with the objectives then The integration of Six-sigma approach with project management will begin with the project life cycle and this integration will completely customizable according to the business need so it is not too much expensive process. The integration of tool/techniques of Six Sigma like "DMAIC" etc into the project management life cycle will help to reap up the maximum benefit of business like reduce the probability of human error and oversight. The DMAIC stands for Define, Measure, Analyze, Improve and Control, when DMAIC integrate into the project life cycle of PMBoK's project management the probability of failure and redundancies will reduce subsequently.
We can integrate the important defining processes of six sigma into the definition phase of project management like pareto diagrams, fishbone diagrams. It helps to identify the root cause for failure or less performance of project life cycle processes. Similarly we can merge other important techniques of six sigma in each phase of project life cycle.
For example, if some company facing a problem of ease of navigation on its website then with the help of six sigma's pareto diagram analysis we can find out the root cause of the problem and recommend a solution with the estimated cost and on the basis of that the project manager can define their project scope and work break down structure and then project can be executed accordingly.
1.3 Aim
To control and maintain the consistent level of quality throughout software project life cycle to avoid the cost and schedule overrun by integrating the important tools and techniques of Six Sigma in the project life cycle of PMBoK's Project management at different phases of software projects to accomplish process enhancement, cost reduction and development of new processes and their faster implementation.
1.4 Scope and Deliverables
The project management procedures and techniques are focused on project development from its initiation to close out phase but it fails in providing a consistent quality level through out the project life cycle where six sigma-defined quality tools have been found to be highly effective in increasing the efficiency of organization in terms of assisting the management of ongoing projects. As a result of this, organizations are increasingly becoming aware of Six Sigma methodologies as an efficient quality control/ improvement tool.
This research will provide a framework which will help to integrate the monitoring aspects of project management tools/processes with the problem -resolving and quality centric trouble shooting features of six sigma. While the quality improvement tools of six sigma ensures that qualitative objectives will achieve, the project management helps to make a health report of the project progress at every small step (known as milestone) through out the project life cycle.
The assessment techniques like FMEA (Failure Mode and Effect Analysis) and R&R (Repeatability and Reproducibility) by six sigma will use to integrate with the project management techniques such as scheduling, budgeting and resource management and ensure that every task in the life cycle is managed and achieved comprehensively. This helps the organization to ensure the consistent quality level through out the project life cycle and reduction in reoccurring errors.
1.5 Objectives:
To understand the basic differences and similarities between project management and six sigma methodologies.
To identify the important tools and techniques of Six Sigma for integration in the project life cycle of PMBoK's.
To analyze the importance of quality for the software project's success.
To develop a improve software project life cycle framework including important six sigma's quality control techniques in it.
1.6 Consideration:
The major considerations were the, issue resolving strengths of six sigma tools and techniques by finding the root cause of the problem. The project management life cycle structured step by step approach.
1.7 Assumptions:
The major assumption of this project is that the integration of these two techniques will create the positive results and increase the efficiency of the software project life cycle. The project life cycle is enough flexible to accommodate the external tools and techniques in it without any hassle.
The other assumptions are:
Some individuals may not complete the survey, possibly creating bias in the collected data. Because of this, incomplete survey questionnaires will immediately be invalidated and will not be included in the analysis.
Individual participant survey will be confidential. In this way, the researcher will be able to ensure the full cooperation of the participants.
1.8 Limitations:
The major limitation of this project is that there are so many six sigma techniques available but we cannot use or study all the techniques in such a short time. So project has to identify only two or three major techniques and concentrate on it.
The Project does not cover any risk management activity in any phase of project life cycle to highlight or resolve the problems arise throughout the life cycle.
1.9 Budget/Resource Requirements:
There were no major budget related issues faced by the researcher during the course of this study. This can be a reason of research orientation that is kept secondary in nature. Further, along with the exclusion of primary research, this research doesn't required any type of consent or permission from higher authorities, which made it the process of data collection free from the aspect of financial contributions.
1.10 Initial Literature Review
Six Sigma is defined as a metric, an initiative, and a philosophy. As a metric, Six Sigma is a robust statistical measurement of a process symbolized by the Greek letter σ that represents process capability with a normal data distribution, having exactly 3.4 defects per million opportunities. It is defined as an initiative taken by leaders of companies who strive for significant financial advantage through continuous improvement. As a philosophy, Six Sigma is a means to achieve data-driven decisions. Six Sigma terminologies originate from the relationship between process variations and customer specifications (Breyfogle, 2003). Six Sigma is also identified as "a framework for linking improvements to profitability" (Gupta, 2003, p. 12). In the context of this study, a Six Sigma program at the host organization was used to represent the project management programs at other similar organizations.
The Six Sigma methodology has evolved from scientific management and various continuous improvement theories by combining the finest elements of many former quality initiatives (Folaron & Morgan, 2003). Project success has a long evolutionary history, dating as far back as when the first human shelter was erected. Relevant history of project success for this literature review dated back to the start of scientific management, and Six Sigma evolved from that time.
In the 1950s and 1960s, the topic of project success was not considered of primary importance in the United States, but starting in Japan in the early 1950s, many companies embraced the quality principles of Deming (1986) and Juran (1974). To make the quality movement toward process measures more attractive to companies in the United States, quality methodologies were repackaged and sold by consultants. Gradually, the quality movement took hold in the business world, and organizational leaders began to "think and talk about organizational effectiveness" (Scott, 2003, p. 356).
Six Sigma project management originated in the quality movement. Gillard and Price (2005) remarked the following on management's quest for improvement through the quality movement: Management lends itself well to prescriptions for improving organization function. Attempts to assist in the quest for effectiveness, such as Total Quality Management (TQM), Management by Objectives (MBO), Management-By- Walking-Around (MBWA), Management by Exception (MBE), ISO 9000, and Six Sigma, focus organization managers on techniques. The modern view of project success is that organizations need to define the specific criteria necessary to enable growth, innovation, and reliability (Scott).
1.11 Initial Research Approach
Prior to searching for evidential support for the report, the author formulated a secondary research plan, based on project class's textbooks and other research resources. That information inspired the following two basic research requirements.
First and foremost, this research required a solid definition of what constituted first and second level sources of information (Graziano, 2007). First level sources consisted of peer reviewed journal articles, conference proceedings, published consultancy presentations and analysis, and white papers published by industry participants with significant experience implementing Six-Sigma and project management process improvement programs (Graziano, 2007).
These sources had to be pertinent to proving or disproving the hypothesis-supplying specific examples relevant to project management, Six-Sigma, and combined approach performance improvement programs, in an acute care organization. Second, source of data collection is the secondary data gathered by sending surveys to the IT employees of the company who will involved in company's unique project management processes or model for the quantitative portion of the study.
1.12 Conclusion:
This chapter highlights the need and background of the topic. It highlights the probable constraints, and assumption of the topic. In this chapter the major things which want to convey is that usage of quality improvement techniques in project management life cycle can affect the overrun problem schedule and budget IT projects. The merging of both these processes creates an extremely effective integrated tool which will suit almost every kind of organization from Transactional to manufacturing, from small to big/complex projects. With the increasing competition by the minute, the need of integration of Six Sigma with the Project management will felt even more in the coming days.
Literature Review:
Recent research shows that many of the IT projects have 'failed', in the combination of budget and/or schedule overruns and/or for not meeting users' requirements. The well known and now widely quoted Chaos Report by Standish Group declared that IT projects are in chaos.
Table 1 provides a summarized report card on project outcomes based on the Report. Type 1 projects are those completed on time and within budget, with all required functions and features initially specified. The ''challenged'' projects, though completed and operation al, suffered budget overruns and/or program slips, and offered fewer functions and features than originally specified. The ''impaired'' projects are those cancelled or abandoned at some point during the development cycle. It is anticipated that many of the software projects would continue to be 'challenged' or 'impaired'. The truly 'successful' stories from the outset will be relative rare.
The Standish study defined project failure as either a project that has been cancelled or a project that does not meet its budget, delivery, and business objectives. Conversely, project success, is defined as a project that meets its budget, delivery, and business objectives. With this definition, the average IT project success rate in the Standish study was an abysmal 16.2%.
Success, for software projects is not a 'black and white' concept. It can be viewed as a combination of project implementation success and systems success. Systems success can be separated into three levels: technical development, deployment to the user and delivery of business benefits or treated as a four-dimensional construct consisting of the success of the development process, success of the use process, quality of the product, and impact on the organization.
The research done by DeLone & McLean (1992) proposes six major dimensions of project success, which they refine to include: project's system quality, information quality, service quality, user satisfaction and net benefits to organization.
The DeLone & Mc lean (1992) research clearly state that quality level at each phase at the project life cycle affects projects success. It is not only final deliverable quality that describes the project success or failure, the quality of the processes; systems used in each phase to achieve the final deliverable make impact too on the project success. According to the Knopefel (1989), Quality is the absence of deviations from the planned new state and the usefulness of this new state for the future operations. Typical examples of quality are safety and reliability, beauty and comfort, economic and financial performance. Quality is achieved if the system possesses the expected attributes and system can be a combination of processes used in the project.
The Yaseen and Marashly (1989) research shows that the project managers of software companies in the developing countries experience difficulty in the quality control management function, i.e. managing the implementation of the project within a certain quality level conforming to predetermine specifications. The reason behind the problem, in the researcher's opinion is the absence of a well structured framework for the quality control in each phase of project life cycle. The research shows that in the absence of suitable quality control measure at the each phase of project life cycle increases the repetition of error. Dtivid S. Alberts examined the impact of programming errors on the life cycle cost of software Projects. Such errors may occur early in the development phase, or later during the operational phase when software maintenance (i.e., change) is required. He found that about half of the IT project life cycle costs are attributable to errors which are made with equal probability in these two phases. Our inability to hold and lower the software error levels, especially in the development phase, is further supported by the work of Marc Bendick. He analyzed several software products having from thirty to two hundred thousand lines of coding. He discovered that the average cost of 'repeating" an error made during the development phase but not discovered until the software attained operational, was 139 times greater than the cost of writing that one line in the first place. So the research by the two researcher clearly states that in absence of proper quality control framework just one line could cause a huge different in project budget. And overrun of project budget is one of the major factors behind the project failure. If quality control measures are strongly placed and executed during project life cycle phases then the control on the overrun of the budget could be possible. Also using of quality techniques like DMAIC (Define, Measure, Analyse, Implement and Control) of Six sigma help in better understanding and recording of customer perception. It is an effective way to measure the deliverable and do the check whether the project is on the right track or not?
Project Management Life Cycle and PMBOK:
According to the Project Management Book of Knowledge (2004), the field of project management represents a broad spectrum of management disciplines encompassing the theory and practice of general management and application specific knowledge domains. Depending upon the scope and nature of project, project managers interface with various stakeholders from external clients, vendors, and suppliers to internal team members, enterprise staff, and executive management (Christenson & Walker, 2004).
Brought in at the inception of a project, project managers need to visualize the outcome while thinking in abstract terms, thrive in uncertainty setting the pace, and manage conflicting priorities among stakeholders while charting the direction to the outcome by eliminating obstacles throughout the project management life cycle of initiation, planning, execution, control, and closure (Bucero, 2004).
The five major project management life cycle steps are plan, define, construct, test and deploy for each case. Each of these project management steps yields standard deliverables, such as business requirements, system requirements, information flow diagrams, test cases, system architectures, etc.
Leading organisations use selection, implementation and evaluation processes uniformly at an enterprise level and within each business unit of their organisation. By contrast, there is very little or no uniformity in how risks, benefits, and costs of various software projects are evaluated. Moreover, many organisations appear to approach the whole management of software industry in an unstructured or ad hoc manner throughout its life cycle. Such approaches have evolved due to a limited understanding of the relationship between software project implementation and traditional business performance metrics.
For each stage of the software project life cycle, the organisation needs to estimate direct and indirect software project costs. The organisation should set up a series of activity cost matrices for each stage of the software project life cycle. Ownership costs include all direct and indirect costs that can be attributed with the initiation, design, development, operation and maintenance of the proposed software project. Therefore, all costs for the proposed software project, over its entire life cycle, must be included in the costing process.
Within most sectors of government and private industry there are suggestions that software investments are often accompanied by poor vision and implementation approaches, insufficient planning and coordination and are rarely linked to business strategies. The successful implementation of new and innovative software requires the development of strategic implementation plans prior to software project commencement. Effective planning should go some way to reduce the current gap between output and expectation from software's investments. Only recently, there has been growing interest in developing planning frameworks to aid Software project implementation.
Fig1: The ten predictors for strategic software implementation (adapted from Gottschalk)
Frederick P. Brooks observes that planning of a software project is an important aspect. He recommends budgeting one-third of the total resources to it. One-sixth of the project funds should be allocated for coding and another quarter each to testing of initial program modules and the final system. Brooks also warns against crash assignment of additional staff to a software project which has slipped in its schedule. Indoctrination of the new team members to the project will only eat into the time of the old team, thus delaying the project even further.
As Brooks stated that Planning phase is most important phase of IT project life cycle and if we integrate some quality improvement tool in planning phase then they will make sure that project management plan become very strong and effective as they can be used in risk management activities throughout the project life cycle to identify the probable cause behind the repeating risks.
Yaseen and Marashly (1989) showed a conceptual framework for the quality control at different phases of project life cycle. This quality control conceptual framework can be used in software project life cycle to implement quality tools at different phases to reduce the repeatability of errors.
System:
The system intended to be quality controlled - in any phase of project development cycle - is generally composed of three parts:
Input
Process
output
Quality control (QC):
This activity is generally composed of three successive actions:
Measuring
Comparing
correcting
These three parameters after being detailed could be structured in the 'quality control cube' in fig 2 .Each of these three segments is broken down into three successive actions.
From Figure 2 it should be noted that the quality control activity is illustrated in three faces of the quality control cube:
Face A represents the system quality control and contains three segments
(i) input QC, (ii) process QC, (iii) output QC.
Each of these three segments is broken down into three successive actions. Namely
Measuring, comparing and correcting the input quality (3 cells)
Measuring, comparing and correcting the process quality (3 cells)
Measuring, comparing and correcting the output quality (3 cells).
DEVELOPING THE QC CUBE:
The QC cube is developed in parallel with the project development phases. These phases are presented in Figure 3. In the first three phases of the project development cycle (i.e. study, design and contracting & procurement), the specs of both the system and QC (faces B and C of the QC cube) are developed, while in the fourth phase (implementation) the system QC (face A of the QC cube) is activated.
Hence the following specifications are sequentially indicated and developed in the successive project phases as follows:
The responsibilities of the three participants and the system quality control are summed up in following figure:
Development of the Quality control cube according to Project life cycle phases:
Overview of Six Sigma
Six Sigma was developed by Motorola Corporation in the early 1980s as a method of reducing product failure and eliminating defects. Six Sigma is defined as a data-driven methodology for eliminating defects that seeks to drive manufacturing and service efforts so that there are six standard deviations between the mean and the nearest specification limit in any process. Six Sigma provides organizations measurable ways to track performance improvement and identify processes that are working well. Organizations using Six Sigma focus on managing processes at all organizational levels, linking senior executives' priorities to operations and the front line in a team-based, collaborative approach (Hake 1991).
The six sigma methodology is a rigorous and focused application of quality improvement tools. The term six sigma refers to the idea of striving for near perfection at the infinitesimal rate of 3.4 problems per million opportunities. The centerpiece of the six sigma approach is the DMAIC (define, measure, analyze, improve, and control) project cycle (Brue 2002).
Beyond its role in quality assurance, six sigma helps the organization become more profitable. While it uses many traditional quality methods found in TQM, six sigma's ability to measure project success in terms of profit or cost savings is appealing to many executives.
Six Sigma as a methodology (DMAIC process)
From a methodology perspective, Six Sigma is a roadmap to help improve customer satisfaction, reduce process related defects, and thereby reduce costs (Siviy, 2001). The methodology supports project prioritization and selection based on the project's relationship to variables such as executive strategies within the organization, risk associated with the project, and estimated benefit resulting from the project. The methodology consists of phases and toll gates - or check points - at the conclusion of each phase to help ensure that all work is complete. The Six Sigma Academy (2002) cites the five phases of Six Sigma as Define, Measure, Analyze, Improve, and Control, or DMAIC.
The Define phase, according to the Six Sigma Academy (2002), begins with a problem. This phase helps to clarify what the problem is and why the problem requires a solution. The common chain of activities in the Define phase includes constructing a business case, clarifying how the problem is linked to the customer, understanding the current process, and forming the project team. The Define phase consists of tools such as voice of the customer (VOC), which includes reviewing customer complaints and using
interviews and surveys to gain a better understanding of the customer's perception of the problem; critical to quality (CTQ) trees, which are intended to provide more clarity around the VOC; and SIPOC mapping, which is a high level process map that takes into consideration the suppliers, inputs, outputs, and customers of a process. Once activities are completed in the Define phase, a toll gate review of the work is completed, and the project team then moves to the next phase of the project, which is the Measure phase. In the Measure phase, statistical tools are applied to establish and validate the measurement system that will be used to measure the process for both baseline and target performance of the process (Six Sigma Academy, 2002). The common chain of activities in the Measure phase includes developing process measures; collecting data from the process; checking the quality of the data; understanding the current performance of the process; and determining the potential capability of the process (Brook, 2004). The Measure phase of Six Sigma is filled with statistical tools and techniques. Data must be correctly categorized so that the relevant statistical models can be applied. For example, a normal distribution is most relevant to continuous data while a Poisson distribution is more relevant to count data. Minimum sample sizes of data are computed so that statistical results of the data have greater significance. The measurement system itself is tested using gauge repeatability and reproducibility (GR&R), which is a statistical study to quantify precision errors in the measurement system. Time series plots and histograms are used to provide an understanding of the current process performance. Future process capability is computed by taking into consideration upper and lower specification limits of the current process and details from the histogram, to arrive at a potential sigma level.
Once these activities in the Measure phase have been completed and approved through a toll gate review, the project team moves on to the next phase, the Analyze phase. The Analyze phase of Six Sigma is a tool box of tools and techniques to identify the critical factors of a good process as well as the root causes of process defects (Brook, 2004). The typical flow of activities in the Analyze phase is analyzing the current process; understanding why defects are in the process; and analyzing the data from the process and verifying the reasons for defects, particularly any cause and effect relationships. This phase also includes many statistical tools and techniques, beginning with detailed process maps, failure mode and effect analysis (FMEA), brainstorming, and Ishikawa diagrams to analyze the current process and to search for root causes of defects. Data are then analyzed graphically using histograms, dot plots, time series plot, box plots, scatter plots, and Pareto charts to understand what the data is saying. Data are also analyzed statistically using normality testing, statistical process control (SPC), hypothesis testing, simple and multiple regression, and design of experiments to help understand the causes of process defects (Six Sigma Academy, 2002). Once the causes of process defects have been identified, the project team, after completing a toll gate review, proceeds to the next phase of the project.
The Improve phase of Six Sigma focuses on developing, selecting, and implementing the best solution to improve the process (Brook, 2004). The typical flow of activities in the Improve phase consists of generating potential solutions, selecting the best solutions, assessing the risks of each solution, and finally, implementing the best solution. Statistical tools and techniques in this phase include brainstorming, benchmarking, solution screening, and FMEA to generate potential solutions, as well as analysis of variance (ANOVA), regression analysis, and simulation to help determine optimum settings for process outputs (Six Sigma Academy, 2002). The final phase of the Six Sigma methodology is the Control phase. During this phase, controls are implemented so that the process improvement can be sustained over time. This phase employs tools such as control plans, hypothesis testing, statistical process control (SPC), and control logs.
CHAPTER 3 RESEARCH and METHODOLOGY
Data Collection and Sampling:
According to Saunders data is, "…facts, opinions and statistics that have been collected together and recorded for reference or analysis…" (Saunders et al 2003)
And sample is the collection of made up of some of the members of a population. (Collis & Hussey 2003)
Where time is constraint sampling is required. Sampling is important as the size and the way the sample is selected will impact the confidence in the data and the extent of generalisation (Saunders et al 2003 p.283). According to the Saunders (2003), Sampling techniques enable you to reduce the amount of data you need to collect by considering only the data from a subgroup.
Collection Methods:
During the research the usage of multiple methods will help to avoid potential bias and thereby improve objectivity, reliability and validity.
Triangulation refers to: "…the use of different data collection methods within one study in order to ensure that the data are telling you what you think they are telling you…" (Saunders et al, 2003)
There are two collection methods; qualitative and quantitative. The qualitative methods, their advantages and disadvantages and rational for acceptance/rejection is listed in below table:
Table 6: Quantitative Data Collection Methods
METHOD
ADVANTAGES
DISADVANTAGES
OUTCOME & RATIONALE
Interview
Researcher has 'control' over the questions and can probe answers to gain in-depth and accurate information.
Flexibility allows the line of enquiry to be altered.
Relatively high response rate.
Data can be checked for accuracy and relevance as it's collected during the interviewing ensuring higher validity.
Allows researchers to explore reasons for people's responses.
Respondents aren't equally clear and perceptive.
Must have in-depth knowledge regarding the issue and respondent.
Requires a skilled and alert interviewer.
Can be difficult to interpret and analyse.
Can be very time-consuming.
Rejected - Due to the constraint on the primary research this method can not be used in this research.
Focus Group
Useful in the design stages of surveys and in interpreting the results.
Produces large amounts of in-depth, credible information
Quick, low cost and flexible
Allows interaction with respondents
Difficulties in summarising and categorising information collected.
Problems in gathering the right number of people who are representative of the population.
Members may be influenced by each other inducing potential bias
Rejected - This was not appropriate as the author wishes to discuss each company with the project managers individually and therefore a focus group concentrating on one issue would have been irrelevant.
Observation
Virtually all data collected are useful.
Provides opportunity to experience the emotions of those being observed.
Heightens the researcher's awareness
Useful for researchers working in their organisation.
Time consuming.
Data recording can be difficult.
Can pose ethical issues for the researcher.
Observer role is very demanding.
Room for potential bias to creep in on observers part
Rejected - Although observations may have provided a useful insight as to the researchers opinion on the companies application of six sigma techniques it's not feasible in the short period of time in which the research is to be conducted.
Documentation
Stable allowing the researcher to review the data repeatedly.
Unobtrusive
Can also provide a broad coverage.
High level of accuracy.
Irretrievability can be low.
Bias in selection of documentation.
Problems can arise if the data collection is incomplete.
Access may be deliberately blocked
Rejected - Company-specific documentation will not be useful in this situation.
(Saunders et al 2007, Ghauri & Gronhaug 2002. Creswell 2003, Denscombe 1998; Yin 2003)
The quantitative methods, their advantages and disadvantages and the rational for acceptance/rejection are also listed in below figure:
Quantitative Data Collection Methods
METHOD
ADVANTAGES
DISADVANTAGES
OUTCOME & RATIONALE
Telephone Survey
Allows interviewer to probe respondents'
Relatively low cost and quick.
High confidence that the intended person is answering the questions.
Must be kept relatively short or respondents may hang up.
Rejected - The author did not have access to the relevant telephone numbers and it would not be convenient for the nature of the topic.
Face to Face Survey
Can use several techniques such as visual aids.
Knowledge of who is answering the questions
Ability to clarify any queries over questions.
Reactive and therefore it's difficult to avoid bias.
Costly in terms of time and money.
Rejected - This method would have required the author to visit each company and speak with various members of project teams which is not practical within the given time period.
Postal/Self Completion Survey
Low cost.
Reach a large geographical population.
Avoids interviewer bias as all respondents receive same questions.
Can ask more complex questions
More likely to gain honest answers due to anonymity.
Response rates low - approximately 30%.
No control over the interpretation of questions.
May be contaminated due to discussion with other people.
Rejected - There is no need to post out the surveys when email is available and the respondents have access to email.
Electronic Mail Survey
Removes costs of postage and telephone calls.
Can contact difficult-to-access groups if post questionnaire on a website.
Can be relatively sure the right person has responded.
If responses are to be unidentifiable an anonymous mailbox is required for returning responses.
May be classed as SPAM and therefore ignored.
Only computer-literate individuals can be contacted.
Accepted - This is the most feasible method due to time available for gathering large amounts of standardised information. By indicating to the Project Manager the levels and positions of team members to receive the survey the researcher will obtain responses from a range of positions and roles.
Experimentation
Knowledge gained from experiment can be tested in similar situations to verify results.
Allows for flexibility, efficiency and powerful statistical manipulation.
Can be difficult to design experiments which represent a particular sample
Problems isolating the 'cause' variable resulting in alternative explanations existing which will weaken the results.
Rejected - Experiments were not required due to the nature of the research. They would not have produced information relevant to the research objectives or hypotheses.
(Saunders et al 2003:2007; Bernard 2000; Creswell 2003; Blaxter et al 2001)
Quantitative Variables:
Quantitative surveys can certainly expose measurable facts for the researcher and reliability, validity, and generalization of the results can be enhanced by obtaining uninfluenced answers from respondents (Creswell, 2003). Quantitative approaches seek to gather factual data and to study relationships between facts and how such facts and relationships accord with theories and the findings of any research executed previously (literature). Scientific techniques are used to obtain measurements -quantified data.
Sampling techniques: Survey (Quantitative)
Surveys operate on the basis of statistical sampling; only extremely rarely are full population surveys possible, practical or desirable. The principles of statistical sampling - to secure a representative sample - are employed for economy and speed. Commonly, samples are surveyed through questionnaires or interviews. Surveys vary from highly structured questionnaires to unstructured interviews. Irrespective of the form adopted, the subject matter of the study must be introduced to the respondents
This research study will administer a survey on a company deploying IT projects with an approximate population of 1000-3000 employees. The survey shall determine the effectiveness of IT solutions within the company. The sample size that can represent the population of the organization was two hundred (200) respondents. This is based on the sample size calculator of the survey system (Creative Research Systems, 2003).
Advantages:
Standardization
Ease of administration
Ability to tap the "unseen"
Suitability to tabulation and statistical analysis
Sensitivity to subgroup differences
Data collection Instrumentation: Questionnaire (Quantitative)
Surveys were sent in the form of questionnaire to IT employees in the company for the quantitative portion of the study. A selected portion of the company shall be subject to answer the survey while the respondents are randomly chosen, which means that everyone in the selected population has an equal chance of being selected.
The survey questionnaire was administered to a sample size of 200 employees. Respondents were randomly selected in order to ensure the impartial selection of the participants of the study. In this way also, the researcher can assure the validity of the outcome of the survey.
The survey questionnaire consisted of two parts. The first part of the survey questionnaire would delve into the demographic profile of the respondents. Participants' characteristics such as age, gender and the number of years in the company was asked and second part contain the questions regarding this research.
The personal profile of the respondents is necessary information for the study because it influences the kind of response given by the participants. In other words, the outcome of the survey can be partially explained through the demographic profile of the respondents. The second part of the survey includes participant identification of the different issues that significantly affects the success of information projects.
Data CollectionMethod: Questionnaire
The assumed sample size for the survey respondents would be two hundred (200) employees, of which 140 surveys (i.e., 70%) questionnaires will expect to return. Statistical tools will used to process the control and predictor variable data derived from the survey and their relationship to the dependent variables. The predictor variable data will be collect by using a five-point Likert scale. Before processing it, the responses will quantify by using the weighted mean.
Whilst constructing the questionnaire, careful consideration was given to phrasing to avoid ambiguity and leading or complex questions, ensuring a logical order was followed and minimum technical jargon used (Blaxter et al 2001).
Finally, a cover letter is to be included with the survey to introduce the researcher to the respondents and describe the research purpose, data collection and to provide clear instructions regarding completion and return.
Limitations
Firstly, only those knowledgeable of project management can be contacted for questionnaire. Secondly, time will be a considerable constraint for the respondents, so the focus will be on obtaining the critical information. Finally, the study is not a comprehensive research investigation but instead smaller-scale projects limited by word count.
Research Problem:
The purpose of this paper is to evaluate the relative role of project management, project cost, project risks and end/user acceptance in the success of software projects. It was determined whether factors like the project management processes, project cost, project risks and end user acceptance can significantly affect the success of software projects.
Major question of the questionnaire on which whole research is based is:
Do the Integration of Project Management Life Cycle and Six Sigma Techniques Improve the Success Rate of software projects?
Different approaches and techniques have been utilized in the process of implementing software projects. In the first problem of the study, it determines whether the use of project management system can improve the success rate of software projects. As such, the hypothesis for this problem is stated as follows:
H1: The integration of project management life cycle and six sigma techniques can significantly improve the success rates of information security projects.
H0: The integration of project management life cycle and six sigma techniques cannot significantly improve the success rates of information security projects.
Data Analysis or Discussion:
Data will collect through the survey instruments. The data will be analyzed by using the Likert rating scale and the weighted mean. In addition, an analysis of secondary information is also conducted. The information will collect and analyze aims to answer the research problem of the study. It is assuming that in the actual survey, the return rates will not one hundred percent (100%). Out of the possible 200 survey respondents, it is assuming that approx 140-150 of them will give response to questionnaire.
This study is expecting to determine that there is a significant role played by integrated project management model with in the success of software projects.
Questionnaire:
PART 1:
1. How many years experience do you have working in project-based environments?
ï¯ Less than 1 year ï¯ 5 - 10 years
ï¯ 1 - 2 years ï¯ 10 - 15 years
ï¯ 2 - 5 years ï¯ More than 15 years
2. How many years have you worked in your current organization?
ï¯ Less than 1 year ï¯ 5 - 10 years
ï¯ 1 - 2 years ï¯ 10 - 15 years
ï¯ 2 - 5 years ï¯ More than 15 years
3. Please provide your job title.
....................................................................................................... ......................
4. Please indicate your age.
ï¯ Less than 25 years ï¯ Between 46 and 55 years ï¯ Between 25 and 35 years ï¯ Between 36 and 45 years ï¯ Above 56 years
5. Please specify your gender.
ï¯ Male ï¯ Female
PART 2:
6. Are you aware with quality control measure/processes used in Project life cycle?
ï¯ Yes ï¯ No
7. Do you think that quality issues are one of the major reasons behind the IT project failure?
ï¯ Yes ï¯ No
8. Do you think that to achieve the deadline some of the project personnel avoid the rectification of the small errors raised during the project life cycle?
ï¯ Yes ï¯ No
9. Do you think that re occurrence of small unresolved errors is one of the major reasons behind the cost and schedule overrun in IT projects?
ï¯ Yes ï¯ No
10. Do you think project management techniques/processes are not sufficient to maintain the consistent quality level throughout the project life cycle?
ï¯ Yes ï¯ No
11. Are you aware of Six sigma's quality tools/ techniques?
ï¯ Yes ï¯ No
12. How many tools and techniques of six-sigma are you aware with?
ï¯ 1 ï¯ 2
ï¯ 3 ï¯ 4 or more
13. In your opinion which phase is the most important phase of software project life cycle?
ï¯ Initiation ï¯ Planning
ï¯ Execution ï¯ Closeout
ï¯ All of the above
14. Do you think if the quality tools of six-sigma integrate in project management life cycle it will help to maintain the consistent quality level in project and control/stop the re-occurrence of error?
ï¯ Yes ï¯ No
15. Do the integration of project management life cycle and six sigma techniques improve the success rate of software projects?
ï¯ Yes ï¯ No
