Quantitative Project Management
A Project Management Process Area at Maturity Level 4
Purpose
The purpose of Quantitative Project Management (QPM) is to quantitatively manage the project’s defined process to achieve the project’s established quality and process-performance objectives.
Introductory Notes
The Quantitative Project Management process area involves the following activities:
• Establishing and maintaining the project’s quality and process-performance objectives
• Identifying suitable subprocesses that compose the project’s defined process based on historical stability and capability data found in process-performance baselines or models
• Selecting subprocesses within the project’s defined process to be statistically managed
• Monitoring the project to determine whether the project’s objectives for quality and process performance are being satisfied and to identify appropriate corrective action
• Selecting measures and analytic techniques to be used in statistically managing selected subprocesses
• Establishing and maintaining an understanding of the variation of selected subprocesses using selected measures and analytic techniques
• Monitoring the performance of selected subprocesses to determine whether they are capable of satisfying their quality and process-performance objectives, and identifying corrective action
• Recording statistical and quality management data in the organization’s measurement repository
The quality and process-performance objectives, measures, and baselines identified here are developed as described in the Organizational Process Performance process area. Subsequently, the results of performing the processes associated with the Quantitative Project Management process area (e.g., measures, measurement data) become part of the organizational process assets referred to in the Organizational Process Performance process area.
To effectively address the specific practices in this process area, the organization must have already established a set of standard processes and related organizational process assets, such as the organization’s measurement repository and the organization’s process asset library for use by each project in establishing its defined process.
The project’s defined process is a set of subprocesses that form an integrated and coherent framework for project activities. It is established, in part, through selecting and tailoring processes from the organization’s set of standard processes. (See the definition of “defined process” in the glossary.)
The project should ensure that supplier effort and progress measurements are made available. Establishing effective relationships with suppliers is also important to the successful implementation of this process area’s specific practices.
Process performance is a measure of actual process results achieved. Process performance is characterized by both process measures (e.g., effort, cycle time, defect removal efficiency) and product measures (e.g., reliability, defect density, response time).
Subprocesses are defined components of a larger defined process. For example, a typical organization’s service system development process may be defined in terms of subprocesses, such as requirements development, design, build, test, and peer review. The subprocesses themselves may be further decomposed as necessary into other subprocesses and process elements.
An essential element of quantitative management is having confidence in estimates (i.e., being able to predict the extent to which the project can fulfill its quality and process-performance objectives). Subprocesses to be statistically managed are chosen based on identified needs for predictable performance. (See the definitions of “statistically managed process,” “quality and process-performance objectives,” and “quantitatively managed process” in the glossary.)
Another essential element of quantitative management is understanding the nature and extent of the variation experienced in process performance and recognizing when the project’s actual performance may not be adequate to achieve the project’s quality and process-performance objectives.
Statistical management involves statistical thinking and the correct use of a variety of statistical techniques, such as run charts, control charts, confidence intervals, prediction intervals, and tests of hypotheses. Quantitative management uses data from statistical management to help the project predict whether it will be able to achieve its quality and process-performance objectives and identify what corrective action should be taken.
This process area applies to managing a project, but the concepts found here also apply to managing other groups and functions. Applying these concepts to managing other groups and functions may not necessarily contribute to achieving the organization’s business objectives but may help these groups and functions control their processes.
Examples of other groups and functions that could benefit from using this process area include the following:
• Quality assurance
• Process definition and improvement
• Effort reporting
• Customer complaint handling
• Problem tracking and reporting
Related Process Areas
Refer to the Capacity and Availability Management process area for more information about ensuring effective service system performance and ensuring that resources are provided and used effectively to support service requirements.
Refer to the Strategic Service Management process area for more information about establishing and maintaining standard services in concert with strategic needs and plans.
Refer to the Causal Analysis and Resolution process area for more information about identifying causes of defects and problems and taking action to prevent them from occurring in the future.
Refer to the Integrated Project Management process area for more information about establishing and maintaining the project’s defined process.
Refer to the Measurement and Analysis process area for more information about aligning measurement and analysis activities and providing measurement results.
Refer to the Organizational Innovation and Deployment process area for more information about selecting and deploying incremental and innovative improvements that measurably improve the organization’s processes and technologies.
Refer to the Organizational Process Definition process area for more information about establishing organizational process assets.
Refer to the Organizational Process Performance process area for more information about establishing and maintaining a quantitative understanding of the performance of the organization’s set of standard processes in support of achieving quality and process-performance objectives, and to provide process-performance data, baselines, and models to quantitatively manage the organization’s projects.
Refer to the Project Monitoring and Control process area for more information about monitoring and controlling the project and taking corrective action.
Specific Practices by Goal
SG 1 Quantitatively Manage the Project
The project is quantitatively managed using quality and process-performance objectives.
SP 1.1 Establish the Project’s Objectives
Establish and maintain the project’s quality and process-performance objectives.
When establishing the project’s quality and process-performance objectives, it is often useful to think ahead about which processes from the organization’s set of standard processes will be included in the project’s defined process and what the historical data indicate regarding their process performance. These considerations will help in establishing realistic objectives for the project. Later, as the project’s actual performance becomes known and more predictable, objectives may need to be revised.
Typical Work Products
1. The project’s quality and process-performance objectives
Subpractices
1. Review the organization’s objectives for quality and process performance.
The intent of this review is to ensure that the project understands the broader business context in which the project must operate. The project’s objectives for quality and process performance are developed in the context of these overarching organizational objectives.
Refer to the Organizational Process Performance process area for more information about establishing quality and process-performance objectives.
2. Identify the quality and process-performance needs and priorities of the customer, suppliers, end users, and other relevant stakeholders.
Examples of quality and process-performance attributes for which needs and priorities might be identified include the following:
• Duration
• Response time
• Availability
• Reliability
• Service continuity
• Predictability
3. Identify how quality and process performance is to be measured.
Consider whether measures established by the organization are adequate for assessing progress in fulfilling customer, end-user, and other stakeholder needs and priorities. It may be necessary to supplement these measures with additional ones.
Examples of measurable quality attributes include the following:
• Mean time between failures
• Number and severity of customer complaints
• Availability
• Response time (of service performance)
Examples of measurable process-performance attributes include the following:
• Cycle time
• Percentage of rework time
• Compliance to service level agreements
Refer to the Measurement and Analysis process area for more information about specifying measures.
4. Define and document measurable quality and process-performance objectives for the project.
Defining and documenting objectives for the project involve the following:
• Incorporating the organization’s quality and process-performance objectives
• Writing objectives that reflect the quality and process-performance needs and priorities of the customer, end users, and other stakeholders and the way these objectives should be measured
5. Derive interim objectives for each lifecycle phase as appropriate to monitor progress toward achieving the project’s objectives.
An example of a method to predict future results of a process is the use of process-performance models to predict latent defects in the delivered product using interim measures of defects identified during product verification activities (e.g., peer reviews, testing).
6. Resolve conflicts among the project’s quality and process-performance objectives (e.g., if one objective cannot be achieved without compromising another).
Resolving conflicts involves the following activities:
• Setting relative priorities for objectives
• Considering alternative objectives in light of long-term business strategies as well as short-term needs
• Involving the customer, end users, senior management, project management, and other relevant stakeholders in tradeoff decisions
• Revising objectives as necessary to reflect results of conflict resolution
7. Establish traceability to the project’s quality and process-performance objectives from their sources.
Examples of sources of objectives include the following:
• Requirements
• The organization’s quality and process-performance objectives
• The customer’s quality and process-performance objectives
• Business objectives
• Discussions with customers and potential customers
• Market surveys
An example of a method to identify and trace these needs and priorities is Quality Function Deployment (QFD).
8. Define and negotiate quality and process-performance objectives for suppliers.
Refer to the Supplier Agreement Management process area for more information about establishing supplier agreements.
9. Revise the project’s quality and process-performance objectives as necessary.
SP 1.2 Compose the Defined Process
Select subprocesses that compose the project’s defined process based on historical stability and capability data.
Refer to the Integrated Project Management process area for more information about establishing and maintaining the project’s defined process.
Refer to the Organizational Process Definition process area for more information about the organization’s process asset library.
Refer to the Organizational Process Performance process area for more information about establishing performance baselines and models.
Subprocesses are identified from process elements in the organization’s set of standard processes and process artifacts in the organization’s process asset library.
Typical Work Products
1. Criteria used to identify which subprocesses are valid candidates for inclusion in the project’s defined process
2. Candidate subprocesses for inclusion in the project’s defined process
3. Subprocesses to be included in the project’s defined process
4. Identified risks when selected subprocesses lack a process-performance history
Subpractices
1. Establish the criteria to use in identifying which subprocesses are valid candidates for use.
Identification may be based on the following:
• Quality and process-performance objectives
• Existence of process-performance data
• Product line standards
• Project lifecycle models
• Stakeholder requirements
• Laws and regulations
2. Determine whether subprocesses that are to be statistically managed and were obtained from organizational process assets are suitable for statistical management.
A subprocess may be more suitable for statistical management if it has a history of the following:
• Stable performance in previous comparable instances
• Process-performance data that satisfy the project’s quality and process-performance objectives
Historical data are primarily obtained from the organization’s process-performance baselines. However, these data may not be available for all subprocesses.
3. Analyze the interaction of subprocesses to understand relationships among subprocesses and measured attributes of the subprocesses.
Examples of analysis techniques include system dynamics models and simulations.
4. Identify the risk when no subprocess is available that is known to be capable of satisfying quality and process-performance objectives (i.e., no capable subprocess is available or the capability of the subprocess is not known).
Even when a subprocess has not been selected to be statistically managed, historical data and process-performance models may indicate that the subprocess is not capable of satisfying quality and process-performance objectives.
Refer to the Risk Management process area for more information about identifying and analyzing risks.
SP 1.3 Select Subprocesses to Be Statistically Managed
Select subprocesses of the project’s defined process to be statistically managed.
Selecting subprocesses to be statistically managed is often a concurrent and iterative process of identifying applicable project and organization quality and process-performance objectives, selecting subprocesses, and identifying process and product attributes to measure and control. Often the selection of a process, quality and process-performance objective, or measurable attribute will constrain the selection of the other two. For example, if a particular process is selected, measurable attributes and quality and process-performance objectives may be constrained by that process.
Typical Work Products
1. Quality and process-performance objectives to be addressed by statistical management
2. Criteria used to select which subprocesses will be statistically managed
3. Subprocesses to be statistically managed
4. Identified process and product attributes of selected subprocesses that should be measured and controlled
Subpractices
1. Identify which of the project’s quality and process-performance objectives will be statistically managed.
2. Identify criteria to be used in selecting subprocesses that are the main contributors to achieving identified quality and process-performance objectives and for which predictable performance is important.
Examples of sources for criteria used in selecting subprocesses include the following:
• Stakeholder requirements related to quality and process performance
• Quality and process-performance objectives established by the customer
• Quality and process-performance objectives established by the organization
• The organization’s performance baselines and models
• Stable performance of the subprocess on other projects
• Laws and regulations
3. Select subprocesses to be statistically managed using selection criteria.
It may not be possible to statistically manage some subprocesses (e.g., where new subprocesses and technologies are being piloted). In other cases, it may not be economically justifiable to apply statistical techniques to certain subprocesses.
4. Identify product and process attributes of selected subprocesses to be measured and controlled.
Examples of product and process attributes include the following:
• Percentage compliance to the service level agreement
• Response time
SP 1.4 Manage Project Performance
Monitor the project to determine whether the project’s objectives for quality and process performance will be satisfied, and identify corrective action as appropriate.
Refer to the Measurement and Analysis process area for more information about obtaining and analyzing measurement data.
A prerequisite for such a determination is that the selected subprocesses of the project’s defined process are statistically managed and their process capability is understood. Specific practices of specific goal 2 provide detail on statistically managing selected subprocesses.
Typical Work Products
1. Estimates (i.e., predictions) of the achievement of the project’s quality and process-performance objectives
2. Documentation of risks in achieving the project’s quality and process-performance objectives
3. Documentation of actions needed to address deficiencies in achieving project objectives
Subpractices
1. Periodically review the performance and capability of each subprocess selected to be statistically managed to appraise progress toward achieving the project’s quality and process-performance objectives.
The process capability of each selected subprocess is determined with respect to that subprocess’ established quality and process-performance objectives. These objectives are derived from the project’s quality and process-performance objectives, which are defined for the project as a whole.
2. Periodically review actual results achieved against established interim objectives for each phase of the project lifecycle to appraise progress toward achieving the project’s quality and process-performance objectives.
3. Track supplier results for achieving their quality and process-performance objectives.
4. Use process-performance models calibrated with obtained measures of critical attributes to estimate progress toward achieving the project’s quality and process-performance objectives.
Process-performance models are used to estimate progress toward achieving objectives that cannot be measured until a future phase in the project lifecycle. An example is the use of process-performance models to forecast frequency of downtime using interim measures of mean time to repair.
Refer to the Organizational Process Performance process area for more information about establishing process-performance models.
Calibration of process-performance models is based on the results obtained from performing the previous subpractices.
5. Identify and manage risks associated with achieving the project’s quality and process-performance objectives.
Refer to the Risk Management process area for more information about identifying, analyzing, and mitigating risks.
Example sources of risks include the following:
• Inadequate stability and capability data in the organization’s measurement repository
• Subprocesses having inadequate performance or capability
• Suppliers not achieving their quality and process-performance objectives
• Lack of visibility into supplier capability
• Inaccuracies in the organization’s process-performance models for predicting future performance
• Deficiencies in predicted process performance (estimated progress)
• Other identified risks associated with identified deficiencies
6. Determine and document actions needed to address deficiencies in achieving the project’s quality and process-performance objectives.
The intent of these actions is to plan and deploy the right set of activities, resources, and schedule to place the project back on a path toward achieving its objectives.
Examples of actions that can be taken to address deficiencies in achieving the project’s objectives include the following:
• Changing quality and process-performance objectives so that they are within the expected range of the project’s defined process
• Improving the implementation of the project’s defined process to reduce its normal variability (Reducing variability may bring the project’s performance within the objectives without having to move the mean.)
• Adopting new subprocesses and technologies that have the potential for satisfying objectives and managing associated risks
• Identifying the risk and risk mitigation strategies for deficiencies
• Terminating the project
Refer to the Project Monitoring and Control process area for more information about managing corrective action to closure.
SG 2 Statistically Manage Subprocess Performance
The performance of selected subprocesses within the project’s defined process is statistically managed.
This specific goal describes an activity critical to achieving the Quantitatively Manage the Project specific goal of this process area. The specific practices under this specific goal describe how to statistically manage subprocesses whose selection was described in specific practices under specific goal 1. When selected subprocesses are statistically managed, their capability to achieve their objectives can be determined. By these means, it is possible to predict whether the project will be able to achieve its objectives, which is crucial to quantitatively managing the project.
SP 2.1 Select Measures and Analytic Techniques
Select measures and analytic techniques to be used in statistically managing selected subprocesses.
Refer to the Measurement and Analysis process area for more information about aligning measurement and analysis activities and providing measurement results.
Typical Work Products
1. Definitions of measures and analytic techniques to be used to statistically manage subprocesses
2. Operational definitions of measures, their collection points in subprocesses, and how the integrity of measures will be determined
3. Traceability of measures back to the project’s quality and process-performance objectives
4. Instrumented organizational support environment that supports automatic data collection
Subpractices
1. Identify common measures from the organizational process assets that support statistical management.
Refer to the Organizational Process Definition process area for more information about defining a common set of process and product measures for the organization’s set of standard processes.
Refer to the Organizational Process Performance process area for more information about common measures.
Product lines or other stratification criteria may categorize common measures.
2. Identify additional measures that may be needed for this instance to cover critical product and process attributes of the selected subprocesses.
In some cases, measures may be research oriented. Such measures should be explicitly identified.
3. Identify the measures that are appropriate for statistical management.
Critical criteria for selecting statistical management measures include the following:
• Controllable (e.g., Can a measure’s values be changed by changing how the subprocess is implemented?)
• Adequate performance indicator (e.g., Is the measure a good indicator of how well the subprocess is performing relative to the objectives of interest?)
Examples of subprocess measures include the following:
• Requirements volatility
• Ratios of estimated to measured values of planning parameters (e.g., size, cost, schedule)
• Effectiveness of training (e.g., percent of planned training completed, test scores)
4. Specify the operational definitions of measures, their collection points in subprocesses, and how the integrity of measures will be determined.
Operational definitions are stated in precise and unambiguous terms. They address two important criteria:
• Communication: What has been measured, how it was measured, what are the units of measure, and what has been included or excluded?
• Repeatability: Can the measurement be repeated, given the same definition, to get the same results?
5. Analyze the relationship of identified measures to the objectives of the organization and its projects, and derive objectives that state target measures or ranges to be met for each measured attribute of each selected subprocess.
6. Instrument the organizational or project support environment to support collection, derivation, and analysis of statistical measures.
This instrumentation is based on the following:
• Description of the organization’s set of standard processes
• Description of the project’s defined process
• Capabilities of the organizational or project support environment
7. Identify appropriate statistical analysis techniques that are expected to be useful in statistically managing the selected subprocesses.
The concept of one size fits all does not apply to statistical analysis techniques. What makes a particular technique appropriate is not just the type of measures but, more important, how the measures will be used and whether the situation warrants applying that technique. The appropriateness of the selection may need to be reviewed from time to time.
Examples of statistical analysis techniques are given in the next specific practice.
8. Revise measures and statistical analysis techniques as necessary.
SP 2.2 Apply Statistical Methods to Understand Variation
Establish and maintain an understanding of the variation of selected subprocesses using selected measures and analytic techniques.
Refer to the Measurement and Analysis process area for more information about aligning measurement and analysis activities and providing measurement results.
Understanding variation is achieved, in part, by collecting and analyzing process and product measures so that special causes of variation can be identified and addressed to achieve predictable performance.
A special cause of process variation is characterized by an unexpected change in process performance. Special causes are also known as “assignable causes” because they can be identified, analyzed, and addressed to prevent recurrence.
The identification of special causes of variation is based on departures from the system of common causes of variation. These departures can be identified by the presence of extreme values or other identifiable patterns in data collected from the subprocess or associated work products. Typically, knowledge of variation and insight about potential sources of anomalous patterns is needed to detect special causes of variation.
Sources of anomalous patterns of variation may include the following:
• Lack of process compliance
• Undistinguished influences of multiple underlying subprocesses on the data
• Ordering or timing of activities within the subprocess
• Uncontrolled inputs to the subprocess
• Environmental changes during subprocess execution
• Schedule pressure
• Inappropriate sampling or grouping of data
Typical Work Products
1. Collected measurements
2. Natural bounds of process performance for each measured attribute of each selected subprocess
3. Process performance compared to the natural bounds of process performance for each measured attribute of each selected subprocess
Subpractices
1. Establish trial natural bounds for subprocesses having suitable historical performance data.
Refer to the Organizational Process Performance process area for more information about establishing process-performance baselines.
Natural bounds of an attribute are the range within which variation normally occurs. All processes show some variation in process and product measures each time they are executed. The issue is whether this variation is due to common causes of variation in the normal performance of the process or to some special cause that can and should be identified and removed.
When a subprocess is initially executed, suitable data for establishing trial natural bounds are sometimes available from prior instances of the subprocess or comparable subprocesses, process-performance baselines, or process-performance models. Typically, these data are contained in the organization’s measurement repository. As the subprocess is executed, data specific to that instance are collected and used to update and replace the trial natural bounds. However, if the subprocess has been materially tailored or if conditions are materially different from those in previous instantiations, data in the repository may not be relevant and should not be used.
In some cases, there may be no comparable historical data (e.g., when introducing a new subprocess, when entering a new application domain, when significant changes have been made to the subprocess). In such cases, trial natural bounds will have to be made from early process data of this subprocess. These trial natural bounds must then be refined and updated as subprocess execution continues.
Examples of criteria for determining whether data are comparable include the following:
• Standard services and service lines
• Application domain
• Work product and task attributes
• Service system attributes (e.g., size, complexity, number of stakeholders)
2. Collect data, as defined by selected measures, on subprocesses as they execute.
3. Calculate the natural bounds of process performance for each measured attribute.
Examples of statistical techniques for calculating natural bounds include the following:
• Control charts
• Confidence intervals (for parameters of distributions)
• Prediction intervals (for future outcomes)
4. Identify special causes of variation.
An example of a criterion for detecting a special cause of process variation in a control chart is a data point that falls outside 3-sigma control limits.
The criteria for detecting special causes of variation are based on statistical theory and experience and depend on economic justification. As criteria are added, special causes are more likely to be identified if they are present, but the likelihood of false alarms also increases.
5. Analyze special causes of process variation to determine the reasons why the anomaly occurred.
Examples of techniques for analyzing the reasons for special causes of variation include the following:
• Cause-and-effect (i.e., fishbone) diagrams
• Designed experiments
• Control charts (applied to subprocess inputs or lower level subprocesses)
• Subgrouping (i.e., analyzing the same data segregated into smaller groups based on an understanding of how the subprocess was implemented facilitates isolation of special causes)
Some anomalies may simply be extremes of the underlying distribution rather than problems. Those implementing a subprocess are usually the ones best able to analyze and understand special causes of variation.
6. Determine the corrective action to be taken when special causes of variation are identified.
Removing a special cause of process variation does not change the underlying subprocess. It addresses an error or condition in the execution of the subprocess.
Refer to the Project Monitoring and Control process area for more information about managing corrective action to closure.
7. Recalculate natural bounds for each measured attribute of the selected subprocesses as necessary.
Recalculating the (statistically estimated) natural bounds is based on measured values that signify that the subprocess has changed. It is not based on expectations or arbitrary decisions.
Examples of when natural bounds may need to be recalculated include the following:
• There are incremental improvements to the subprocess
• New tools are deployed for the subprocess
• A new subprocess is deployed
• The collected measures suggest that the subprocess mean has permanently shifted or subprocess variation has permanently changed
SP 2.3 Monitor the Performance of Selected Subprocesses
Monitor the performance of selected subprocesses to determine their capability to satisfy their quality and process-performance objectives, and identify corrective action as necessary.
The intent of this specific practice is to do the following:
• Statistically determine process behavior expected from the subprocess
• Appraise the probability that the subprocess will meet its quality and process-performance objectives
• Identify the corrective action to be taken based on a statistical analysis of process-performance data
Corrective actions may include renegotiating affected project objectives, identifying and implementing alternative subprocesses, or identifying and measuring lower level subprocesses to achieve greater detail in performance data.
These actions are intended to help the project use a more capable process. (See the definition of “capable process” in the glossary.)
A prerequisite for comparing the capability of a selected subprocess against its quality and process-performance objectives is that the measured attributes of the subprocess indicate that its performance is stable and predictable.
Process capability is analyzed for those subprocesses and measured attributes for which (derived) objectives are established. Not all subprocesses or measured attributes that are statistically managed are analyzed regarding process capability.
Historical data may be inadequate for initially determining whether the subprocess is capable. It also is possible that the estimated natural bounds for subprocess performance may shift away from quality and process-performance objectives. In either case, statistical control implies monitoring capability as well as stability.
Typical Work Products
1. Natural bounds of process performance for each selected subprocess compared to its established (derived) objectives
2. The process capability of each subprocess
3. The actions needed to address deficiencies in the process capability of each subprocess
Subpractices
1. Compare quality and process-performance objectives to the natural bounds of the measured attribute.
This comparison provides an appraisal of the process capability for each measured attribute of a subprocess. These comparisons can be displayed graphically in ways that relate the estimated natural bounds to the objectives or as process capability indices, which summarize the relationship of objectives to natural bounds.
2. Monitor changes in quality and process-performance objectives and the process capability of the selected subprocess.
3. Identify and document deficiencies in subprocess capability.
4. Determine and document actions needed to address deficiencies in subprocess capability.
Examples of actions that can be taken when the performance of a selected subprocess does not satisfy its objectives include the following:
• Rederiving quality and process-performance objectives for each selected subprocess so that they can be met given the performance of the selected subprocess
• Improving the implementation of the existing subprocess to reduce its normal variability (Reducing variability may bring natural bounds within the objectives without having to move the mean.)
• Adopting new process elements, subprocesses, and technologies that have the potential to satisfy objectives and manage associated risks
• Identifying risks and risk mitigation strategies for each deficiency in subprocess capability
Refer to the Project Monitoring and Control process area for more information about managing corrective action to closure.
SP 2.4 Record Statistical Management Data
Record statistical and quality management data in the organization’s measurement repository.
Refer to the Measurement and Analysis process area for more information about aligning measurement and analysis activities and providing measurement results.
Refer to the Organizational Process Definition process area for more information about the organization’s measurement repository.
Typical Work Products
1. Statistical and quality management data recorded in the organization’s measurement repository