Project Quality Management
Overview:
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Quality: the degree to which a set of inherent characteristics
fulfills requirements, decrease rework/costs, increase
productivity/stakeholder satisfaction
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everyone
in the organization is responsible for the quality (project team for destined
parts while PM for project quality), the project manager is ultimately
responsible for the project quality
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The
project manager is required to perform continuous improvement activities
(quality assurance), verify quality before completion of deliverables (control
quality)
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prevention
over inspection
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continuous
improvement to ensure quality (quality assurance) — Quality Management
processes are so focused on reviewing EVERY deliverable –
not just the final product, but all of the components, designsand specifications too.
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some
costs of quality will be borne by the organization (organization quality
policy, e.g. quality audit, ISO accreditation)
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Quality
Management Concepts
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Crosby Zero
Defects:
identify processes to remove defects, quality is built in to the processes
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Juran Fitness
for Use: does
the product/service meet customer’s need? i) Grade, ii) Quality conformance,
iii) Reliability/maintainability, iv) Safety, v) Actual Use
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W.
Edwards Deming: 85% of
quality problem is managers’ responsibility, develop “System of Profound
Knowledge” [system = components working together to achieve an aim] i)
Appreciation for system, ii) Knowledge about variation (special cause vs common
cause) , iii) Theory of Knowledge (built up by
prediction/observation/adjustment) , iv) Psychology
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Six
Sigma: achieve
3.4/1 mil defect level (99.999%) using DMAIC (Define, Measure, Analyze,
Implement, Control) or [Design for Six Sigma] DMADV (Define, Measure,
Analyze, Design, Validate) approach, refine the
process to get rid of human error and outside influences with precise
measurements, variations are random in nature
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Just
In Time:
eliminate build up of inventory
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Total
Quality Management (TQM): ISO 8402 all members to center on quality to drive customer satisfaction , refine the process of producing the
product
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Kaizen: implement consistent and
incremental improvement, to reduce costs, cycle time, drive customer
satisfaction using PDCA (Plan Do Check Act)
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The Plan-Do-Check-Act cycle is a way of making small
improvements and testing their impact before you make a change to the process
as a whole. It comes from W. Edwards Deming’s work in process improvement,
which popularized the cycle that was originally invented by Walter Shewhart in
the 1930s.
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Capability
Maturity Model Integration (CMMI): improve overall software quality (design,
development and deployment)
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ISO9000:
ensures the defined processes are performed in accordance to the plan
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Important
Terms:
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outliers are singular measurements outside
the control limits
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under
control: the
process is predictable and repeatable
Plan Quality Management
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Inputs: Project Charter, Project
Management Plan, Project Documents (assumption log, requirements traceability
matrix, risk register, stakeholder register), EEF, OPA
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Tools
& Techniques: Expert Judgement, Data Gathering, Data Analysis, Decision Making, Data
Representation, Test and Inspection Planning, Meetings
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Outputs: Quality Management Plan, Quality
Metrics, Project Management Plan Updates, Project Documents Updates
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quality
policy (either organizational or just for the project), methods and procedures
to meet the objectives and satisfy customer’s needs
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including
identifying the quality requirements and document how to achieve
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The goal
is to refine the process so that human errors and outside influences no longer
exist, and any remaining variations are completely random
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Quality
Metrics:
function points, MTBF (mean time between failure), MTTR (mean time to repair)
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Data
Gathering Tools:
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Benchmarking: compare to past
activities/standard/competition
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Brainstorming
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Interviews
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Data
Analysis tools:
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Cost-benefit
Analysis: cost of
implementing quality requirements against benefits
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Cost
of Quality:
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Cost of
Quality is the total cost of quality efforts throughout the product’s lifecycle
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cost of
conformance + cost of non-conformance: cost of conformance (prevention cost,
appraisal cost) vs cost of non-conformance (failure cost [internal/external])
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lowest
quality cost is prevention, highest quality cost (poor quality) is
rework and defect repair (as high as 5000 times the cost for carrying out unit
testing), lost reputation and sales, failure cost may
be internal/external (found by customer)
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Warranty
claims are external cost
of quality — internal/external is reference to the project (not the
organization)
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Cause-and-effect
/ Ishikawa /
Fishbone Diagram: for identifying the cause
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Flowchart: (e.g. SIPOC diagram) for identifying failing process
steps and process improvement opportunities
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Check
Sheets (tally sheets): collecting data/documenting steps for defeat analysis
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Histograms: does not consider the influence
of time on the variation that exits within a distribution
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Pareto
Chart: based
on 80/20 principle, a prioritization tool to identify critical issues in
descending order of frequency, sort of a histogram
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Statistical
Process Control (SPC) Chart: determine if a process is stable/predictable using statistical
sampling (assessed by accuracy[conformance]
and precision[standard deviation]), identity the internally
computed control limits (UCL/LCL) and specification limits (USL/LSL) by the customer/PM
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run
chart is similar to control chart, but without the control
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usually
+-3sigma i.e. a range of 6 sigma
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a form of time series
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if a
process is within control limit but beyond specification limit,
the process is experiencing common cause variation
(random) that cannot be corrected by the system, management
help is needed (special cause can be tackled but NOT common cause)
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Stability
Analysis / Zone Test: rule of seven (7 consecutive on either
side of the mean = out of control), rule of six (six
consecutive with a trend = out of control), rule of ten (10 as a saw-tooth pattern around the mean),
rule of one (1 point beyond control limit) [signal in the noise]
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Scatter
Diagram: for
trending, a form of regression analysis
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Data
Representation tools:
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Flowcharts
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Logical
data model
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Matrix
Diagrams: House of Quality (HOQ) used in Quality Function Deployment (QFD)
(method to transform user demands [VOC] into design quality)
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Mind
mapping
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Test
and Inspection Planning
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used to
determine how to test or inspect the product, deliverable, or service to meet
the stakeholders’ needs and expectations
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alpha/beta
releases
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inspections
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field
tests
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Other
quality tools:
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Design
of Experiments (DOE): change
several factors at a time for each experiment, to
determine testing approaches and their impact on cost of quality
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Loss
Function: a
financial measure of the user’s dissatisfaction with product performance
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Kano
Model:
differentiate features as satisfy, delight or
dissatisfy
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Marginal
Analysis:
cost-benefit analysis
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Force
Field Analysis (FFA):
reviews any proposed action with proactive and opposing forces
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Process
Improvement Plan: process boundaries, configuration, process
metrics/efficiencies, targets for improved performance
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Quality
Checklists: checklist to verify a series of steps have been performed
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Marginal
Analysis: ROI of quality measures
Manage Quality (formerly Perform
Quality Assurance)
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Inputs: Project Management Plan,
Project Documents, OPA
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Tools
& Techniques: Data Gathering, Data Analysis, Decision Making, Data Representation,
Audits, Design for X (DfX), Problem Solving, Quality
Improvement Methods
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Outputs: Quality Report, Test and Evaluation
Documents, Change
Requests, Project Management Plan Updates, Project Documents Updates
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Manage
Quality is in the Executing Process Group
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ensures the quality standards are
being followed, to ensure unfinished works would meet the quality requirements
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by
quality assurance department or sponsor/customer not actively involved in the
project
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primarily
concerned with overall process improvement for
activities and processes (rather than the deliverable)
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utilize
the data collected in Control Quality Process
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Data
Analysis tools:
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Alternatives
analysis
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Document
analysis
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Process
analysis — to identify opportunities for process improvements
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Root
cause analysis
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Data
Representation tools:
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Affinity
Diagrams: like a
mind-mapping diagram, organize thoughts on how to solve problems
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Cause and
Effect Diagrams
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Flowcharts
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Histograms
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Matrix
Diagrams: e.g. ‘house of quality’ in QFD
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Scatter
Diagrams
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a set of
technical guidelines that may be applied during the design phase of a product
to optimize specific aspects of the design
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may be
able to improve the final characteristics of the product (in aspects like cost
reduction, quality improvement, better performance and customer satisfaction)
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Quality
Audit: to
verify quality of processes, to seek improvement, identify best practices,
reduce overall cost of quality, confirm implementation of approved changes,
need quality documentation
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Quality
Review: to
review the quality management plan
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change
requests are mostly procedural changes
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Quality
Improvement Methods are used to analyze and evaluate opportunities for
improvements:
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Plan-Do-Check-Act
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Six Sigma
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Quality
Reports
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can be
graphical, numerical or qualitative
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to
provide information to help take corrective actions to fulfil project quality
expectations
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includes
quality management issues escalated by the team; recommendations for process,
project and product improvements; corrective action recommendations (can be
rework, defect, bug repair, inspection, etc.), summary findings
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Test
and Evaluation Documents
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used to
evaluate the achievement of quality objectives
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may
include checklists and detailed requirements traceability matrices
Control Quality
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Inputs: Project Management Plan,
Project Documents, Approved Change Requests,
Deliverables, Work Performance Data, EEF, OPA
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Tools
& Techniques: Data Gathering, Data Analysis, Inspection, Testing/product Evaluations,
Data Representation, Meetings
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Outputs: Quality Control
Measurements, Verified Deliverables, Work
Performance Information, Change Requests, Project Management Plan Updates,
Project Documents Updates
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verify
the deliverables against customer’s specifications to ensure customer satisfaction
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validate
the changes against
the original approved change requests
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conditional
probability (events somewhat related) vs statistical
independence (events not interrelated) vs mutual
exclusivity
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statistical
sampling for control quality
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variable
(continuous) data:
measurements, can do maths on e.g. average
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attribute
(discrete) data: yes/no,
no.123, just an identifier, can’t do maths on
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QC
includes the PM process (lesson learnt, budget, scope)
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tolerance
(spec limits,
deliverables acceptable) vs control limits (process acceptable)
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if within
control limit but outside tolerance: rework the process to
give better precision
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all
control and execution processes may generate lesson learned
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Data
Analysis Techniques:
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Performance
reviews
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Root
cause analysis
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Testing/Product
Evaluations
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an
organized investigation to provide objective information about the quality of
the product/service in accordance with project requirements
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