Academy for Jewish Religion Business Reflective Learning Essay

DescriptionManaging Quality: Integrating The Supply
Chain
Sixth Edition
Chapter 14
Managing Quality
Improvement Teams and
Projects
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Introduction
• Employee participation is a key element for managing
changing organizations in an increasingly complex world
due to:
– Complexity in the workplace
– An increase in collaboration
▪ Change from routine work to knowledge work
▪ More teamwork
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Why Employees Enjoy Teams
• Mutuality
• Recognition for personal achievement
• Belonging
• Bounded power
• Creative autonomy
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Leading Teams for Quality Improvement (1
of 7)
• Employee empowerment and involvement:
– When using teams, decision-making authority is given
to team members
– Empowerment:
▪ Giving power to team members who perhaps had
little control over their jobs
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Leading Teams for Quality Improvement (2
of 7)
• Promises to employees implicit to empowerment:
– You will have greater control over your own work.
– You will not be penalized for making painful changes.
– Management is changing and becoming more
contemporary.
– Management is committed to quality improvement
over the long haul.
– Management will concede more control over
company systems to you.
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Leading Teams for Quality Improvement (3
of 7)
• Promises to employees implicit to empowerment:
– Management values your ideas and opinions and will
give them serious consideration.
– Management trusts you and is worthy of trust in
return.
– You will be rewarded for making decisions that benefit
the company.
– Labor is capable of decision making concerning its
own jobs and company processes.
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Leading Teams for Quality Improvement (4
of 7)
• Preconditions necessary for empowerment:
– Clear authority and accountability
– Participation in planning at all levels
– Adequate communication and information for decision
making
– Responsibility with authority
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Leading Teams for Quality Improvement (5
of 7)
• Flattening hierarchies for improved effectiveness:
– Too many layers of management can impede
creativity, stifle initiative, and make empowerment
impossible.
– With few layers of management, companies tend to
rely more on teams.
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Leading Teams for Quality Improvement (6
of 7)
• Team leader roles and responsibilities:
– Situational leadership is based on interplay among
the following:
▪ The amount of guidance and direction a leader
gives (task behavior)
▪ The amount of socioeconomic support a leader
provides (relationship behavior)
▪ The readiness level that followers exhibit in
performing a specific task, function, or objective
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Figure 1 Leading Teams for Quality
Improvement
• Hersey, Blanchard, and
Johnson model of
situational leadership
• Hersey, P., Blanchard, K.,
and Johnson, D.,
Management of
Organizational Behavior
(Englewood Cliffs, NJ:
Prentice Hall, 2007).
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Table 1 Leading Teams for Quality
Improvement
Key Stages of Team Activity
Team Roles Relevant to Particular Stages
Identifying needs
Key figures at this stage are individuals with a strong goal awareness.
Shapers and coordinators make their mark here.
Finding ideas
Once an objective is set, the means of achieving it are required. Here plant
and resource investigators play a crucial role.
Formulating plans
Two activities help ideas turn into plans. One is weighing up the options;
the second, making good use of all relevant experience and knowledge to
ensure a good decision.
Making contacts
Making contacts People must be persuaded that an improvement is
possible. Champions of the plans and cheerleaders must be found. This is
an activity where resource investigators are in their element.
Establishing the organization
Establishing the organization Plans must turn into procedures, methods,
and working practices to become routines. Implementers are the people
required here. Identify people to fit the system.
Following through
Too many assumptions are made that all will work out well in the end.
Good follow through benefits from the attentions of completers.
Implementers, too, pull their weight in this area because they pride
themselves on being efficient in anything they undertake.
Belbin’s Team Roles
Based on R. M. Belbin, Team Roles at Work (Boston: Butterworth-Heinemann, 2010).
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Leading Teams for Quality Improvement (7
of 7)
• Team formation and evolution steps:
– Forming
– Storming
– Norming
– Performing
– Mourning
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Table 2 Leading Teams for Quality
Improvement
1. Test assumptions and inferences.
2. Share all relevant information.
3. Focus on interests, not positions.
4. Be specific-use examples.
5. Agree on what important words mean.
6. Explain the reasons behind one’s statements, questions, and actions.
7. Disagree openly with any member of the group.
8. Make statements; then invite questions and comments.
9. Jointly design ways to test disagreements and solutions.
10. Discuss undiscussable issues.
11. Keep the discussion focused.
12. Do not take cheap shots or otherwise distract the group.
13. All members are expected to participate in all phases of the process.
14. Exchange relevant information with non group members.
15. Make decisions by consensus.
16. Do self-critiques.
Ground Rules for Effective Teams
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Types of Teams
• Process improvement teams
• Cross-functional teams
• Tiger teams
• Natural work groups
• Self-directed work teams
• Virtual teams
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Implementing Teams
• Facilitation
• Team building
• Meeting management steps:
– Define an agenda.
– Develop meeting objectives.
– Design the agenda activity outline.
– Use process techniques.
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Table 3 Structured Process Activities
By Activity Type
Approaches
Tools
• Clarify techniques
—Lasso
• Generate techniques
—Structured brainstorming
—Round-robin contribution
—Silent writing
—Sticky notes recording
—Brain writing
• Evaluate techniques
—Reduce list
—Pros and cons
—Force fields
—Silent voting
—Sticky dots
—Idea writing
• Action planning
Based on Anson, R., “Facilitation Skills for Focused Meetings,” Working Paper (Boise, ID: Boise State University,
2012).
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Conflict Resolution in Teams (1 of 4)
• Team leaders and project managers spend more than
20% of their time resolving conflict.
• Four recognizable stages in the conflict resolution
process:
– Frustration
– Conceptualization and orientation
– Interaction
– Outcome
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Conflict Resolution in Teams (2 of 4)
Modes of Conflict Behavior
Based on T. L. Ruble and K. W. Thomas, “Support for a Two-Dimensional Model of Conflict Behavior,”
Organizational Behavior and Human Decision Processes 16 (1976): 221–237, with permission of Elsevier
Science.
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Conflict Resolution in Teams (3 of 4)
• Leaders resolve conflicts in a variety of ways:
– Passive conflict resolution
– Win-win
– Structured problem solving
– Confronting conflict
– Choosing a winner
– Selecting a better alternative
– Preventing conflict
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Table 4 Conflict Resolution in Teams
1. Goal structure – Goals should be well-defined and operational, and should reflect each unit’s contribution to the total
organization. Managers, in turn, should convey to their subordinates as precisely as possible the feeling that their own
unit is dependent on the work of other units.
2. Reward systems – Each unit’s contribution to the effectiveness of the total organization should be assessed carefully
and rewarded accordingly. Where high levels of interdependence exist, reward systems should be designed specifically
to reflect interdependence. Such a reward system will encourage cooperation among organization units.
3. Contact and communication – Frequent contact and communication between organization units needs to be
encouraged. Individuals should be rotated through related organization units in order to have them gain experience,
understanding, and empathy for the work done and the problems encountered in other units.
4. Coordination – Liaison roles should be established when potential communication and coordination problems exist
(e.g., between R&D and manufacturing at the point where a new product moves from advanced development into the
pilot stage of production). The liaison role can be used to facilitate necessary interaction, thus reducing the time and
information content lost when using formal channels. In addition, the liaison becomes better acquainted with the work
of the different units and can provide continuous updating to each of the other units.
5. Competitive systems – Competition, where it does exist, should be examined carefully. Although competition can
facilitate productivity, it can also produce conflict whenever organization units are interdependent. In such situations,
competition need not be eliminated, but its benefits should be evaluated against its potential for causing conflict.
Clearly, organization units should not be forced into win-lose situations.
Constructive Conflict Resolution Components
Based on Ronald J. Ebert, Charles N. Greene, and Everett E. Adam Jr., Management for Effective Performance,
1st ed. (Upper Saddle River, NJ: Pearson Education, 1985), p. 454. ISBN: 0135485045.
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Conflict Resolution in Teams (4 of 4)
• Conflict-resolution approach alternatives:
– Avoidance
– Defusion
– Confrontation
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Saving Quality Teams from Failure: Diagnosing
and Intervening Before it is Too Late
• When failure occurs, the diagnosis-intervention cycle
must be undertaken:
– The cycle is followed to diagnose team failure and to
intervene before the team fails.
– This requires observing the behavior of members and
then deciding whether to intervene to improve the
behavior.
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Managing and Controlling Projects (1 of 2)
• Qualifying projects:
– Determine the worthiness of a project on different
dimensions.
– Cost-benefits analysis (CBA) using payback period
calculations are often used for comparing and
selecting projects.
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Managing and Controlling Projects (2 of 2)
Payback period calculations:
Ct =  (Cd + Ci )
PP = Ct / Cb
Where:
where:
C t = total project costs
PP = payback period
Cd = direct project costs
Ct = total project costs
Ci = indirect project costs
Ba = annualized benefits
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Example 1 (1 of 3)
• A Six Sigma master black belt has asked you to help analyze a
possible project. This project involves implementing a computerbased sales system to improve supply chain performance.
• Some of the direct and indirect costs are as follows:
• Direct Costs
– Twenty networkable PCs-$1,500/each
– A server -$2,000
– Peripherals-$2,000
– Network installation-$5,000
– Sales system software-$10,000
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Example 1 (2 of 3)
• Indirect Costs
– Training-$10,000
– Lost time-30 days*$120/day
– Sales-related losses during implementation-$25,000
• Annualized Benefits
– Increased sales capacity-$200,000
– Improved customer retention-$500,000
– Improved follow-up sales opportunities-$100,000
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Example 1 (3 of 3)
• Solution:
– Total costs = $49,000 + $38,600 = $87,600
– Benefits = $800,000 per year
– Payback period = $87,600/$800,000 = .11 years
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Figure Managing and Controlling Projects
• Project charters:
– Simple tools to help
teams identify
objectives,
participants, and
expected benefits from
projects
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Managing and Controlling Projects (1 of 4)
• Force-field analysis
– A tool that is designed to identify and quantify all the
forces for or against an organizational change
• Three steps of force-field analysis:
– 1. List all forces for change in the first column and all
forces against change in the third column.
– 2. Assign a score for each force, where 1 = weak and
5 = very strong.
– 3. Sum the forces for and against the change and
draw a diagram showing the forces.
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Figure 1 Example 2
• A project was performed to
determine the feasibility of
implementing a new
customer relationship
management system (CRM
S). A group of experts within
the firm identified major
forces for and against
implementing the new CRM
S.
• These forces and scores are
shown in Figure 14-6.
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Managing and Controlling Projects (2 of 4)
• Work breakdown structure (WBS)
– A tool used for determining the tasks to complete a
project
• Identifying precedence relationships
• Identifying outcome measures
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Managing and Controlling Projects (3 of 4)
• Identifying task times:
– a = Optimistic completion time
– m = Most likely completion time
– b = Pessimistic completion time
– t = Task time
– T = Total project completion time
Expected time = (at + 4mt + bt ) / 6
• The task variance is computed as

2
= [(bt − at ) / 6]2
t
2 n 2
• The project variance is computed as the sum of task variances  =  
T t =l t
2
• Finally, the project standard deviation T = 
T
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Table 6 Managing and Controlling Projects
Tasks
Workdays
Start Date
End Date
Preceding
Tasks
1. Use flowcharts to list steps
5
Sat 6/9/12
Fri 6/15/12
Blank
2. Preliminary design of form
5
Sat 6/16/12
Fri 6/22/12
1
3. Check against Roles team and
Andrea
10
Sat 6/16/12
Fri 6/29/12
Blank
4. Develop procedures for using form
10
Sat 6/16/12
Fri 6/29/12
Blank
5. Preliminary draft of form completed
5
Sat 6/30/12
Fri 6/6/12
4,2,3
6. Test-run form and procedures
40
Sat 6/7/12
Fri 6/31/12
5
7. Revise and finalize form and
procedures
10
Sat 6/1/12
Fri 6/14/12
6
Example of tasks to use in work breakdown structure
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Figure 1 Managing and Controlling
Projects
Work Breakdown Structure (WBS)
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Managing and Controlling Projects (4 of 4)
• Creating an activity network diagram (PERT chart) involves
the following steps:
– Using the inputs from a tree diagram that lists the tasks to
be performed in the project, list all the tasks.
– Determine task times.
– Determine which tasks depend on the completion of
others.
– Draw the network diagram.
– Compute early start and early finish times.
– Compute late start and late finish times.
– Compute slack times and determine the critical path.
▪ Slack time = late start – early start
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Example 4 (1 of 2)
• A company developing a new advertising brochure has
identified the steps in the project.
• These were placed in the tree diagram shown in Figure
14-8.
• Table 14-7 lists all the tasks with their brainstormed times
and predecessors.
• Figure 14-9 shows the network with tasks. This also
shows times and precedence relationships using activityon-node (AON).
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Table 1 Example 4 (1 of 2)
Task
Task IDs
Predecessors
Expected
Time (Weeks)
Brochure printed
A
B,C
4
Assemble text and artwork
B
E,D
1
Contract with printer
C
F
2
Write text in Word format
D
H
2
Prepare artwork for printing
E
I
4
Select printer
F
G
2
Send out RFPs
G
Blank
3
Gather information from departments
H
J,K,L
1
Receive work from graphic artist
I
M,N,O
4
Marketing
J
Blank
1
Tasks
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Table 2 Example 4 (2 of 2)
Task
Task IDs
Predecessors
Expected
Time (Weeks)
Finance
K
Blank
1
Operations
L
Blank
1
Review text
M
Blank
1
Specify artwork needed
N
Blank
1
Decide on format
O
Blank
1
Tasks
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Example 4 (2 of 2)
Tree diagram of tasks
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Figure 1 Example 4
AON network
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Figure 2 Example 4
AON network with early times
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Figure 3 Example 4
AON network with late times
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Figure 4 Managing and Controlling
Projects
Arrow Gantt charts
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Figure 5 Managing and Controlling
Projects
Managing multiple projects using a multiple-project control form
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Copyright
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Managing Quality: Integrating The Supply
Chain
Sixth Edition
Chapter 13
Lean-Six Sigma
Management and Tools
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What Is Six Sigma? (2 of 3)
• Six Sigma is a very popular approach to improving quality.
• Sigma (  ) is a Greek letter used to designate a standard deviation
(SD) in statistics.
• Six refers to the number of SDs from a specification limit to the mean
of a highly capable process.
• Began at Motorola in 1982 when its CEO requested that costs be cut
in half and
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What Is Six Sigma?
• Variation between a three-sigma and six-sigma process
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What Is Six Sigma?
Sigma Level
1
2
3
4
5
6
Long-term ppm defects
691,462
308,538
66,807
6,210
233
3.4
Table 13-1 shows the number of defective parts per million (ppm) that are produced between one and Six Sigma levels.
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What Is Six Sigma?
Six Sigma Effectiveness
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What Is Six Sigma? (3 of 3)
• At the core of Six Sigma is the following equation, which
means that an output is a function of inputs and
processes:
Y = f(X)
Where:
Y = output (key business objectives and measures)
f = function (interrelationships to be controlled and
managed)
X = controllable and non controllable variables that affect Y
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Organizing Lean-Six Sigma
• The key players:
– Champion – work with black belts
to identify possible projects
– Master Black Belts – work with
and train new black belts
– Black Belts – committed full time
to completing cost-reduction
projects
– Green Belts – trained in basic
quality tools and work in teams
– Yellow Belts – employees
familiar with improvement
processes
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Packaging Lean with Six Sigma
• Lean-Six Sigma:
– Combination of lean
with Six Sigma, where
firms still generally
follow the DMAIC
process but are also
focused on reducing
wastefulness in the
organization
– Muda-waste
– Pull production
– Value stream map
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DMAIC Overview
• DMAIC stands for:
– Define. Define the project goals and customer
(internal and external) deliverables.
– Measure. Measure the process to determine current
performance.
– Analyze. Analyze and determine the root cause(s) of
the defects.
– Improve. Improve the process by eliminating defects.
– Control. Control future process performance.
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DMAIC Overview
Tools used at each step in the DMAIC cycle
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Define Phase
• In the define phase, projects are identified and selected.
• Four phases:
1. Developing the business case
2. Project evaluation
3. Pareto analysis
4. Project definition
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Define – Developing the Business Case (1 of 2)
• Business case development involves:
– Identifying a group of possible projects
– Writing the business case
– Stratifying the business case into problem statement
and objective statements
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Define – Developing the Business Case (2 of 2)
• Business case
– A short statement outlining the objectives,
measurables, and justification for the project
• Efficacy of a business case (RUMBA)
– Realistic-Are the goals attainable? Is the timeline
feasible?
– Understandable-Do I understand the case?
– Measurable-Do we show the measures?
– Believable-That is a lot of money. Can it be done?
– Actionable-Can it be implemented?
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Measure Phase
• The measure phase involves two major steps:
1. Selecting process outcomes
2. Verifying measurements
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Measure – Selecting Process Outcomes (1 of 3)
• Tools used in the measure phase:
– Process map
– XY matrix
– FMEA
– Gauge R&R
– Capability assessment
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Measure – Selecting Process Outcomes
• Process map:
– A flowchart with
responsibility,
with the goal of
identifying nonvalue-added
activities
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Measure – Selecting Process Outcomes
• XY matrix:
– Used to identify
inputs (Xs) and
outputs (Ys) from a
project you have
mapped and are
desiring to pursue
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Measure – Selecting Process Outcomes (2 of 3)
• Measurement system analysis (MSA)
– Used to determine whether measurements are
consistent
• Gauge repeatability and reproducibility analysis (gauge R
&R)
– Used to determine the accuracy and precision of your
measurements
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Measure – Selecting Process Outcomes (3 of 3)
• Problems in measurements can result for a variety of
reasons:
– The measurement gauges are faulty.
– Operators are using gauges improperly.
– Training in measurement procedures is lacking.
– The gauge is calibrated incorrectly.
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Analyze Phase (1 of 3)
• The analyze phase involves gathering and analyzing data
relative to a particular black-belt project.
• Three steps:
– Define your performance objectives.
– Identify independent variables (Xs).
– Analyze sources of variability.
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Analyze Phase (2 of 3)
• When defining performance objectives, you are
attempting to determine what characteristics in the
process need to be changed to achieve improvement.
– Capability analysis
– Identifying independent variables in which data will be
gathered:
▪ Process maps
▪ XY matrices
▪ Brainstorming
▪ FMEAs
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Figure 13-16 Analyze Phase
Capability Results
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Analyze Phase (3 of 3)
• Analyzing sources of variation:
– Use visual and statistical tools to better understand
the relationship between dependent and independent
variables (Xs and Ys) for use in future
experimentation.
▪ Histograms
▪ Box plots
▪ Scatter plots
▪ Regression analysis
▪ Hypothesis tests
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Improve Phase
• The improve phase involves offline experimentation,
which is studying the variables that we have identified
and using ANOVA to determine whether these
independent variables significantly affect variation in the
dependent variables.
– Taguchi method
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Control Phase
• The control phase involves managing the improved
processes using process charts and implement control
plans.
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Design for Six Sigma (DFSS)
• Used for designing new products and services with high
performance as measured by the customer-based,
critical-to-quality metrics
• DMADV
– Design, measure, analyze, design, verify
• IDOV
– Identify, design, optimize, verify
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Lean-Six Sigma from a Contingency
Perspective
• Reasons for lean-Six Sigma failures:
– Lack of leadership by champions
– Misunderstood roles and responsibilities
– Lack of appropriate culture for improvement
– Resistance to change and the Six Sigma structure
– Faulty strategies for deployment
– Lack of data
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Copyright
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Managing Quality: Integrating The Supply
Chain
Sixth Edition
Chapter 11
Statistically Based Quality
Improvements for Variables
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Statistical Thinking
• Statistical thinking is a decision-making skill
demonstrated by the ability to draw conclusions based on
data.
• Statistical thinking is based on three concepts:
– All work occurs in a system of interconnected
processes.
– All processes have variation (the amount of variation
tends to be underestimated).
– Understanding variation and reducing variation are
important keys to success.
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Why Do Statistics Sometimes Fail in the
Workplace? (1 of 4)
• A lack of knowledge about the tools leads to tools being
misapplied.
• General disdain for all things mathematical creates a
natural barrier to the use of statistics. When was the last
time you heard someone proclaim a love for statistics?
• Cultural barriers in a company make the use of statistics
for continual improvement difficult.
• Statistical specialists have trouble communicating with
managerial generalists.
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Why Do Statistics Sometimes Fail in the
Workplace? (2 of 4)
• Statistics generally are poorly taught, emphasizing
mathematical development rather than application.
• People have a poor understanding of the scientific
method.
• Organizations lack patience in collecting data. All
decisions have to be made “yesterday.”
• Statistics are viewed as something to buttress an
already-held opinion rather than a method for informing
and improving decision making.
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Why Do Statistics Sometimes Fail in the
Workplace? (3 of 4)
• People fear using statistics because they fear they may
violate critical statistical assumptions. Time-ordered data
are messy and require advanced statistical techniques to
be used effectively.
• Most people don’t understand random variation, resulting
in too much process tampering.
• Statistical tools often are reactive and focus on effects
rather than causes.
• When either type I or type II errors occur, erroneous
decisions are made relative to products that can result in
high costs or lost future sales.
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Why Do Statistics Sometimes Fail in the
Workplace? (4 of 4)
• Type 1 error
– Producer’s risk
– Probability that a good product will be rejected
• Type 2 error
– Consumer’s risk
– Probability that a nonconforming product will be
available for sale
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Understanding Process Variation
• All processes exhibit variation
– Some variation can be managed and some cannot be
managed.
• Types of process variation:
– Random
– Nonrandom
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Random Variation
• Also called common
cause
• Centered around the
mean and occurs with a
somewhat consistent
amount of dispersion
• Uncontrolled variation
• May be either large or
small
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Nonrandom Variation
• Also called special cause
variation
• Results from some event
which may be a shift in a
process mean or some
unexpected occurrence
• Dispersion and average
of the process are
changing
• Process is not repeatable
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Process Stability
• The variation that we observe in the process is random
variation and not nonrandom.
• Process charts
– Graphs designed to signal process workers when
nonrandom variation is occurring in a process
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Sampling Methods (1 of 3)
• Reasons why sampling is used:
– Samples are cheaper, take less time, are less
intrusive, and allow the user to frame the sample.
– If quality testing is destructive, 100% inspection would
be impossible.
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Sampling Methods (2 of 3)
• Reasons why 100% inspection is used:
– When a lot of material has been rejected in the past
and materials must be sorted to keep good materials
and return defective materials for a refund
– When employees perform their own in-process
inspection
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Sampling Methods (3 of 3)
• Random samples
– To sample in such a way that every piece or product
has an equal chance of being selected for inspection
• Systematic samples
– To sample according to time or according to
sequence
• Rational subgroup samples
– To sample by a group of data that is logically
homogenous
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Planning for Inspection
• Questions to answer about sampling:
– What type of planning will be used?
– Who will perform the inspection?
– Who will use in-process inspection?
– What is the sample size?
– What are the critical attributes to be inspected?
– Where should the inspection be performed?
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Control Plans
• Provide a documented, proactive approach to defining
how to respond when process control charts show that a
process is out of control
• Required part of an ISO 9000 quality management
system (QMS)
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Control Plan Sample
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Process Control Charts
• Statistical process control charts:
– Tools for monitoring process variation
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Variables and Attributes Control Charts
• Variable
– Continuous measurement such as height, weight, or
volume
• Attribute
– An either-or situation, such as a motor starting or not,
or a lens being scratched or not
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Variables and Attributes Control Charts
Steps in developing process control charts:
1. Identify critical operations in the process where
inspection might be needed. These are operations in which
the product will be negatively affected if the operation is
performed improperly.
2. Identify critical product characteristics. These are the
aspects of the product that will result in either good or poor
functioning of the product.
3. Determine whether the critical product characteristic is a
variable or an attribute.
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Variables and Attributes Control Charts
Steps in developing process control charts:
4. Select the appropriate process control chart from among
the many types of control charts. (This decision process
and the types of charts available are discussed later.)
5. Establish the control limits and use the chart to
continually monitor and improve.
6. Update the limits when changes have been made to the
process.
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Interpreting Control Charts (1 of 2)
Signals for concern sent by a control chart
Hansen, Bertrand L. Quality Control: Theory and Applications. Upper Saddle River, NJ: Pearson Education
(1964). ISBN: 013745208X. ©1964, p.65. Reprinted and Electronically reproduced by permission of Pearson
Education, Inc., New York, NY.
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Interpreting Control Charts (2 of 2)
Signals for concern sent by a control chart
Hansen, Bertrand L. Quality Control: Theory and Applications. Upper Saddle River, NJ: Pearson Education
(1964). ISBN: 013745208X. ©1964, p.65. Reprinted and Electronically reproduced by permission of Pearson
Education, Inc., New York, NY.
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Interpreting Control Charts
• Out-of-control situations:
– Two points in succession farther than two standard
deviations from the mean
– Process run – Five points in succession either above
or below the center line
– Process drift – Seven points, all increasing or
decreasing
– Erratic behavior – Large jumps of more than three or
four standard deviations
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Choosing the Correct Variables Control
Chart
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Corrective Action
• Corrective action steps when a process is out of control:
– Carefully identify the quality problem.
– Form the appropriate team to evaluate and solve the problem.
– Use structured brainstorming along with fishbone diagrams or
affinity diagrams to identify causes of problems.
– Brainstorm to identify potential solutions to problems.
– Eliminate the cause.
– Restart the process.
– Document the problem, root causes, and solutions.
– Communicate the results of the process to all personnel so this
process becomes reinforced and ingrained in the organization.
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Using Control Charts to Continuously
Improve
• Two key concepts:
– The focus of control charts should be on continuous
improvement.
– Control chart limits should be updated only when
there is a change to the process. Otherwise, any
changes are unexpected.
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Effects of Tampering with the Process
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Process Capability for Variables
• The capability of a process to produce a product that
meets specification
• World-class levels of process capability are measured by
parts per million (ppm) defect levels.
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Process Capability for Variables
• Six Sigma programs result in highly capable processes
and an average of only 3.4 defects per million units
produced.
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Population versus Sampling Distributions (1
of 2)
• Population distributions
– Distributions with all individual responses from an entire
population
• Population
– A collection of all the items or observations of interest to a
decision maker
• Sample
– A subset of the population
• Sampling distributions
– Distributions that reflect the distribution of sample means
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Population versus Sampling Distributions
Population and Sampling Distributions for Class Heights
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Population versus Sampling Distributions (2
of 2)
• In the context of quality, specifications and capability are
associated with population distributions.
• Sample-based process charts and stability are computed
statistically and reflect sampling distributions.
• Quality practitioners should not compare process chart
limits with product specifications.
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Capability Studies (1 of 4)
• Reasons to perform a process capability study:
– To determine whether a process consistently results
in products that meet specifications
– To determine whether a process is in need of
monitoring through the use of permanent process
charts
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Capability Studies (2 of 4)
• Five steps to perform process capability studies:
– Select a critical operation. These may be bottlenecks,
costly steps of the process, or places in the process in
which problems have occurred in the past.
– Take k samples of size n, where x is an individual
observation.
▪ Where 19 < k < 26 ▪ If x is an attribute, n > 50 (as in the case of a binomial)
▪ Or if x is a measurement, 1 < n < 11 – Use a trial control chart to see whether the process is stable. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Capability Studies (3 of 4) • Five steps to perform process capability studies: – Compare process natural tolerance limits with specification limits. Note that natural tolerance limits are three standard deviation limits for the population distribution. This can be compared with the specification limits. – Compute capability indexes. To compute capability indexes, compute an upper capability index (Cpu), a lower capability index (Cpl), and a capability index (Cpk). The formulas are: Cpu = (USL − μ) / 3σˆ Cpl = (μ − LSL) / 3σˆ Cpk = min {Cpu, Cpl} Where: USL = upper specification limit LSL = lower specification limit μ = computed population process mean σˆ = Estimated process standard deviation = σˆ = R / d 2 Copyright © 2017 Pearson Education, Inc. All Rights Reserved Capability Studies (4 of 4) • Although different firms use different benchmarks, the generally accepted benchmarks for process capability are 1.25, 1.33, and 2.0. • We will say that processes that achieve capability indexes (Cpk) of 1.25 are capable, 1.33 are highly capable, and 2.0 are world-class capable (Six Sigma). Copyright © 2017 Pearson Education, Inc. All Rights Reserved Capability versus Stability • A process is capable if individual products consistently meet specification. • A process is stable if only common variation is present in the process. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Copyright Copyright © 2017 Pearson Education, Inc. All Rights Reserved Managing Quality: Integrating The Supply Chain Sixth Edition Chapter 12 Statistically Based Quality Improvement for Attributes Copyright © 2017 Pearson Education, Inc. All Rights Reserved Types of Attributes • Structural attributes • Sensory attributes • Performance attributes • Temporal attributes • Ethical attributes • Customer-based attributes • Production-related attributes Copyright © 2017 Pearson Education, Inc. All Rights Reserved Generic Process for Developing Attributes Charts • Identify critical operations in the process where inspection might be needed. • Identify critical product characteristics. • Determine whether the critical product characteristic is a variable or an attribute. • Select the appropriate process chart from the many types of charts. • Establish the control limits and use the chart to continually monitor and improve. • Update the limits when changes have been made to the process. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Understanding Attributes Charts (1 of 2) • Attribute charts use binomial and Poisson processes that are not measurements. • Think in terms of defects and defective units. • Defect – an irregularity or problem with a larger unit – Countable; can be several within one unit – Monitored using c and u charts • Defective – a unit that, as a whole, is not acceptable or does not meet performance requirements – Monitored using p and np charts Copyright © 2017 Pearson Education, Inc. All Rights Reserved Process Charts • A process chart (or p chart) is used to graph the proportion of items in a sample that are defective or nonconforming to specification. • They are also used to determine when there has been a shift in the proportion defective for a particular product or service. Copyright © 2017 Pearson Education, Inc. All Rights Reserved p Chart Applications • Late deliveries • Incomplete orders • Calls not getting dial tones • Accounting transaction errors • Clerical errors on written forms • Parts that do not mate properly Copyright © 2017 Pearson Education, Inc. All Rights Reserved p Charts Calculations • Subgroup sizes – Typically between 50-100 units, and can be of different sizes • Formulas for control limits: Control lim its for p = p  3 [(p)(1 − p) / n] Where p = the proportion defective p = the average proportion defective n = the sample size Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example (1 of 4) • Problem: A city police department was concerned that the number of convictions was decreasing relative to the number of arrests. The suggestion was raised that the district attorney’s office was becoming less effective in prosecuting criminals. You are asked to perform an analysis of the situation. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example (2 of 4) Sample Number of Cases Reviewed Number of Convictions Proportion 1 100 60 .60 2 95 65 .68 3 110 68 .62 4 142 62 .44 5 100 56 .56 6 98 58 .59 7 76 30 .39 8 125 68 .54 9 100 54 .54 10 125 62 .50 11 111 70 .63 12 116 58 .50 13 92 30 .33 14 98 68 .69 The data for the previous 27 weeks are provided in the following table. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example (3 of 4) Sample Number of Cases Reviewed Number of Convictions Proportion 15 162 54 .33 16 87 62 .71 17 105 70 .67 18 110 58 .53 19 98 30 .31 20 96 68 .71 21 100 54 .54 22 100 62 .62 23 97 70 .72 24 122 58 .48 25 125 30 .24 26 110 68 .62 27 100 54 .54 The data for the previous 27 weeks are provided in the following table. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example (4 of 4) • Solution: Notice that in this problem, the sample size is not constant. When this happens, you have at least two options: – Compute the control limits using an average sample size. (This is easier to understand.) – Compute the control limits using the different sample sizes. (This is statistically more correct.) Copyright © 2017 Pearson Education, Inc. All Rights Reserved Figure 1 Example Copyright © 2017 Pearson Education, Inc. All Rights Reserved Figure 2 Example Results using Excel Microsoft Excel, Microsoft Corporation. Used by permission. Copyright © 2017 Pearson Education, Inc. All Rights Reserved np Charts • A graph of the number of defectives (or nonconforming units) in a subgroup • Requires that the sample size of each subgroup be the same each time a sample is drawn • If sample sizes are equal, either the p or np chart can be used. Copyright © 2017 Pearson Education, Inc. All Rights Reserved np Chart Calculations • Subgroup sizes – Typically between 50-100 units • Formulas for control limits: CLnp = n(p)  3Snp where: n = the sample size p = the average proportion defective Snp = standard error of np(1 − p) Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example 2 (1 of 3) • Problem: Within the J. Kim Insurance Company of Boston, Massachusetts, management found that too many of its policies were rated incorrectly. Management directed that policy applications be reviewed for the past 24 months on a sampling basis. As an analyst, you are asked to review the policies for correct rating. If any problem is found with the rating of a policy, it is said to be defective. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example 2 (2 of 3) Month Number of Policies Reviewed Number of Policies with Rating Errors p 1 100 11 0.11 2 100 10 0.10 3 100 12 0.12 4 100 6 0.06 5 100 14 0.14 6 100 8 0.08 7 100 10 0.10 8 100 9 0.09 9 100 12 0.12 10 100 2 0.02 11 100 14 0.14 12 100 18 0.18 13 100 17 0.07 14 100 13 0.13 15 100 14 0.14 One hundred policies from each month were selected for review. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example 2 (3 of 3) Month Number of Policies Reviewed Number of Policies with Rating Errors p 16 100 12 0.12 17 100 11 0.11 18 100 8 0.08 19 100 9 0.09 20 100 17 0.17 21 100 18 0.18 22 100 20 0.20 23 100 25 0.25 24 100 28 0.28 Blank Blank Blank Mean = 0.13 One hundred policies from each month were selected for review. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Figure Example 2 Solution: The results of the control chart are shown in Figure 12-3. The chart shows that rating errors are increasing. Assignable causes should be identified through investigation. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Figure 2 Example 2 Results using Excel Microsoft Excel, Microsoft Corporation. Used by permission. Copyright © 2017 Pearson Education, Inc. All Rights Reserved c Charts • A graph of the number of defects (nonconformities) per unit • Units must be of the same metric such as height, length, volume, and so on. • Used to detect nonrandom events in the life of the production process and when you are inspecting the same size sample space Copyright © 2017 Pearson Education, Inc. All Rights Reserved c Chart Applications • Number of flaws in an auto finish • Number of flaws in a standard typed letter • Number of data errors in a standard form • Number of incorrect responses on a standardized test Copyright © 2017 Pearson Education, Inc. All Rights Reserved u Charts • A graph of the average number of defects per unit • Allows for the units sampled to be different sizes, areas, heights, etc. • The uses for the u chart are the same as the c chart. Copyright © 2017 Pearson Education, Inc. All Rights Reserved c and u Chart Calculations • Formulas for control limits: CLc = c  3 c u CLu = u  3 n Where: n = average sample size c = process average number of nonconformities u = process average number of nonconformities per unit Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example 3 (1 of 4) • Problem: The J. Grout Window Company makes coloredglass objects for home decoration. J. Grout, the owner, has been concerned about scratches in the finish of recently made product. • The company makes two products: Demi-Glass, which comes in one standard configuration; and StreaklessGlass, which comes in three similar models. • As an analyst, you are asked to evaluate the process by determining whether the processes are stable. Assume that, on average, the Streakless are 1.5 times the size of the Demis. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example 3 (2 of 4) Item Number Demi Defects Streakless Defects 1 5 6 2 4 4 3 6 7 4 3 9 5 9 5 6 4 8 7 5 7 8 4 4 9 3 5 10 7 4 11 9 5 12 12 4 13 3 5 14 6 6 Using high-power magnifying glasses, the company examined 25 each of the Demi (one style only) and the Streakless (randomly selected in all three styles). Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example 3 (3 of 4) Item Number Demi Defects Streakless Defects 15 2 4 16 8 8 17 5 5 18 7 7 19 12 10 20 4 5 21 6 4 22 8 7 23 5 5 24 7 6 Blank Sum c = 144 C =6 Sum u = 140 U = 5.83 Using high-power magnifying glasses, the company examined 25 each of the Demi (one style only) and the Streakless (randomly selected in all three styles). Copyright © 2017 Pearson Education, Inc. All Rights Reserved Figure 1 Example 3 c Chart for Demis Solution: As shown in Figure 12-5 and Figure 12-6, the process for Demis appears to be in control. However, the process for Streakless shows a run of five points below the mean. An assignable cause should be sought. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Figure 2 Example 3 u Chart for Streakless Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example 3 (4 of 4) Results using Excel Microsoft Excel, Microsoft Corporation. Used by permission. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Table 12-2 Attributes Charts Summary Chart LCL CL UCL p p − 3 p(1 − p) / n p p + 3 p(1 − p) / n np np − 3 np(1 − p) np np + 3 np(1 − p) c c −3 c c c+3 c u u − 3 u/n u u + 3 u/n Copyright © 2017 Pearson Education, Inc. All Rights Reserved Figure Reliability Models • Bathtub-shaped hazard functions: – The vertical axis is the failure rate. – The horizontal axis is time. – Shows that products are more likely to fail either very early or late in their useful lives. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Reliability Models • Series reliability: – Components in a system are in a series if the performance of the entire system depends on all the components functioning properly. – The components need not be physically wired sequentially for the system to be in series. – All parts must function for the system to function. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Reliability Models (1 of 3) • System unreliability can be modeled as: Rs = P( x1x 2  xn ) = P( x1)P( x 2 | x1)P( x3 | x1x 2 )...P( xn | x1x 2....xn −1) Where: Rs = system reliability P( x ) = 1 − probability of failure for component xi • System reliability for the series is expressed as: Where: Qs = 1 − R s Qs = system unreliability Copyright © 2017 Pearson Education, Inc. All Rights Reserved Reliability Models (2 of 3) • Parallel reliability: – High reliability systems often require extremely high component reliability. – When such high reliability is an impossibility, an alternative is to use a backup system. – Another word for backup is redundant or parallel. Copyright © 2017 Pearson Education, Inc. All Rights Reserved Reliability Models (3 of 3) • System reliability for the series is expressed as: Rp = P( x1 + x 2 + ... + xn ) = 1 − P( x1x 2...xn • Redundant reliability can be modeled as: Rp = 1 − P( x1)P( x 2 | x1)P( x3 | x1x 2 )...P( xn | x1x 2...xn −1 • System unreliability can be modeled as: n Qp = π Qi i=1 Copyright © 2017 Pearson Education, Inc. All Rights Reserved Figure Example 4 • At times, systems have some components in series and some components in parallel (or redundancy). • Figure 12-10 has one such system. • Overall reliability for this system is R = .98 * .99 * 11 - 1.1 * .122 * .97 = .932 Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example 4 • To continue the example, it is interesting to compare the overall reliability of this system without component C2 . • It equals R = .98 * .99 * .90 * .97 = .847 • Thus the overall improvement in system reliability by adding the additional component is D = .932 - .847 = .085 Copyright © 2017 Pearson Education, Inc. All Rights Reserved Measuring Reliability • Failure rate: Failure rate =  = number of failures /(units tested  number of hours tested) Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example 5 • Problem: Suppose that we tested 25 ski exercise machines under strenuous conditions for 100 hours per machine. Of the machines tested, three experienced malfunctions during the test. What is the failure rate for the exercise machines? • Solution: Failure rate = 3/(25  100) = .0012 failures per operating hour Copyright © 2017 Pearson Education, Inc. All Rights Reserved Mean Time to Failure (MTTF) (1 of 2) • Reliability: R( T ) = 1 − F( T ) = e −T where: • Mean time to failure: – Average time before the product will fail Copyright © 2017 Pearson Education, Inc. All Rights Reserved Mean Time to Failure (MTTF) (2 of 2) • Reliability: R(T ) = 1 − F(T ) = e −T Where: R(T) = reliability of the product F(T) = unreliability of the product  = failure rate T = product’s useful life expressed as a function • Mean time to failure: – Average time before the product will fail 1/  Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example 6 • Problem: Suppose that a product is designed to operate for 100 hours continuously with a 1% chance of failure. Find the number of failures per hour incurred by this product and the MTTF. • Solution: 0.99 = e − (100 ) ln 0.99 = −100  = −(ln 0.99) / 100  = .01005 / 100  = 0...1005 MTTF = 1/ .0001005 = 9950.25 = 1/  Copyright © 2017 Pearson Education, Inc. All Rights Reserved Mean Time between Failures (MTBF) • The average time from one failure to the next when a product can be repaired • MTBF = total operating hours/number of failures Copyright © 2017 Pearson Education, Inc. All Rights Reserved Example 7 • Problem: A product has been operated for 10,000 hours and has experienced four failures. What is the MTBF? • Solution: – MTBF = 10,000>4 = 2,500 hours between failures
– The failure rate is then calculated as l = 1>2,500 =
.0004 failures per hour.
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
System Availability
• A useful measure for maintainability of a product that
considers both MTBF and a new statistic: mean time to
repair (MTTR)
• Gives the “uptime” of a product or system
SA = M T B F  (M T B F + M T T R)
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Example 8 (1 of 2)
• Problem: Jami Kovach has to decide between three
suppliers for a network server. Other factors being equal,
she will base her decision on system availability. Given
the following data, which supplier should she choose?
Supplier
MTBF (h)
MTTR (h)
A
67
4
B
45
2
C
36
1
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Example 8 (2 of 2)
• Solution:
– SAA = 67> 167 + 42 = .944
– SAB = 45> 145 + 22 = .957
– SAC = 36> 136 + 12 = .973
• Choose supplier C. As you can see, service does matter.
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Copyright
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Managing Quality: Integrating The Supply
Chain
Sixth Edition
Chapter 10
The Tools of Quality
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Improving the System (1 of 2)
• To be successful, a business or organization must
balance the needs of these different functional areas:
– Supply chain management
– Marketing
– Accounting
– Human resources
– Operations
– Engineering
– Strategy
Copyright © 2017, 2013, 2010 Pearson Education, Inc. All Rights Reserved
Improving the System (2 of 2)
• A quality system uses the business model with a focus on
the customer, and includes the dynamics of:
– Continual improvement
– Change
– Planning
– Renewal
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A Quality System
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Ishikawa’s Basic Seven Tools of Quality (B 7)
• Process maps
• Check sheets
• Histograms
• Scatter plots
• Control charts
• Cause and effect diagrams
• Pareto analysis
Copyright © 2017, 2013, 2010 Pearson Education, Inc. All Rights Reserved
Ishikawa’s Basic Seven Tools of Quality (B7)
Logical Map of the Order of the Basic Seven (B7) Tools
Based on M. Brassard, The Memory Jogger II, published by GOAL/QPC, 2 Manor Parkway, Salem, New
Hampshire, 2004. Reprinted with permission of GOAL/QPC.
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Process Maps
• A picture of a process, or map of the process, as it exists
• The following set of symbols is used:
Copyright © 2017, 2013, 2010 Pearson Education, Inc. All Rights Reserved
Simple Rules for Process Maps (1 of 2)
• Use the simple symbols to chart the process from the
beginning, with all arcs in the process map leaving and
entering a symbol.
– The arcs represent the progression from one step to
the next.
• Develop a general process map and then fill it out by
adding more detail, or a sub flowchart, to each of the
elements.
• Step through the process by interviewing those who
perform it – as they do the work.
Copyright © 2017, 2013, 2010 Pearson Education, Inc. All Rights Reserved
Simple Rules for Process Maps (2 of 2)
• Determine which steps add value and which don’t in an
effort to simplify the work.
• Before simplifying the work, determine whether the work
really needs to be done in the first place.
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Home Occupation Process: Current
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Home Occupation Process: Proposed
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Steps in Process Mapping
1. Settle on a standard set of process mapping symbols to be
used.
2. Clearly communicate the purpose of the process map to all
the individuals involved in the exercise.
3. Observe the work being performed by shadowing the
workers performing the work.
4. Develop a map of the process.
5. Review the process map with the employees to make
needed changes and adjustments to the process map.
6. Develop a map of the improved process.
Copyright © 2017, 2013, 2010 Pearson Education, Inc. All Rights Reserved
SIPOC Diagram
• A diagram that is useful when it is not clear who your
customers are, where specifications for inputs exist, and
when clarifying customer requirements.
– Supplier
– Inputs
– Process
– Outputs
– Customers
Copyright © 2017, 2013, 2010 Pearson Education, Inc. All Rights Reserved
Cause-and-Effect (Ishikawa Diagrams) (1 of 2)
• A tool to help move to lower levels of abstraction in
solving problems
• Looks like the skeleton of a fish
– Problem = Head
– Ribs = Major causes
– Bones = Subcases
Copyright © 2017, 2013, 2010 Pearson Education, Inc. All Rights Reserved
Cause-and-Effect (Ishikawa Diagrams) (2 of 2)
• Steps to create a cause-and-effect diagram:
– State the problem clearly in the head of the fish.
– Draw the backbone and the ribs by asking
participants to identify major causes of the problems
labeled in the head of the diagram.
– Continue to fill out the diagram asking “Why?” about
each problem or cause of a problem until the fish is
filled out.
– View the diagram and identify core causes.
– Set goals to address the core causes.
Copyright © 2017, 2013, 2010 Pearson Education, Inc. All Rights Reserved
Cause-and-Effect Diagram: Wobbling Saw
Blade
Reprinted by permission from Patrick Shannon.
Copyright © 2017 Pearson Education, Inc. All Rights Reserved
Pareto Charts (1 of 2)
• Charts used to identify and prioritize problems to be
solved
• Aided by the 80/20 rule, which states that roughly 80% of
the problems are created by 20% of the causes – or there
are a “vital few” causes that create most of the problems
Copyright © 2017, 2013, 2010 Pearson Education, Inc. All Rights Reserved
Pareto Charts (2 of 2)
• Rules for constructing Pareto charts:
– Information must be selected based on types or
classifications of defects that occur as a result of a
process.
– Data must be collected and classified into categories.
– A frequency chart must be constructed, showing the
number of occurrences in descending order.
• Steps in Pareto analysis:
– Gather categorical data relating to quality problems.
– Draw a frequency chart of the data.
– Focus on the tallest bars in the frequency chart first when
solving the problem.
Copyright © 2017, 2013, 2010 Pearson Education, Inc. All Rights Reserved
Dashboards
• Tools that quickly communicate performance levels, with
a focus on easy, clear communication
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Copyright
Copyright © 2017, 2013, 2010 Pearson Education, Inc. All Rights Reserved

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