Managing Operations – Global Homework Experts

MGMT3011 Managing Operations
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Week 4 – Quality Management Tutorial
Text: Chapter 5, Operations Management (2020) 7th R. Dan Reid & Nada R. Sanders
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Learning Objectives
LO1 – Explain the meaning of total quality management (TQM).
LO2 – Identify costs of quality.
LO3 – Describe the evolution of TQM.
LO4 – Identify features of the TQM philosophy.
LO5 – Describe quality awards and quality certifications.
LO6 – Understand why and how TQM efforts fail.
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TQM Philosophy Concepts – ways of improving quality
Focus on customer:
o Identify and meet customer needs.
o Quality is customer driven.
o Stay tuned to changing needs, e.g., fashion styles, psychometrics & demographics
Continuous improvement: a philosophy of never-ending improvement, e.g., Kaizen.
o Plan-Do-Study-Act (PDSA) cycle
o Benchmarking – study how others do things.
o Reliability
o Cost of quality
Employee empowerment:
o Empower all employees
o External versus internal customers
o Team approach (teams formed around processes, Quality circles; 8–10 people;
meet weekly to analyze and solve problems)
Use of quality tools:
LO 4
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LO2 – Identify costs of quality.
Cost of Quality
Quality affects all aspects of the organization;
Quality has dramatic cost implications:
o Quality control costs (to achieve high quality)
Prevention costs – Costs of preparing and implementing a quality plan
Appraisal costs – Costs of testing, evaluating, and inspecting quality
o Quality failure costs (high costs associated with poor quality)
Internal failure costs – Costs of scrap, rework, and material losses
External failure costs – Costs of failure at customer site, including
returns, repairs, and recalls
LO 2
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External Failure Costs and Defects
FIGURE 5.2 Cost of defects
LO 2
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TQM Philosophy Concepts: Use of Quality Tools
Use of Quality Tools
o Ongoing training on analysis, assessment, and correction, as well
as the use of implementation tools
o Seven Tools of Quality Control
Cause-and-effect diagrams
Flowcharts
Checklists
Control charts
Scatter diagrams
Pareto analyses
Histograms
LO 4
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The Seven Tools of Quality Control
FIGURE 5.7 The seven tools
of quality control
LO 4
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1. Cause-and-Effect Diagrams
Often called Fishbone
Diagrams or Ishikawa
Diagram
A tool that identifies
process elements (causes)
that may effect an
outcome
Focused on identifying
potential causes of quality
problems.
Used by quality control
teams; brainstorming.
FIGURE 5.8 A general cause-andeffect (fishbone) diagram
LO 4
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2. Flowcharts
Schematic diagrams used to document the detailed steps in a process.
LO 4
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3. Checklists
Simple data check-off sheets.
Designed to identify type of quality problems at each work station;
per shift, per machine, per operator.
LO 4
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4. Control Charts
The UCL and LCL are calculated limits used to show when a process
is in or out of control
; e.g., weight, width, or volume.
Key tool used in statistical process control (Chapter 6).
LO 4
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Figure 9.5 An example of a statistical control chart (Samson & Singh, 2010, p.273)
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Example using a control chart to monitor defects per unit
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The re-Cycle Cardboard Company produces cardboard used for making lazer-cut jigsaw puzzles. As a
final step in the process, the cardboard passes through a machine that measures various product
quality characteristics. When the cardboard production process is in control, it averages 10 defects
per roll.
1. Set up a control chart for the number of defects per roll. For this example, use two-sigma control limits.
2. Five cardboard rolls had the following number of defects: 8, 11, 9, 12 and 14 respectively. The sixth roll, using
recycled materials from a new supplier, had 3 defects. Is the paper production process in control?
3. How can the OM manager use this analysis to assist in decisions regarding his suppliers?
Example of control charts
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Upper control limit
UCL
c = 𝑐𝑐̅ + z 𝑐𝑐̅
Lower control limit
LCL
c = 𝑐𝑐̅ – z 𝑐𝑐̅
where
𝑐𝑐̅ = average number of defects
z = sigma control limits

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8 6 4 2 0
10
12
14
16
Roll 1 Roll 2 Roll 3 Roll 4 Roll 5 Roll 6
Control chart – cardboard defects per roll
Sample number
𝑐𝑐̅ = 10
UCL
c = 16.32
LCL
c = 3.68
Number of defects
Statistical Quality Control
Identifying quality control centres on whether an activity needs adjustment
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(b) Special causes of variation present
(process is not in control) (Foster, 2007)
(a) Common causes of variation
present (process is in control)

±6 Sigma versus ± 3 Sigma
Motorola coined “six-sigma” to describe their
higher quality efforts back in 1980’s; process
capability
Six-sigma quality standard is now a
benchmark in many industries
Before design, marketing ensures customer
product characteristics
Operations ensures that product design
characteristics can be met by controlling
materials and processes to 6σ levels
Other functions like finance and accounting
use 6σ concepts to control all of their
processes
PPM Defective for ±3σ versus
±6σ quality
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5. Scatter Diagrams
Graphs that show how two variables are related to one another.
The greater the degree of correlation, the more linear are the observations
LO 4
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6. Pareto Analysis
Used to identify quality problems based on their degree of importance.
Named after a 19th -century Italian economist; often called the 80-20 rule.
o Principle is that quality problems are the result of only a few causes; e.g., 80% of
problems are caused by 20% of causes.
LO 4
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Example Pareto chart
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Formerly a high street retailer, Oats and Stem have expanded to include an e-commerce retail
department. Given the e-commerce retail area has now been operating for 6 months, they wish to gain
an accurate picture of the current state of quality across the departments using a range of quality tools.
Due to the fact that the new e-commerce department has been documenting customer complaints, it
will be the first department to be reviewed. Table 1 shows the customer complaints data the
department has collected over 6 months:
Table 1: Customer complaints over a 6 month period, e-commerce department

order now
Customer complaint Number of complaints %
1. Billing errors 1421 18
2. Shipping errors 845 11
3. Electronic charge errors 650 8
4. Shipping delays 3016 38
5. Packing errors 1879 23
6. Too many calls to resolve an issue 189 2
TOTAL 8000 100

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Customer complaint Number of
complaints
% Cumulative
%
4. Shipping delays 3016 38 38
5. Packing errors 1879 23 61
1. Billing errors 1421 18 79
2. Shipping errors 845 11 90
3. Electronic charge errors 650 8 98
6. Too many calls to resolve
an issue
189 2 100
TOTAL 8000 100

Re-order highest to lowest number of complaints
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7. Histograms
A chart that shows the frequency distribution of observed values of a
variable (e.g., service time at a bank drive-up window).
Displays whether the distribution is symmetrical (normal) or skewed.
LO 4
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Product Design: Quality Function Deployment (QFD)
Critical to ensure that product design meets customer expectations (and
to enhance communications internally).
QFD is a useful tool for translating customer specifications into technical
requirements.
QFD encompasses:
o customer requirements
o competitive evaluation
o product characteristics
o relationship matrix
o trade-off matrix
o setting targets
LO 4
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Quality Function Deployment (QFD) Details
FIGURE 5.9
Relationship matrix
LO 4

 

Customer

Process used to ensure that the product meets customer specifications
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QFD: House of Quality
FIGURE 5.10 House of
quality
LO 4

 

 

Adding trade-offs, targets & developing product specifications
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Benchmarking

Used for a variety of purposes:
o Comparing an organisation’s processes
Selecting best practices to use as a standard
for performance

with an organisation who has best
practice at performing a particular
activity regardless of the industry itself
(
functional benchmarking)
o Comparing an organisation’s products
and services with those of direct
industry competitor (
competitive
benchmarking
)
o Identifying the best practices to be
implemented
o Projecting trends in order to be able to
respond proactively to future
challenges and opportunities
Prepare the benchmark study
Determine what to benchmark
Form a benchmark team
Identify benchmarking partners
Collect and analyze benchmarking
information
Published data
Original research
Take action to match or exceed the
benchmark
Apply research – set challenging
goals
Involves comparing an organisation’s processes with the processes of the best to be
found in today’s process-based organisations
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Product Design: Reliability
Reliability is the probability that a product, service, or part will perform as intended for
a specified period of time under normal conditions.
No product is 100% certain to function properly.
Reliability is a probability function dependent on sub-parts or components.
Reliability of a system is the product of component reliabilities
R
S = (R1) (R2) (R3) . . . (Rn)
where
R
S = reliability of the product or system
R
1 . . . n = reliability of components 1 through n
LO 4
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Product Design: Process Management and
Managing Supplier Quality
Quality products come from quality
sources.
Quality must be built into the
process.
Quality at the source is the belief
that it is better to uncover the source
of quality problems and correct it.
TQM extends to quality of product
from company’s suppliers.
LO 4
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Some terms for quality measuring data
Inspection of variables – Variable data
o measurable usually relates to weight, length, temperature, diameter, or some
other variable that can be scaled
o use a continuous scale, e.g. length, weight
o quality tool – histogram
Inspection of attributes Attribute data
o determining the existence of a characteristic;
o usually uses a dichotomous variable such as right/wrong; acceptable/defective;
timely/late; red/not red
o quality tool – bar chart
Subjective data
o are based on opinions and perceptions,
o data are usually collected on a five, six or seven point scale; called the Likert scale,
these can sometimes be turned into variable data
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Ayman Bahjat Abdallah; Alfar, Nour A; Alhyari, Salah. 2021. The effect of supply chain quality management on supply
chain performance: the indirect roles of supply chain agility and innovation
. International Journal of Physical
Distribution & Logistics Management.
51(7): 785-812.
Carter, P.J., R.M. Monczka, and J. Mossconi. 2005. Looking at the Future of Supply Management, Supply Chain
Management Review,
27–29.
Dischinger, J., D.J. Closs, E. McCulloch, C. Speier, W. Grenoble, and D. Marshall. 2006. The Emerging Supply Chain
Management Profession,
Supply Chain Management Review,, 62–68.
Körber, Maximilian; Cotta, Diogo. 2021. Supply chain in the C-suite: the effect of chief supply chain officers on
incidence of product recalls. Supply Chain Management. 26(4): 495-513.
Meredith, J. and J. Shafer. 2019. Operations and Supply Chain Management for MBAs. 7th ed. New York: John Wiley
& Sons.
Reid, D. and N. Sanders. 2020. Operations Management: An Integrated Approach. 7th ed. New Jersey: John Wiley &
Sons.
Samson, D. and P. Singh. eds. 2008. Operations Management: An integrated Approach. Melbourne: Cambridge
University Press.
Schemmner, R.W. and M. Swinck. 1998. On Theory in Operations Management. Journal of Operations Management
17; 97-113.
Slack, N., S. A. Brandon-Jones, R. Johnson and A. Betts. 2015. Operations and Process Management, 4th ed.
England: Prentice Hall..
References
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