VIETNAM NATIONAL UNIVERSITY HCMC
INTERNATIONAL UNIVERSITY
FINAL REPORT
APPLYING SIX SIGMA IN PROCESS
IMPROVEMENT – A CASE STUDY IN A PAPER
MANUFACTURING COMPANY
Members of the project:
No.
Student’s ID
Full name
1
Pham Nhat Tan
IEIEIU16002
2
Pham Hoang Viet
IELSIU16115
3
Pham Le Bach Hop
IELSIU16028
4
Dinh The Long
IEIEIU16047
5
Nguyen Hoai Nghia
IELSIU16003
6
Le Thi Kim Ngan
IELSIU16017
Academic advisor: M.Sc Duong Vo Nhi Anh
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TABLE OF CONTENT
I. Introduction
a. Motivation for the Research Report
b. Research Report Objective
c. Scope and Limitations of the study
d. Organization of the Research Report
II. Comparing six sigma with quality management practices
III. Facets of six sigma implementation in SMEs
IV. Six sigma implementation – case study six sigma
a. Definition
b. The Effects on society of six sigma
c. Quality of Six sigma
V. The dmaic six sigma methodology
a. Define Phase
b. Measure Phase
c. Analyze Phase
d. Improve Phase
e. Control Phase
VI. METHODS IN QUALITY IMPROVEMENT
a. Six sigma in quality management
b. Six sigma in Warehouse management
c. Six sigma in Transportation management
d. Optimal algorithms to improve quality
VII. Conclusion
VIII. References
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I.
INTRODUCTION
1.
Motivation of the research:
Six Sigma/Lean Six Sigma is a new and efficient approach for process
improvement. It was introduced by engineers Bill Smith & Mikel J Harry while
working at Motorola in 1986. Jack Welch made it central to his business strategy
at General Electric in 1995.It seeks to improve the quality of the output of a
process by identifying and removing the causes of defects and minimizing
variability in manufacturing and business processes. It uses a set of quality
management methods, mainly empirical, statistical methods, and creates a special
infrastructure of people within the organization who are experts in these methods.
Each Six Sigma project carried out within an organization follows a defined
sequence of steps and has specific value targets, for example: reduce process
cycle time, reduce pollution, reduce costs, increase customer satisfaction, and
increase profits. This report apply Six Sigma techniques to solve a specific case
study in order to improve the current process of the company.
2. Research report objective:
- To implement the use of Six Sigma tools ( control charts, statistical analysis,...
ect) to analyze and provide suggestions to improve the current status of the
system in the case study.
3. Scope and limitation:
- In this report, we apply all phases of the famous DMAIC process,
including Define, Measurement, Analyze, Improvement, and Control to solve the
problems. However, since the data sample is relatively small, our measurement and
analyze phase could yield bias results.
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II.
Comparing six sigma with quality management practices (total quality
management)
Six Sigma represents a new wave of the quality management evolution (preceded by TQM
evolution) towards operational excellence (Basu, 2004). The definition of TQM is different from
that of Six Sigma but the aims are similar (Anderson et al., 2006). Six Sigma has additional data
analysis tools and more financial focus than what is found in TQM (Kwak and Anbari, 2004).
TQM has a comprehensive approach that involves and commits everyone in a company while
Six Sigma has a project management approach that is associated with a team (Anderson et al.,
2006). Arnheiter and Maleyeff (2005) have indicated that a number of components of Six Sigma
can be traced back to TQM. This explains that Six Sigma is an extension of TQM and that they
both share similar principles. Snee (2007) suggested there are benefits for integrating Lean and
Six Sigma with the Baldrige assessment (a TQM model) and ISO9000. Antony (2004) stresses
that it is important to remember that Six Sigma has a better record than TQM since its inception
in the mid 1980s. Table 2 represents a summary of a literature review on Six Sigma, TQM and
their comparison:
Six Sigma is similar to TQM in terms of theory and handling methods (Hwang, 2006). Both
draw from behavioural and quantitative sciences (Friday-Strout and Sutterfield, 2007). Basis It
includes two dimensions of philosophy (or management) and methodology (or analysis)
(Hwang, 2006). TQM can be described as a philosophy and is considered as a management
process that applies management principles (Jitpaiboon and Rao, 2007). Aim It is an
improvement methodology (Hoerl, 2004). Six Sigma and TQM focus on continuous
improvement (CI) (Antony, 2006) and share similar principles and aims. TQM aims at
improving all processes within an organisation and it treats the organisation as a total system
(Shah and Ward, 2007). It is a holistic quality management system (Jitpaiboon and Rao, 2007) or
management process with the goal of generating a quality-based culture (Aly et al., 1990). Link
to Deming Six Sigma DMAIC is closely linked to Deming’s PDCA cycle (Haikonen et al., 2004;
Linderman et al., 2005) and it improves upon the PDCA cycle (Tannock et al., 2007). TQM is
based on teachings of Deming (Snee, 2004) in which the main tenets of Six Sigma are
embedded (Mayeleff and Kaminsky, 2002; Black and Revere, 2006). Mutual relationship Six
Sigma is an expansion of TQM (Terziovski, 2006; Proudlove et al., 2008) with components
rooting and traced back to TQM (Aly, 1990; Arnheiter and Mayeleff, 2005) and can be viewed
as a methodology within TQM and not as an alternative (Klefsjo et al., 2001). Six Sigma is an
extension of TQM (Klefsjo et al., 2001; Proudlove et al., 2008). Existing TQM activities can
help in the implementation of a Six Sigma system (Cheng, 2008). TQM has become an umbrella
for Six Sigma and other tools (Harnesk and Abrahamsson, 2007). Financial savings It tracks
cost savings on a project by project level (Schroeder et al., 2008). It has more financial focus
(Kwak and Anbari, 2004) It has an organisation-wide cost of qual
It is seen that Six Sigma and TQM share common ground in terms of theory, philosophical
approach, CI focus, aims, principles, links to the teachings of Deming, focus on people, approach
to design, focus on customer, focus on process and dependence on management support. On the
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other hand, Six Sigma and TQM are different in terms of mutual relationship (Six Sigma can be
seen as part of the holistic TQM. TQM can help Six Sigma and Six Sigma extends TQM),
financial focus and scope, incentives and career development, strategic link, project selection
approach, training focus and intensity, team approach, structure, progress monitoring, basis for
motivation, tools, performance target, focus on suppliers and record of results. However, these
differences can be considered as additional strengths for the integration of TQM and Six Sigma
as the weaknesses of one are completed by the strengths of the other. Based on observation of
many firms, Lucas proposed that (Yang, 2004): Current business system + Six Sigma = TQM
(2) Schroeder et al. (2008) proposed that the introduction of Six Sigma to organisations that
already have TQM would help them realise incremental benefits in their financial results and
customer service. The application of Six Sigma can help strengthen the values of TQM within an
organisation (Anderson et al., 2006). Thus, TQM and Six Sigma are similar in many aspects and
compatible with each other. They share numerous values and aims and both can benefit from the
advantages that each can provide where TQM can be the holistic and comprehensive umbrella
that reaches to all stakeholders and Six Sigma can be the extension that provides a strong
structure for achieving greater process improvements. Six Sigma has roots traced back to TQM
(Upton and Cox, 2008). Six Sigma principles are embedded in TQM (Sheehy et al., 2002) and it
could be seen as a concept supporting the aims of TQM.
III. Facets of six sigma implementation in SMEs
In the following paragraphs, the process used to develop the Six Sigma process will be
presented, starting with the team member's selection. 3.1 Six Sigma Team Members Six Sigma
Project started in November 2004, as a cooperation project between Manufacturas Quality Ltda
and the Faculty of Industrial Engineering of the Ibagué University. Some meetings were
organized to present to the Top Management of the company all aspects related with Six Sigma
approach and the nature of the improvement project. A Guide to Six Sigma was prepared and
distributed in the meetings. The general aspects of the project were presented to all the
company's personnel, explaining what was planned and which will be their role. In the following
weeks Six Sigma team was selected. The small dimension of the company conditioned the team
structure. After analyze skills and profile of managers and employees, the decision was that
Marketing Manager will act as the Champion, and that Production Manager will act as Black
Belt, considering his previous experience in Quality and his deep knowledge of the process.
Quality Supervisor was selected to act as Green Belt. The absence of an engineer in the
production area, requested to incorporate to the Six Sigma Team a member of the University
project team. Thus, C. Tavera, from the Ibagué University, was appointed as Master Black Belt
with the mission of training Black Belt in statistical and nonstatistical improvement tools. Other
University project member, D. Segovia, has the responsibility for training and helping Green
Belt. 3.2 Improvement Projects Identification In the third week of December, the team started the
analysis of the organization, for better understanding its present situation, customers, products,
problems, etc. This week and in subsequent meetings, possible problems affecting quality were
5|P a g e
evaluated. To do this, DMAIC process was used, and some tools corresponding to Define phase
were applied (Escalante, 2003; Gitlow, 2007). The first tool used was Brainstorming. Six Sigma
team members expressed their opinions about present quality problems in the company. Critical
quality characteristics were identified tentatively. The need for a process flow diagram was
recognized, and that for the basic T-shirt was prepared (Figure 2). The Brainstorming produced a
list of quality problems, possible candidates to the improvement process: large amount of error
in basic T-shirt production, delays in delivery dates, lack of adequate inspections in supplies
at the start of the basic T-shirt process, some minor and isolated problems, as shortage of
supplies, energy disruptions, etc. 3.3 Data Measurement To define which of these problems was
more relevant, a Pareto analysis was developed. The frequency of the different problems was
controlled, and the results show that the first one (large amount of errors in basic T-shirt
production) was the most important, representing 55% of the total incidence of quality problems
(see Table 1).
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IV. Six sigma implementation-case study six sigma
a. Definition
Six Sigma (6σ) is a set of techniques and tools for process improvement. It was introduced by
engineer Bill Smith while working at Motorola in 1980.[1][2] Jack Welch made it central to his
business strategy at General Electric in 1995. A six sigma process is one in which 99.99966% of
all opportunities to produce some feature of a part are statistically expected to be free of defects.
Six Sigma strategies seek to improve the quality of the output of a process by identifying and
removing the causes of defects and minimizing variability in manufacturing and business
processes. It uses a set of quality management methods, mainly empirical, statistical methods,
and creates a special infrastructure of people within the organization who are experts in these
methods. Each Six Sigma project carried out within an organization follows a defined sequence
of steps and has specific value targets, for example: reduce process cycle time, reduce pollution,
reduce costs, increase customer satisfaction, and increase profits.
The term Six Sigma (capitalized because it was written that way when registered as a Motorola
trademark on December 28, 1993) originated from terminology associated with statistical
modeling of manufacturing processes. The maturity of a manufacturing process can be described
by a sigma rating indicating its yield or the percentage of defect-free products it creates—
specifically, within how many standard deviations of a normal distribution the fraction of defectfree outcomes corresponds to. Motorola set a goal of "six sigma" for all of its manufacturing.
b. The effects on society of six sigma
Six Sigma programs attempt to improve the processes with the firm with the focus on reducing
variability in orga- nizational processes and routines (Linderman et al., 2003; Schroeder et al.,
2008). A popular framework for Six Sigma is DMAIC which encompasses Design, Measure,
Analyze, Improve, and Control phases (Hammer, 2002; Linderman et al., 2003, 2006; Knowles
et al., 2005). This structured methodology helps Six Sigma programs to identify the root causes
of the problem, look for solution, and improve the process. It should be noted that in the search
for improvement in organizational routines and procedures, Six Sigma efforts are primarily
focused on improving efficiency within an existing technological base of the firm (Benner and
Tushman, 2003). Because of the focus of process improve- ment programs on continuous and
7|P a g e
incremental change, they are best suited for improving the existing technologi- cal trajectory of
the firm. In the pursuit of reducing vari- ability and increasing efficiency, Six Sigma programs
ensure that the new technological innovation (in processes or systems) are very close to the
current technological base of the firm. Accordingly, P1: Six Sigma programs positively affect
incremental innovation of the firm. Six Sigma programs improve organizational procedures and
routines. Six Sigma assumes that the current organiza- tional processes are sound but they need
minor (incremen- tal) improvement to be efficient (Hammer, 2002). Six Sigma does not change
the integrity and interconnectedness of
organizational processes; rather, in improves them.
Therefore, P2: Six Sigma programs positively affect modular inno- vation in the firm. According
to Douglas and Erwin (2000) Six Sigma is a concept that concentrates on the customer rather
than the product. The primary target for Six Sigma improve- ment efforts are the existing
customers. Information and data from the existing customers are collected and ana- lyzed, and
Six Sigma projects are defined to improve the processes in order to meet customer requirements.
Org
c. Quality of six sigma
Well controlled process that is ± six sigma from the centerline of a control chart; thus, no defects
within six standard deviations at the target level of performance. It translates into 0.00034
percent defects (3.4 defects per million) or, in practical terms, zero defects.
An addition to the U.S. Constitution which states that Congress shall have the power to levy and
collect taxes on income derived from occupation and other activities.
Degree to which a statistical distribution is not in balance around the mean (is asymmetrical or
lopsided), a perfectly symmetrical distribution having a value of 0. Distributions with extreme
values (outliners) above the mean have positive skew, and the distributions with outliners below
the mean have negative skew.
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V. The dmaic six sigma methodology
The Six Sigma is not merely a quality improvement strategy in theory, as it features a well
defined methodical approach of application in DMAIC and DMADV which can be used to
improve the quality of production. At the core of DMAIC, the framework is a formalized
improvement strategy with the following five facets i.e. define, measure, analyze, improve and
control (DMAIC).
a. Define Phase
Development of a Project Charter
The define facet of Six Sigma adjudicates the objectives and the goal of the project. This phase
also pile up evidences on processes and particularize the yield to internal and end customers.
Opportunity statement
Reduction in PPM Level at final assembly line which will reduce in rework, field failure,
increase the productivity and thus improve customer satisfaction. As a result of high PPM Level,
customer satisfaction is low.
Goal Statement
Reductions in internal PPM from 18909 PPM to 2500 PPM for Lighting stalk assembly.
Critical to Quality
“Critical to Quality” shows the constituent within a process that has a vital ascendancy on the
process quality and customary the quality of a crucial process, or need more attention in Six
Sigma project.
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Table1: Critical to Quality
CTQ
Critical to
Customer
Requirement
PPM
level
Voice of the
Customer
Requirement
Key Customer
Issue
Requirement
PPM
level less
than 2500
(For total
defects )
1. No noise / Low noise level from
steering
wheel
2. Wiper system should function
whenever
operates the Combi switch.
3. Combi- switch side indicator
function
should not fail.
4. Horn should function whenever SW
operates.
1. Noise from steering
wheel
while turning of vehicle.
2. Wiper is not working
in flick
wiper mode.
3. Side indicator not
working.
4. Horn is not blowing
Table 2: SIPOC Diagram
Supplier
Component
stores
Input
Lighting stalk
assembly
Process
Combination
Switch assembly
Output
Assembled
Switch
Customer
Inspection and
External Customer
M1
Wiper stalk
assembly
Central housing
assembly
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Table 3: Input indicators
Input
Contact Pin
Input indicators
Material specification
Hardness
Dimenssion
Plating
Cover
Block Dimenssion
Flash
Pawl fitment dimenssion
Profile
Pivoting Pin Diameter
Slot Dimenssion for action slide movement
Table 4 lists out most basic steps of process where the major steps on the top of board in order that they
occur in process. Under each major step, there is need of listing the different sub-steps that make up the
element of the process.
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Table 4 Process mapping through Top-Down Charting
Process
Manufacturing Of Combination Switch
Sub
processes
Lighting stalk
assembly
Wiper stalk
assembly
Central housing
assembly
Mounting of
subassembly
Activities
LCS base soldering
and visual
inspection
Wiper base
soldering and
visual inspection
Assembly of
Central housing,
Horn pin, Spring
and Retainer plate
Mounting of
lighting stalk
sub assembly,
wiper stalk
subassembly,
central housing
assembly
Screwing of lever,
Spacer, and LCS
fret base
Wiper assembly
riveting and visual
inspection
Screwing
CPC assembly
Riveting of 6
contact pins
Assembly of clamp
plate, Nut, Spring
CPC assembly with
lever
Riveting of contact
blade, contact pins
wash feed link and
link blade
Mounting of striker
bush with
application of
grease
Screwing of cap
assembly with CPC
assembly
Visual inspection
Date Stamping
12 | P a g e
Table 5 Process Indicators for Lighting Stalk
Head Off /Blink
Printing Defective
Park / Head Circuit Interchanged Right engagement return
LCS Snap Defective
Right lane change off
D.I. Snap Left lane change off Head OFF Rivet bend
Flash Operation Noisy (H/D) Check date stamping.
L- Side Indicator OFF
b. Measure Phase
This phase forms the measurement systems for the inputs and outputs of the selected project with major
focus on lighting stalk assembly. It also ensure that way of aggregating data as well accumulated data is
right data from right place. If data accruement manner is wrong then it will provide inaccurate inferences
in the phases underneath. Therefore, achieving success in the measure phase is vital. For SS project, an
operational definition focuses on meticulous definition of a measure while collecting every type of data
and is well-defined.
The data accumulation will be senseless if the definition has not been delimitated in time. So, operational
definitions should be eventuated and verified before the data picking up commences.
Then data collection for complete 30 days of production processes was measured as per operational
definition for each defect from process indicators for each sub-assembly was analyzed but this paper
specifically focuses on SS employment on lighting stalk assembly. The PPM level of each sub assemblies
is shown below:
13 | P a g e
Measuring Process Indicators:
It is necessary to find which inputs affect outputs (CTQ‟s) most. The following observation shows the
measurement of data with regards to lighting stalk assembly.
14 | P a g e
Table: Notations for Process indicator
Process Indicators
Notation
Measurement
Parking OFF/ Blink
y1
Ref. Figure 4
Printing Defective
y2
Ref. Figure 5
LCS Snap defective
y3
Ref. Figure 6
D.I. Snap
y4
Ref. Figure 7
Head Off/ Blink
y5
Ref. Figure 8
Right Lane change Off
y6
Ref. Figure 9
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16 | P a g e
Preliminary Failure Mode & Effect Analysis (FMEA) is also employed in this measure phase which
supports to establish the identity and accomplish apparent predicament in order to curtail defects and
redeem costs as soon as possible.
Outcome from Measure Phase:
Benchmark period is assigned for PPM level of rejection at customer end and internal assembly. In
project scope start should be from receipt of raw material / component from own organisation. Internal
failure cost should co-relate with the 2500 PPM target. In SIPOC, the list of component Part No. should
be given in a part description. In process walk through should be in details like include the observation on
housekeeping, assembly layout, material flow, visual control and work instructions. Special process on
the shop floor should be identified separately. Additional Operating resources for data collection are
required and will be provided.
17 | P a g e
3.
ANALYZE PHASE
The Analyze phase has five steps:
1. Develop a more detailed process map (that is, more detailed than the process map developed
in the SIPOC analysis of the Define phase).
2. Construct operational definitions for each input or process variable (called Xs).
3. Perform a Gage R&R study on each X (test the adequacy of the measurement system).
4. Develop a baseline for each X.
5. Develop hypotheses between the Xs and Ys.
The Ys are the output measures used to determine whether the CTQs are met.
Team members prepare a detailed process map identifying and linking the Xs and Ys, as shown
in Figure 16.11.
Team members develop an operational definition for each X variable identified on the process
map. The operational definitions for X1, X2, X3, and X8 relate to individual MSDs and are
shown below.
Criteria: Each X conforms to either one or the other of the options.
X1 Vendor
Ibix
Office Optimum
X2 Size
X3 Ridges
X8 Type of usage
Small (stock size)
With ridges
Large stack of paper (number
of papers is 10 or more)
Large (stock size)
Without ridges
Small stack of paper (number
of papers is 9 or less)
Test: Select MSD.
Decision: Determine X1, X2, X3, and X8 options for the selected MSD.
The operational definitions for the procedures used to measure X4, X5, X6, and X7 are shown
below.
Criteria: Compute the cycle time in days by subtracting the order date from the date on the bill
of lading.
18 | P a g e
Process Map Linking CTQs and Xs for the MSD Purchasing Process
X4
Cycle time from order to receipt for MSDs
In days
Test: Select a box of MSDs upon receipt of shipment from vendor. Compute the cycle time.
19 | P a g e
Decision: Determine X4 for the selected box of MSDs.
Criteria: Count the number of boxes of MSD received for a given order. Subtract the number
of boxes ordered from the number of boxes received for the order under study.
X5 Discrepancy in count from order placed and order
In boxes of
MSDs by order
received
Test: Select a particular purchase order for MSDs.
Decision: Compute the value of X5 in number of boxes for the selected purchase order.
Criteria: Compute the cycle time in days to place a shipment of MSDs in inventory by
subtracting the date the shipment was received from the date the order was placed in inventory.
X6
Cycle time to place product in inventory
days
In
Test: Select a particular purchase order.
Decision: Compute the value of X6 in days for the selected purchase order.
Criteria: Compute the inventory shelf-time in days for a box of MSDs by subtracting the date
the box was placed in inventory from the date the box was removed from inventory.
X7
Inventory shelf time
In
days
Test: Select a box of MSDs.
Decision: Compute the value of X7 in days for the selected box of MSDs.
Team members conduct Gage R&R studies for the Xs. Recall that the purpose of a Gage
R&R study is to determine the adequacy of the measurement system for an X. In this case, the
measurement systems for all of the Xs are known to be reliable and reproducible. Hence, Gage
R&R studies were not conducted by team members.
Team members gather baseline data on durability (Y1) functionality (Y2), and the relevant
Xs using the following sampling plan. For a 2-week period, the first box of MSDs brought to
20 | P a g e
the PSD each hour was selected as a sample. This yielded a sample of 80 boxes of MSDs,
which can be seen Table.
Table: Baseline Data
Sampl
e
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Day
Mo
n
Mo
n
Mo
n
Mo
n
Mo
n
Mo
n
Mo
n
Mo
n
Tue
Tue
Tue
Tue
Tue
Tue
Tue
Tue
We
d
We
d
We
d
We
d
We
Ho
ur
1
X1
X2
0
X
3
0
X
7
7
Du
r
2
Fu
n
5
1
2
0
1
0
7
2
9
3
0
0
1
7
10
7
4
0
1
0
7
1
4
5
0
0
0
7
7
3
6
0
1
1
7
2
5
7
0
1
1
7
1
9
8
0
0
0
7
7
5
1
2
3
4
5
6
7
8
1
0
0
0
1
1
1
1
0
1
1
1
1
1
1
1
1
0
1
0
0
0
1
0
1
1
1
1
8
8
8
8
8
8
8
8
9
2
1
1
9
9
10
10
8
8
8
7
13
5
9
11
11
9
11
2
1
0
0
9
1
11
3
1
1
1
9
10
11
4
0
0
0
9
7
11
5
1
1
1
9
9
9
21 | P a g e
22
23
24
25
26
27
28
29
30
31
d
We
d
We
d
We
d
Thu
Thu
Thu
Thu
Thu
Thu
Thu
6
0
0
1
9
9
5
7
1
0
1
9
2
11
8
1
0
0
9
1
10
1
2
3
4
5
6
7
1
0
1
0
0
0
0
0
1
1
0
0
1
0
1
1
1
1
0
1
0
10
10
10
10
10
10
10
1
1
8
10
7
3
9
14
10
13
12
14
13
13
For each sampled box, team members determined the durability (Y1) and functionality
(Y2) measurements. Furthermore, information concerning the vendor (X1), size of the MSD
(X2), whether the MSD has ridges (X3), and inventory shelf-life is recorded (X7).
Table: Baseline Data
(Continue)
Sampl
e
32
33
34
35
36
37
38
39
40
41
42
43
44
45
Day
Thu
Fri
Fri
Fri
Fri
Fri
Fri
Fri
Fri
Mo
n
Mo
n
Mo
n
Mo
n
Mo
n
Hou
r
8
1
2
3
4
5
6
7
8
1
X1
X2
X7
Dur
1
1
1
1
1
1
1
0
1
1
X
3
1
0
0
0
0
0
0
0
0
1
10
1
1
1
1
1
1
1
1
4
8
2
2
1
3
2
10
10
2
3
Fu
n
11
0
1
6
3
2
6
0
0
4
1
0
0
0
0
0
1
0
0
0
2
0
1
0
4
3
7
3
0
1
1
4
3
3
4
0
0
0
4
10
2
5
1
1
0
4
8
5
22 | P a g e
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
Mo
n
Mo
n
Mo
n
Tue
Tue
Tue
Tue
Tue
Tue
Tue
Tue
We
d
We
d
We
d
We
d
We
d
We
d
We
d
We
d
Thu
Thu
Thu
Thu
Thu
Thu
Thu
Thu
Fri
Fri
Fri
Fri
Fri
Fri
Fri
Fri
6
0
1
1
4
3
4
7
1
0
0
4
10
4
8
0
0
1
4
3
5
1
2
3
4
5
6
7
8
1
1
1
1
1
1
0
0
0
0
1
0
1
0
0
0
0
1
0
1
1
0
1
0
0
1
1
0
5
5
5
5
5
5
5
5
6
2
9
9
3
9
9
9
3
9
6
4
6
4
6
5
4
5
5
2
1
0
1
6
9
7
3
0
0
0
6
9
5
4
1
1
0
6
2
7
5
1
1
0
6
2
5
6
1
1
1
6
10
7
7
0
0
1
6
1
7
8
0
1
0
6
2
5
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
0
1
1
0
1
0
1
1
0
1
1
0
0
1
1
0
0
1
0
1
0
1
0
1
1
1
0
1
1
1
0
0
0
1
0
0
0
1
0
0
1
1
1
0
1
1
1
1
7
7
7
7
7
7
7
7
8
8
8
8
8
8
8
8
10
9
1
2
1
1
1
10
3
9
1
2
3
8
3
10
7
5
7
5
6
5
8
5
7
7
13
8
9
10
11
11
DATAMINING
X1 = vendor (0 = Office Optimum and 1 = Ibix)
23 | P a g e
X2 = size (0 = small and 1 = large)
X3 = ridges (0 = without and 1 = with)
X7 = inventory shelf-time, in days WEB
The Purchasing Department will separately study cycle time from order to receipt of order
(X4), discrepancy between ordered and received box counts (X5), and cycle time from receipt
of order to placement in inventory (X6). These last factors may influence such concerns as
choice of vendor, ordering procedures, and inventory control, but they do not impact durability
and functionality. Furthermore, the MSDs are not tested after they are used, so the type of usage
(X8) is not studied here. As was indicated in the Define phase, certain variables (e.g., X4, X5,
X6, and X7) can be addressed in subsequent Six Sigma projects. The baseline data revealed that
the yield for durability is 0.4625 (37/80) and the yield for functionality is 0.425 (34/80), as
shown in Table 16.21. As before, this indicates very poor levels for the CTQs in the PSD. For
comparison purposes, the judgment sample carried out by the team during the Define phase
showed that the yield was approximately 40% (i.e., the team assumed the failure rate was
approximately 60%) for both durability and functionality. The slightly increased yields in this
study can be due to natural variation in the process.
The baseline data also showed that 56.25% of all MSDs are from Office Optimum (X1),
42.50% of MSDs are small (X2), 50.00% of all MSDs are without ridges (X3), and the average
shelf-time for boxes of MSDs (X7) is 6.5 days, with a standard deviation of 2.5 days (see
Table).
Team members develop hypotheses [Y = f(X)] about the relationships between the Xs and
the Ys to identify the Xs that are critical to improving the center, spread, and shape of the Ys
with respect to customer specifications. This is accomplished through data mining. Data mining
is a method used to analyze passive data; that is, data that is collected as a consequence of
operating a process. In this case, the baseline data in Table is the passive data set that will be
subject to data mining procedures. Dot plots or box plots of durability (Y1) and functionality
(Y2) stratified by X1, X2, X3, and X7 can be used to generate some hypotheses about main
effects (i.e., the individual effects of each X on Y1 and Y2). Interaction plots can be used to
generate hypotheses about interaction effects (i.e., those effects on Y1 or Y2 for which the
influence of one X variable depends on the level or value of another X variable) if all
combinations of levels of X variables are studied. If not all combinations of levels of X
variables are studied, then interaction effects are often not discovered. Team members
constructed dot plots from the baseline data in Table 16.20 to check whether any of the Xs (i.e.,
main effects) impact durability (Y1) and functionality (Y2).
Table: Basic Statistics on Baseline Data
Variable
Y1: Durability
Four
or
Proportion Mean
more 0.4625
5.213
Standard
deviation
3.703
24 | P a g e
Y2: Functionality
X1: Vendor
X2: Size
X3: Ridges
X7: Inventory shelf-time
bends/clip
Five
or
fewer
broken/box
0 = Office Optimum
1 = Ibix
0 = Small
1 = Large
0 = Without ridges
1 = With ridges
Shelf-time in days
0.4250
7.025
3.438
6.5000
2.5160
0.5625
0.4375
0.4250
0.5750
0.5000
0.5000
Minitab Dot Plot for Durability by X1 (i.e., Vendor)
25 | P a g e
Minitab Dot Plot for Durability by X2 (i.e., Size)
Minitab Dot Plot for Durability by X3 (i.e., Ridges)
26 | P a g e
Minitab Dot Plot for Durability by X7 (i.e., Shelf-life)
Minitab Dot Plot for Functionality by X1 (i.e., Vendor)
27 | P a g e
Minitab Dot Plot for Functionality by X2 (i.e., Size)
Minitab Dot Plot for Functionality by X3 (i.e., Ridges)
28 | P a g e
Minitab Dot Plot for Functionality by X7 (i.e., Shelf-life)
The dot plots for durability (Y1) indicate: (1) the values of durability tend to be low or
high, with a significant gap between 4 and 6 for X1, X2, X3, and X7, and (2) the variation in
durability is about the same for all levels of X1, X2, X3, and X7. The dot plots for functionality
(Y2) indicate: (1) the values of functionality tend to be lower when X1 = 0 than when X1 = 1,
(2) the variation in functionality is about the same for all levels of X2 and X3, and (3) the
values of functionality tend to be lower for low values of X7.
Discussion of the Analysis of Durability. Because there are no clear differences in
variation (i.e., spread) of durability for each of the levels of X1, X2, X3, and X7, the team
wondered whether there might be differences in the average (i.e., center) for each level of the
individual Xs. Team members constructed a main effects plot for durability to study differences
in averages.
Figure indicates that for the ranges of shelf-life observed, there is no clear pattern for the
relationship of shelf-life (X7) to the average durability. On the other hand, it appears that ridges
(i.e., X3 = 1) have a positive relationship to the average durability. At first glance, it would
seem that better results for average durability are seen when the vendor Ibix is chosen using
small MSDs (i.e., X1 = 1 and X2 = 0), whereas using large MSDs from Office Optimum (i.e.,
X1 = 0 and X2 = 1) yields worse results.
While discussing the dot plots and main effects plot, it is dangerous to make any
conclusions without knowing whether there are interaction effects. An interaction effect is
present when the amount of change introduced by changing one of the Xs depends on the value
of another X. In that case, it is misleading to choose the best value of the Xs individually
29 | P a g e
without first considering the interactions between the Xs. Consequently, team members did an
interaction plot for X1, X2, and X3. X7 was not included in the interaction plot because the
main effects plot indicated no clear pattern or relationship with durability (Y1). All
combinations of levels of the X variables must be present to draw an interaction plot. This is
often not the case with passive data (i.e., no plan was put in place to insure all combinations
were observed in the data-gathering phase). Fortunately, although not all combinations were
observed equally often, they were all present. Figure 16.21 is the interaction plot for durability.
Minitab Main Effects Plot for Durability by X1, X2, X3, and X7
30 | P a g e
Minitab Interaction Effects Plot for Durability by X1, X2, and X3
Surprise! The interaction plot indicates that there is a possible interaction between X1 (i.e.,
vendor) and X2 (i.e., size). How is this known? When there is no interaction, the lines should be
parallel to each other, indicating that the amount of change in average durability when moving
from one level of each X variable to another level should be the same for all values of another
X variable. This plot shows the lines on the graph of X1 and X2 not only are not parallel, but
they cross. The average durability is the highest when either large Ibix MSDs (i.e., X1 = 1 and
X2 = 1) or small Office Optimum MSDs (i.e., X1 = 0 and X2 = 0) are used. This means the
choice of vendor may depend on the size of MSD required. The main effects plot suggests that
the best results for average durability occurs when small MSDs from Ibix are used, but the
interactions plot suggests this combination yields a bad average durability. To study all of this
further, the team decides that during the Improve phase, they will run a full factorial design to
examine the relationship of X1, X2, and X3 on durability (Y1) because the main effects plot
indicates potential patterns. Again, there does not appear to be a relationship between durability
(Y1) and X7.
Discussion of the Analysis of Functionality. Figures show the main effects and
interaction effects plots for functionality (Y2).
The main effects plot indicates that higher values of shelf-life (X7) yield higher values for
functionality (Y2). The team surmised that the longer a box of MSDs sets in inventory (i.e.,
higher values of shelf-life), the higher will be the count of broken MSDs (i.e., functionality will
be high). From a practical standpoint, the team felt comfortable with this conclusion. They
decided the Purchasing Department should put a Six Sigma project in place to investigate
whether the potential benefit of either a “just-in-time” MSD ordering process or the
establishment of better inventory handling procedures will solve the problem.
31 | P a g e
The interaction effect plot indicates a potential interaction between the X2 (i.e., size) and
X3 (i.e., ridges). Better results for functionality (i.e., low values) were observed for large MSDs
without ridges (i.e., X2 = 1 and X3 = 0). Why this may be the case needs to be studied further.
Also, there may be an interaction between X1 (i.e., vendor) and X2 (i.e., size), but it appears
that better results are observed whenever Office Optimum is used (i.e., X1 = 0). In other words,
the average count of broken MSDs is lower (i.e., functionality average is lower) whenever
Office Optimum is the vendor.
Minitab Main Effects Plot for Functionality by X1, X2, X3, and X7
32 | P a g e
Minitab Interaction Effects Plot for Functionality by X1, X2, and X3
Analyze Phase Summary. The Analyze phase resulted in the following hypotheses:
Hypothesis 1: Durability = f(X1 = Vendor, X2 = Size, X3 = Ridges) with a strong interaction
effect between X1 and X2.
Hypothesis 2: Functionality = f(X1 = vendor, X2 = size, X3 = ridges, X7 = shelf-life), the
primary driver being X7 with some main effect due to X1 and an interaction effect between X2
and X3. X7 is the main driver of the distribution of functionality (Y2) and is under the control
of the employees of POI. Hence, team members restructured Hypothesis 2 as follows:
Functionality = f(X1 = vendor, X2 = size, X3 = ridges) for each fixed level of X7 (shelf-life).
33 | P a g e
4. IMPROVE PHASE
The Improve phase involves designing experiments to understand the relationship between
the Ys and the vital few Xs and major noise variables (see Chapter 13); generating the actions
needed to implement the levels of the vital few Xs that optimize the shape, spread, and center of
the distributions of the Ys; developing action plans; and conducting pilot tests of the action
plans.
Team members conducted an experimental design to determine the effect of X1 (vendor),
X2 (size), and X3 (ridges), and their interactions, on the Ys, with X7 = 0 (no shelf-life—MSDs
are tested immediately upon arrival to POI before they are placed in inventory). A 23 full
factorial design with two replications (16 trials) was performed for durability and functionality.
The factor conditions for vendor (X1) are Office Optimum (–1) or Ibix (1); the factor conditions
for size (X2) are small (–1) or large (1), and the factors conditions for ridges (X3) are without
ridges (–1) or with ridges (1). The experiment was set up in two blocks to increase experimental
reliability, with the first eight runs conducted in the morning and the second eight runs conducted
in the afternoon. The runs were randomized within each block. The purpose of the blocks and
randomization is to help prevent lurking (background) variables that are related to time (e.g.,
time of day and order in which data is collected) from confusing the results. Additional
information can be gathered because 16 trials were run, rather than the minimum of 8 trials,
especially regarding potential interactions. The data from the 23 full factorial design (with two
replications in run order, the first eight runs constituting the first replicate) is shown in Table.
TABLE: Durability and Functionality Data
Std order Run order Vendor
Size
2
1
Ibix
Small
3
2
Ibix
Large
Ridges
Without
Without
Durability Functionality
1
8
9
9
4
8
5
6
7
1
16
10
12
14
13
11
9
15
Without
With
With
With
With
Without
With
Without
Without
With
With
Without
Without
With
1
11
10
4
4
10
9
3
9
3
9
2
8
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Office Optimum
Ibix
Ibix
Ibix
Office Optimum
Office Optimum
Office Optimum
Office Optimum
Office Optimum
Large
Large
Small
Small
Large
Small
Large
Small
Large
Small
Small
Large
Small
Large
8
8
0
2
3
2
3
0
0
7
6
4
1
4
34 | P a g e
Pareto charts showing which of the vital few Xs and the interactions between them have a
statistically significant effect on durability (Y1) and functionality (Y2) at the 10% level of
significance can be seen in Figures, respectively.
Minitab Pareto Chart of Effects for Durability
Minitab Pareto Chart of Effects for Functionality
35 | P a g e
The major effects (i.e., those that have significance level less than 0.10—in other words,
over 90% confidence level) for durability are the interaction of vendor and size and the main
effect due to ridges. There are no significant effects due to vendor, size, or ridges present for
functionality. This indicates that because the effect of shelf-life was held constant in this
designed experiment, although it was shown to affect functionality in the data mining analysis,
the team can restrict its attention to improving functionality by addressing shelf-life first.
Because durability is the only outcome influenced by vendor, size, or ridges in this designed
experiment, further consideration in this study will be restricted to durability. Another project
can address shelf-life and its effect on functionality.
Because interaction effects should be interpreted prior to studying main effects, the team
decided to construct an interaction effect plot for vendor and size. Figure is the interaction effect
plot for vendor and size, relative to durability.
Minitab Interaction Effect Plot for Vendor and Size, Relative to Durability
The interaction effect plot between size and vendor shown in Figure indicates that the best
results for durability are obtained using small MSDs supplied by Office Optimum or large MSDs
supplied by Ibix. The reasons for this interaction may be due to factors such as materials used for
each size of MSD, differences in supplier processes for each size of MSD, or other
supplierdependent reasons. Team members can ask each vendor why its sizes show significant
differences in average durability, if there is a preference to use only one vendor. Otherwise, the
Purchasing Department should buy small MSDs from Office Optimum or large MSDs from Ibix
to optimize durability (Y1).
The only significant main effect not involved within a significant interaction effect is X3,
ridges. The main effect for ridges on durability is shown in Figure.
This plot indicates that the average durability is about 6.5 – 5.4 = 1.1 more when an MSD
with ridges is used rather than an MSD without ridges. Therefore, because ridges is a main effect
independent of any interaction effects, the right selection of MSDs is to use Office Optimum for
36 | P a g e
small MSDs with ridges and Ibix for large MSDs with ridges. If the experimental results from
Table 16.22 are used, the average durability for Office Optimum’s small MSDs with ridges is
(10 + 9) / 2 = 9.5, and the average durability for Ibix’s large MSDs with ridges is (11 + 9) / 2 =
10.0. Both averages are well above the required corresponding CTQ of at least 4. As long as the
variation (spread) of results is small enough so that no individual durability result is far from
these averages, the team is successful with respect to durability. The variation in these results can
be monitored using control charts after changing the purchasing process for selecting MSDs.
The team members decided to purchase all MSDs with ridges. In addition, the choice of
vendor and size will be as follows: (vendor = Office Optimum) and (size = small) or (vendor =
Ibix) and (size = large) to maximize average durability. In addition, the team decided to take on
another project to reduce shelf-life to less than 5 days to address functionality. The revised
flowchart for the Purchasing Department incorporating the findings of the Six Sigma project is
shown in Figure.
The team members conducted a pilot test of the revised best practice (see the flowchart in
Figure ). Data for durability from the pilot test is shown in Table.
Table indicates that the rolled throughput yield (RTY) for durability is 100%. Functionality
was also tested (not shown here), using shelf-life = 0 days; that is, the MSDs were tested
immediately upon arrival to POI before they were placed in inventory. This resulted in an RTY
of 75%, which is better than the baseline RTY. The effect on functionality of shelf-life and
inventory control procedures will be investigated in subsequent projects if management decides
these projects should be chartered.
Figure shows that durability is in control, with a higher mean number of bends for all MSDs
in the pilot test. The test pilot data shown in Table 16.23 includes results for both small MSDs
from Office Optimum and large MSDs from Ibix. Subsequently, team members realized that,
with all things being equal, large MSDs from Ibix should have a higher average durability than
small MSDs from Office Optimum. Consequently, team members constructed two control charts,
one for small MSDs from Office Optimum and another for large MSDs from Ibix (Figures 16.30
on page 534 and 16.31 on page 535, respectively).
37 | P a g e
Minitab Main Effect Plot for Ridges, Relative to Durability
Revised Flowchart of the Purchasing Department
TABLE: Data from the Pilot Test
Hour
Shift 1 — Hour 1
Shift 1 — Hour 2
Shift 1 — Hour 3
Vendor
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Size
Small
Large
Small
Large
Small
Ridges
With
With
With
With
With
Durability
10
11
7
11
10
38 | P a g e
Shift 1 — Hour 4
Shift 1 — Hour 5
Shift 1 — Hour 6
Shift 1 — Hour 7
Shift 1 — Hour 8
Shift 2—Hour 1
Shift 2—Hour 2
Shift 2—Hour 3
Shift 2—Hour 4
Shift 2—Hour 5
Shift 2—Hour 6
Shift 2—Hour 7
Shift 2—Hour 8
Ibix
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Ibix
Office Optimum
Ibix
Large
Small
Large
Small
Large
Small
Large
Small
Large
Small
Large
Small
Large
Small
Large
Small
Large
Small
Large
Small
Large
Small
Large
Small
Large
Small
Large
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
With
11
8
11
9
10
9
9
8
11
9
10
9
11
8
10
10
9
7
9
7
10
9
11
10
9
8
11
32/32=1
39 | P a g e
Minitab Individual Value and Moving Range Chart for Durability
Figures show that durability (Y1) is in control, but it is dangerous to compute any process
capability statistics due to the small sample sizes. However, estimates for the mean and standard
deviation of small MSDs from Office Optimum are 8.625 and 1.05 (calculated from the data but
not shown here), respectively. The mean and standard deviation for large MSDs from Ibix are
10.25 and 0.83, respectively. Because the CTQ for durability requires the number of bends to be
four or more, this requirement is 4.4 standard deviations below the mean for small MSDs from
Office Optimum and 7.5 standard deviations below the mean for large MSDs from Ibix. Team
members all agreed that as long as the process for both small MSDs from Office Optimum with
ridges and large MSDs from Ibix with ridges remain in control, it is extremely unlikely that the
MSDs will fail the CTQ for durability (Y1).
Minitab Individual Value and Moving Range Chart for Durability of Small MSDs from Office Optimum
40 | P a g e
Minitab Individual Value and Moving Range Chart for Durability of Large MSDs from Ibix
5. CONTROL PHASE
The Control phase involves mistake proofing the improvements and/or innovations
discovered in the Six Sigma project; establishing a risk management plan to minimize the risk of
failure of product, service, or process; documenting the improvement and/or innovation in ISO
9000 documentation; and preparing a control plan for the process owners who will inherit the
improved or innovated product, service, or process; turning the process over to the process
owner; and disbanding the team and celebrating their success.
Team members identified and prioritized two problems while mistake proofing the process
improvements discovered in the improve phase. They are: (1) Purchasing agents do not specify
“with ridges” on a purchase order and (2) purchasing agents do not consider that the choice of
vendor depends on the size of the MSDs being requested on the purchase order.
Team members created solutions that make both errors impossible. They are: (1) The
purchase-order entry system does not process an order unless “with ridges” is specified on the
purchase order and (2) the purchase-order entry system does not process an order unless Office
Optimum is the selected vendor for small MSDs and Ibix is the selected vendor for large MSDs.
Team members use risk management to identify two risk elements. They are: (1) failing to train
new purchasing agents in the revised purchasing process shown in Figure and (2) Office
Optimum and Ibix are out of MSDs with ridges. Team members assigned risk ratings to both risk
elements, as shown in Table .
41 | P a g e
Both risk elements must be dealt with in risk abatement plans. The risk abatement plan for
“failing to train new purchasing agents” is to document the revised purchasing process in training
manuals. The risk abatement plan for “vendor out of MSDs with ridges” is for POI to request
that both Office Optimum and Ibix manufacture only MSDs with ridges, due to their superior
durability. This is a reasonable and acceptable suggestion to POI, Office Optimum, and Ibix
because the cost structures for manufacturing MSDs with and without ridges are equal, and
neither Office Optimum nor Ibix has other customers requesting MSDs without ridges. Office
Optimum and Ibix agree to produce only MSDs with ridges after a 6-month trial period in which
they check incoming purchase orders for requests for MSDs without ridges. If the trial period
reveals no requests for MSDs without ridges, the POI Purchasing Department will revise Figure
and the appropriate ISO 9000 documentation to reflect the possibility of purchasing only MSDs
with ridges. Additionally, Office Optimum and Ibix thanked POI for pointing out to them that
average durability is higher for MSDs with ridges than for MSDs without ridges. Both vendors
claim that they are going to experiment with possible different ridge patterns to increase
durability and decrease costs. Both vendors stated that they anticipate decreased costs from
producing only MSDs with ridges because of the lower amortized costs of having only one
production line.
Team members prepare ISO 9000 documentation for the revisions to the training manual for
the purchasing process from Figure .
Team members develop a control plan for the PSD that requires a monthly sampling of the
boxes of MSDs in inventory. The purpose of the sampling plan is to check whether the boxes of
MSDs being purchased are either small Office Optimum MSDs with ridges or large Ibix MSDs
with ridges. The percentage of nonconforming boxes of MSDs will be plotted on a p-chart. PSD
management will use the p-chart to highlight violations of the new and improved purchasing
process shown in Figure. The p-chart will be the basis for continuously turning the PlanDoStudy-Act (PDSA) cycle for the revised purchasing process.
Team members check the business indicator from the PSD and determine that production
costs in the PSD decreased, probably due to the MSD Six Sigma project The MSD project took
effect in month 73 of Figure.
TABLE:Risk Elements for Purchasing Process
Risk elements
Risk
Likelihood of
category
occurrence
Failing to train
Performance 5
new purchasing
agents Vendor
Materials
2
out of MSDs with
ridges
Impact of
occurrence
5
Risk element
score
25
High
2
10
Medium
Scale: 1–5, with 5 being the highest
42 | P a g e
VI. METHODS IN QUALITY IMPROVEMENT
a. Six sigma in Quality management
The TQM idea was created by various American administration experts, including W.
Edwards
Deming, Joeseph Juran, and A.V. Feigenbaum.Originally, these specialists won couple of
believers in the United States. In any case, supervisors in Japan grasped their thoughts
excitedly and even named their head yearly prize for assembling brilliance in the wake of
Deming.
The Six Sigma the executives technique began in 1986 from Motorola's drive towards
decreasing deformities by limiting variety in procedures. The primary distinction among
TQM and Six Sigma (a more up to date idea) is the approach. At its center, Total Quality.
The board (TQM) is an administration way to deal with long haul accomplishment
through consumer loyalty.
In a TQM exertion, all individuals from an association take an interest in enhancing
forms, items, administrations and the culture in which they work. The techniques for
executing this methodology originate from individuals, for example, Philip B. Crosby, W.
Edwards Deming, Armand V. Feigenbaum, Kaoru Ishikawa and Joseph M. Juran.
Six Sigma (6σ) is a lot of strategies and instruments for process enhancement. It was
presented by specialists Bill Smith and Mikel J Harry while working at Motorola in 1986.
Jack Welch made it integral to his business procedure at General Electric in 1995.
It tries to enhance the nature of the yield of a procedure by recognizing and expelling the
reasons for deformities and limiting fluctuation in assembling and business forms. It
utilizes a lot of value the executives strategies, for the most part exact, factual techniques,
and makes a unique framework of individuals inside the association who are specialists in
these strategies. Every Six Sigma venture did inside an association pursues a
characterized succession of steps and has explicit esteem focuses, for instance: lessen
process duration, diminish contamination, decrease costs, increment client fulfillment,
and increment benefits.
Six Sigma is a business the board methodology which goes for enhancing the nature of
procedures by limiting and in the end expelling the mistakes and varieties. The idea of
Six Sigma was presented by Motorola in 1986, however was advanced by Jack Welch
who fused the technique in his business forms at General Electric. The idea of Six Sigma
appeared when one of Motorola's senior administrators griped of Motorola's awful
quality. Bill Smith inevitably figured the philosophy in 1986.
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Quality assumes an essential job in the achievement and disappointment of an
association. Ignoring a critical angle like quality, won't let you make due over the long
haul. Six Sigma guarantees predominant nature of items by expelling the deformities in
the procedures and frameworks. Six sigma is a procedure which helps in enhancing the
general procedures and frameworks by distinguishing and in the end expelling the
obstacles which may stop the association to achieve the dimensions of flawlessness. As
indicated by sigma, any kind of test which runs over in an association's procedures is
viewed as an imperfection and should be killed.
Associations rehearsing Six Sigma make uncommon dimensions for representatives
inside the association. Such dimensions are called as: "Green belts", "Dark belts, etc.
People ensured with any of these belts are regularly specialists in six sigma process. As
indicated by Six Sigma any procedure which does not prompt consumer loyalty is alluded
to as an imperfection and must be disposed of from the framework to guarantee
predominant nature of items and administrations. Each association endeavors hard to
keep up great nature of its image and the procedure of six sigma guarantees the
equivalent by expelling different imperfections and blunders which come in the method
for consumer loyalty.
The procedure of Six Sigma started in assembling forms yet now it discovers its
utilization in different organizations too. Legitimate spending plans and assets should be
apportioned for the execution of Six Sigma in associations.
Following are the two Six Sigma strategies:
-
DMAIC
DMADV
DMAIC centers around enhancing existing business rehearses. DMADV, then again
centers around making new techniques and approaches.
b. Six sigma in Warehouse management
● Higher- and Lower-Level Root Causes
An analysis of excess and obsolete inventory often shows that its major root
causes are associated with long lead times, poor forecasting accuracy, quality
problems or design obsolescence. However, these higher-level causes can be
successively broken down into lower-level root causes as shown in the figure
below.
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As the figure recommends, from a stock speculation point of view, a long lead
time might be caused, to a limited extent, by vast part sizes. For instance, if the
genuine lead time or request process duration is 30 days, yet the required part
estimate for buy is 90 days of supply (DOS), at that point this parcel measure
drives a higher normal stock dimension than lead time independent from anyone
else. For this situation, the normal available stock (disregarding a wellbeing stock
estimation) increments from 15 to 45 DOS expecting a consistent utilization rate.
Obviously, the genuine explanations behind extensive parcel sizes would need to
be explored by a Lean Six Sigma enhancement group. The underlying drivers of
long lead times likewise could be because of confused procedures having various
improve circles and non-esteem including tasks and planning issues and
additionally late conveyances.
The second real reason for overabundance and outdated stock is poor interest the
board rehearses. Some lower-level underlying drivers may incorporate erroneous
authentic interest information, a poor guaging demonstrating philosophy or
different issues, for example, excessively hopeful deals projections. Lean Six
Sigma extends additionally can be utilized to assault bring down dimension
underlying drivers around there. Lean Six Sigma is as often as possible used to
enhance quality dimensions to lessen squander and improve caused by a large
number of various factors inside a procedure work process. At long last, Design
for Six Sigma can be utilized to enhance the plan forms for new items or
administrations.
● Using DMAIC to Find Root Causes
Lean Six Sigma enhancement groups can drive to the main drivers of their
abundance and old stock issue utilizing the DMAIC critical thinking system
(Define, Measure, Analyze, Improve, Control) related to Lean instruments and
additionally process work process models. Truth be told, building basic Excel45 | P a g e
based stock models or utilizing off-the-rack programming, are great approaches to
recognize the key procedure input factors (KPIVs) or drivers of overabundance
and out of date stock issues. Stock models pursue a summed up Six Sigma main
driver rationality – Y = f(x). They additionally are powerful correspondence
vehicles appearing, promoting, fabricating and other production network
capacities, and in addition the effect of lead time and request the board rehearses
on abundance and old stock.
In a genuine enhancement venture, the group starts a stock investigation by
characterizing the undertaking's objectives in the Define stage. Utilizing these
objectives as rules, important inquiries are produced to empower the group to see
how the framework works. Information fields comparing to these inquiries are
distinguished and removed from data innovation (IT) frameworks. The
information fields are then sorted out as a stock model to give the data important
to answer the group's inquiries and comprehend the main drivers of the stock
issue.
After the Define stage, the group starts to assess estimation frameworks and plan
information accumulation exercises. This is the Measure period of the task. An
essential action in this stage is an on location physical check by area of stocked
things related with the issue. This is done to quantify valuation exactness with
respect to expressed book esteem. Estimation investigations likewise are led of
the executives reports and their related work process frameworks. These
examinations decide the precision of key store network measurements, for
example, lead time, part estimate, anticipated interest and its variety, guaging
exactness (not quite the same as interest variety), on-time conveyance and
different measurements that might be identified with a stock speculation issue.
Sadly, inventory network measurements regularly are dispersed over the few
programming frameworks inside an association. These frameworks incorporate
the estimating module, ace creation plan module, materials necessities arranging
module, stock record documents, distribution center administration framework
module and comparable IT frameworks.
After check of a framework's measurements, the enhancement group starts
information gathering to catch data important to answer the group's inquiries
created amid the Define stage. Pertinent data, which may help the group in its
main driver examination, generally incorporates providers, lead times, anticipated
interest and its variety, parcel sizes, stockpiling areas, conveyance data, clients
and different certainties.
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c. Six sigma Points in Transportation management
Lean Six Sigma (LSS) is a quality activity program that offers some incentive to the two
clients and representatives. LSS gives a chance to representatives to have the capacity to
acquire a range of abilities and preparing in a zone that conveys an incentive to their
organization and clients through the end of waste and mistake.
There has been an adjustment in how the commercial center ponders quality in the
inventory network as of late. Lean Six Sigma is a program that consolidates the best of
Lean, which centers around the decrease of waste, and best of Six Sigma, which centers
around lessening mistake. In the production network, particularly in transportation the
board, there is a genuine requirement for end of waste and to impart progressively
proficient procedures.
By taking a gander at both interior and outside activities and openings, you can reveal
numerous zones to apply LSS standards with regards to transportation the board. For
instance, LSS standards can:
Dispense with mistakes and drive huge reserve funds with regards to streamlining inward
procedures. Organizations can expand their volume of business without requiring more
assets.
Help organizations cut down on outer waste for their clients by lessening additional costs
and driving investment funds.
There are different touch focuses among organizations and clients where waste and
blunder can be wiped out. In transportation the board, the absolute most basic spots to
search for diminishing waste is in the acquisition and activities process, foundation and
execution of directing aides, and in revealing.
d. Optimal algorithms to improve quality
More than one year after the introduction of digital algorithms, we carried out two cross
sectional studies to assess the improvements in comparison with the situation prior to the
implementation of the project, in two Basic Health Centres in Kabul province. One
survey was carried out inside the consultation room and was based on the direct
observation of 181 consultations of children aged 2 months to 5 years old, using a
checklist completed by a senior physicians. The second survey queried 181 caretakers of
children outside the health facility for their opinion about the consultation carried out
through the tablet and prescriptions and medications given.
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In subtleties, we looked at the execution of medicinal services specialists (HWs) in two
ARCS centers following one year of utilizing ALMANACH contrasted with the standard
earlier the usage.
To have a benchmark to all the more likely comprehend the results of ALMANACH, a
pattern study was completed before execution to evaluate the execution dimension of
clinical exercises. Two hundreds pediatric conferences for each wellbeing office were
seen by a senior Afghan specialist not connected to the venture. The example measure
accepted a distinction in actuality of 15%, an intensity of 0.8 and a blunder rate of 5%.
The outer evaluator recorded the youngster's age and sex, the protests, the indications
asked and the physical examination performed by the medicinal services supplier, and, at
long last, the analysis and prescription recommended. Side effects communicated by the
patients, signs evaluated and endorsed treatment were contrasted together and the finding
and the IMCI's rules.
Since the organization of the electronic gadget, automatic information (number of
interviews and finding) were progressively routinely transferred to District Health
Information System 2 (DHIS 2) to assist the program and wellbeing directors and to draft
a month to month epidemiological announcement to be imparted to the social insurance
specialists so as to make them mindful of their clinical execution.
Despite the fact that these standard information were routinely gathered, we didn't know
whether right now of the discussion, the HWs were less inclined to embrace the direction
of the CDSS or bound to utilize their own judgment and supersede proposals they felt
were not suitable. Consequently, in July 2017, over one year since the execution of
ALMANACH, the execution of clinical exercises was again evaluated by two diverse
reviews:
● Discussion room review (CRS): this overview had a spectator ordering data about
the meeting of kids at the BHCs through utilizing an agenda. This review was
finished inside the counsel room: recording quiet appraisal, physical examination,
analysis and endorsed treatment were cautiously watched and archived. To
encourage the correlation, the CRS utilized a similar agenda of the benchmark
review.
● Overseer study (CTS): to limit the perception inclination this study was led with
youngsters' (guardians or watchmen) following the counsel and without the
nearness of the medicinal services supplier. Overseers were gotten some
information about the conference utilizing the tablet and key data were gathered
about remedy and treatment got.
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There was no connection among CRS and CTS: CRS and CTS recorded administration of
various kids.
To ascertain the example measure for both overviews, we know, based on the standard
information gathered in over one year, that 25% of the kids got no less than one antimicrobial (ATB). With a certainty dimension of 95%, we assessed that 180 interviews
(90 counsels for every wellbeing office) would be sufficient to record the ATB medicine
with a certainty interim of ± 4.5. This example estimate does not permit to contrast the
past standard information and the new outcomes by stratifying per every wellbeing office
however it is adequate for worldwide correlation. The studies were completed just in two
wellbeing offices as the third BHC was avoided because of security concerns
counteracting supervision visits since February 2017.
Information were carefully gathered. For this reason, an electronic form of the surveys
was made (CommCare, Dimagi inc.). Information were then sent out to Microsoft Excel
(2016) and to STATA (StataCorp. 2013. Stata Statistical Software: Release 13. School
Station, TX: StataCorp LP) for further examination.
Through this examination we contrasted the aftereffects of the overviews and the pattern
regarding ATB solution and consistence with the IMCI. Results are shown in diagrams
and tables as extents or medians. At whatever point fundamental the chi square test (or ztest) was utilized to examine the distinctions, for this situation we considered a critical
contrast when the p esteem was <0.05.
Prior to continuing to any review, overseers and wellbeing suppliers' assent was taken.
The studies were directed in the system of the automatic venture evaluation of ICRC and
ARCS and they were upheld by these two associations and they were excluded for moral
endorsement.
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REFERENCES
1. Gitlow, H., “Innovation on Demand,” Quality Engineering, 11, 1998–1999, pp. 79–89.
2. Gitlow, H., A. Oppenheim, R. Oppenheim, and D. Levine Quality Management, 3rd
ed.
(New York: McGraw-Hill-Irwin, 2005).
3. Deming, W. E., The New Economics: For Industry, Government, Education,
(Cambridge,
MA: M.I.T., Center for Advanced Engineering Study, 1994).
4. Deming W. E., Out of the Crisis (Cambridge, MA: M.I.T., Center for Advanced
Engineering
Study, 1986).
5. Gitlow H. and Gitlow S., The Deming Guide to Quality and Competitive Position
(Englewood Cliffs, NJ: Prentice Hall, 1987).
6. Gitlow, H. and PMI, Planning for Quality, Productivity and Competitive Position
(Homewood, IL: Richard D. Irwin, pp. 83–89, 1990).
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PEER EVALUATION FORM
Task
No.
Full name
Percentage
Student’s ID
complete
1
Pham Nhat Tan
IEIEIU16002
Pham Hoang Viet
IELSIU16115
Pham Le Bach Hop
IELSIU16028
Dinh The Long
IEIEIU16047
Part II, III, IV
Nguyen Hoai Nghia
IELSIU16003
Le Thi Kim Ngan
IELSIU16017
Part V: analyze 100
phase + improve
phase + control
phase
Part V: define
100
phase + measure
phase + PPT
2
3
4
5
6
Find casestudy
100
+ collect data +
Part I+ PPT
Part VI:
100
SixSigma in
QM + Six
Sigma in WH
Part VI:
100
SS in
Transportation +
Optimal
algorithms
100
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