Lecture 3
Statistical Process
Control (SPC)
Data collection for Six SigmaData are simply facts and figures without context or interpretation.Information refers to useful or meaningful patterns found in the data.Knowledge represents information of sufficient quality and/or quantity that actions can be taken based on the information.If data are not collected and used wisely, their vary existence can lead to activities that are ineffective and possibly even counterproductive.An organization collects data & reacts whenever an out-of-specification condition occurs.
“Common cause” & “ special cause” variation
There are two causes of process variations:
1) Common cause variation: This variation is due to the process only. It may not tell you whether the process meets the needs of the customer unless it is compared with the specification. This can be improved by focusing on the process.
2) Special cause variation: This variation is due the individual employee, if the point is beyond specification limits. In this case the focus should be about what happened relative to the individual employee as though it were a “special” condition.
Attribute versus Variable Data
Attribute data: It is a data with yes or no decision such as:whether an iten passed or failed a testpass/fail, go/no go gaging, true/false, accept/reject. There are no quantifiable values
Variable data: are related to measurements with quantifiable values such as:Diameter of a part which has been machinedlength or thickness of the machined part
The success of Six SigmaThe success of Six Sigma depends upon knowing the difference between special & common cause variations and how the organization reacts to the data.If the management focuses on wrong cause of variation, it can lead to waste of time (firefighting).It can also effect employee motivation & morale.Reacting to one data point that do not meet the specification limit can be counterproductive and very expensive.Do not use “firefighting” actions just because the data point is out of specification limits. It must first be determined whether the condition is common or special cause.
Example of variability due to common causeControl limits are calculated from the sample data.There are no data points outside the control limits therefore there are no special causes within the data.The source of variation in this case is “common cause” due to process.
Type of firefighting done by management before evaluating the cause of variabilityProduction supervisors might constantly review production output by employee, machine, product line, work shift etc.An administrative assistant’s daily output & memo’s may be monitored.The average time per call may be monitored in a call center.The efficiency of computer programmers may be monitored by tracking “lines of code produced per day”.
All of these actions would be a waste of time if the cause of variability is “common cause” and due to the process rather than individu ...
C O N T R O L L P R E S E N T A T I O Nوديع المخلافي
The document describes the DMAIC process for problem solving and process improvement. It consists of 5 phases: Define, Measure, Analyze, Improve, and Control. The Control phase involves implementing process controls, monitoring performance with tools like statistical process control (SPC) charts, and ensuring defects do not recur. The objective is to prevent problems and their root causes from reoccurring by documenting results, implementing controls, and selecting the appropriate control tools and activities based on the solution developed in prior phases.
I wrote this eBook for a software client based on the appropriate persona, available technical materials and interviews with internal subject matter experts. The client used this eBook for their content marketing lead generation campaigns targeted to international manufacturers.
Statistical Process Control (SPC) is a method of using statistical analysis to monitor and control a process. SPC helps determine whether a process is stable or unpredictable by comparing data to control limits on charts. There are control charts for variables (data that can be measured numerically) and attributes (data classified into categories). The document discusses types of control charts like p charts for proportions and u charts for defects per unit. It also covers process capability indices, which measure how well a process produces outputs within specifications. The goal of SPC is to detect non-routine variations and make processes as consistent as possible through continuous improvement.
process monitoring (statistical process control)Bindutesh Saner
Statistical Process Control (SPC) is an industry
standard methodology for measuring and controlling quality during
the manufacturing process. Attribute data (measurements)
is collected from products as they are being produced. By
establishing upper and lower control limits, variations in the
process can be detected before they result in defective product,
entirely eliminating the need for final inspection.
This document discusses quality management techniques. It provides an overview of six sigma quality methodology, which establishes quality standards and goals for products. It also discusses using tools like control charts, check sheets, Pareto charts, scatter plots and Ishikawa diagrams to measure quality, identify issues, and determine corrective actions. The document lists several quality management tools in detail and provides additional related quality management topics and resources.
The document presents information on control charts including what they are, their purpose and advantages, types of control charts, and how to construct and interpret them. Control charts are graphical representations that detect variations in a production process and warn if quality characteristics depart from specified tolerance limits. The main types discussed are X-bar and R-bar charts, with X-bar charts showing changes in the process average and R-bar charts controlling process variability. A case study example on using control charts in the hospitality industry is also included.
This document provides information on selecting appropriate statistical process control charts and implementing statistical process control. It discusses different types of control charts for variable and attribute data, factors to consider when selecting control charts such as the type of data and subgroup size. It also covers collecting and sampling data, calculating control limits, detecting special causes or assignable causes from control charts, and determining sampling frequency. The goal of statistical process control is to monitor process variation and detect when a process is out of control through the use of control charts, which plot process data over time and can indicate the presence of special causes of variation.
C O N T R O L L P R E S E N T A T I O Nوديع المخلافي
The document describes the DMAIC process for problem solving and process improvement. It consists of 5 phases: Define, Measure, Analyze, Improve, and Control. The Control phase involves implementing process controls, monitoring performance with tools like statistical process control (SPC) charts, and ensuring defects do not recur. The objective is to prevent problems and their root causes from reoccurring by documenting results, implementing controls, and selecting the appropriate control tools and activities based on the solution developed in prior phases.
I wrote this eBook for a software client based on the appropriate persona, available technical materials and interviews with internal subject matter experts. The client used this eBook for their content marketing lead generation campaigns targeted to international manufacturers.
Statistical Process Control (SPC) is a method of using statistical analysis to monitor and control a process. SPC helps determine whether a process is stable or unpredictable by comparing data to control limits on charts. There are control charts for variables (data that can be measured numerically) and attributes (data classified into categories). The document discusses types of control charts like p charts for proportions and u charts for defects per unit. It also covers process capability indices, which measure how well a process produces outputs within specifications. The goal of SPC is to detect non-routine variations and make processes as consistent as possible through continuous improvement.
process monitoring (statistical process control)Bindutesh Saner
Statistical Process Control (SPC) is an industry
standard methodology for measuring and controlling quality during
the manufacturing process. Attribute data (measurements)
is collected from products as they are being produced. By
establishing upper and lower control limits, variations in the
process can be detected before they result in defective product,
entirely eliminating the need for final inspection.
This document discusses quality management techniques. It provides an overview of six sigma quality methodology, which establishes quality standards and goals for products. It also discusses using tools like control charts, check sheets, Pareto charts, scatter plots and Ishikawa diagrams to measure quality, identify issues, and determine corrective actions. The document lists several quality management tools in detail and provides additional related quality management topics and resources.
The document presents information on control charts including what they are, their purpose and advantages, types of control charts, and how to construct and interpret them. Control charts are graphical representations that detect variations in a production process and warn if quality characteristics depart from specified tolerance limits. The main types discussed are X-bar and R-bar charts, with X-bar charts showing changes in the process average and R-bar charts controlling process variability. A case study example on using control charts in the hospitality industry is also included.
This document provides information on selecting appropriate statistical process control charts and implementing statistical process control. It discusses different types of control charts for variable and attribute data, factors to consider when selecting control charts such as the type of data and subgroup size. It also covers collecting and sampling data, calculating control limits, detecting special causes or assignable causes from control charts, and determining sampling frequency. The goal of statistical process control is to monitor process variation and detect when a process is out of control through the use of control charts, which plot process data over time and can indicate the presence of special causes of variation.
Histogram, Pareto Diagram, Ishikawa Diagram, and Control ChartNicola Ergo
The document provides information on various quality control tools including histograms, Pareto diagrams, Ishikawa diagrams, and control charts. Histograms show the distribution of numerical data by frequency. Pareto diagrams highlight the most important factors by showing variables in descending order. Ishikawa diagrams show causes of a problem in a branching diagram format. Control charts graph process data over time to determine if a process is stable or unpredictable through the use of control limits.
This document provides an overview and examples of quality management systems. It discusses implementing a quality assurance process to reduce defects and costs. It recommends keeping documentation and processes simple using visual diagrams. Several quality management tools are described, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Links are provided to download additional quality management resources.
Seven tools of quality control.slideshareraiaryan448
7 tools of quality control help identify potential problem root cause and then target them for improvements and process optimization. These are widely used in all kind of manufacturing industries along with service industry as well.
This document discusses 5S in quality management. It provides definitions and explanations of the 5S steps - Sort, Straighten, Shine, Standardize, and Sustain. It also lists and briefly describes several quality management tools including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and others. Additional related topics are listed for download.
This document discusses quality management in manufacturing. It provides definitions of quality management systems and how they can help identify potential quality issues. It also lists several quality management tools like check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms that can be used to monitor quality. Finally it provides some additional related topics in quality management in manufacturing that can be downloaded as PDFs.
This document provides information about quality management certificates, including the contents and curriculum of a quality management certificate program. The certificate program covers quality tools, quality management, and six sigma quality improvement methods. It is designed to provide skills at the green belt level and prepare students for the ASQ six sigma certification. The document also lists several quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms.
The document discusses 7 quality management tools that are commonly used in quality control processes. It provides descriptions of each tool, including cause and effect diagrams, flowcharts, checksheets, Pareto diagrams, histograms, control charts, and scatter diagrams. For each tool, it explains what the tool is used for and how it can help identify issues, optimize processes, ensure consistency, prioritize problems, analyze distributions, determine if a process is stable/predictable, and determine relationships between variables. It also includes more detailed explanations and examples of checksheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms as specific quality management tools.
This document summarizes key concepts in quality control and statistical process control. It discusses total quality management, the Malcolm Baldridge National Quality Award criteria, ISO 9000 standards, and Six Sigma methodology. It also describes different types of control charts used in statistical process control, including x-bar, R, p, and np charts. Control charts help determine whether process variation is due to common or assignable causes by comparing output to control limits. Interpreting point patterns on control charts indicates whether a process is in statistical control.
The document discusses the purpose of quality management systems. It defines quality management as a concept used throughout a business to improve product quality by focusing on continuous improvement and high standards at all stages of production. The primary goal of a quality management system is to improve customer satisfaction, increase sales, and further the goodwill of a business by identifying waste and inefficiencies to reduce costs. The document also provides examples of common quality management tools like check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms.
The document provides information about the Asian Institute of Quality Management (AIQM), including that it is run by experts with backgrounds in process excellence training and consultancy. AIQM conducts Lean Six Sigma, TQM, and other quality management training and consultancy services in several countries. The document also lists several quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It provides brief descriptions of each tool.
1. The document presents an overview of seven quality control tools: Pareto diagram, stratification, scatter diagram, cause and effect diagram, histogram, check sheet, and control chart.
2. It describes each tool, including how it is used and the results that can be obtained from its use. For example, a Pareto diagram is used to identify problems and their causes, while a control chart examines whether a process is stable or needs adjustment.
3. Implementing these quality control tools is part of establishing a quality program that continuously improves processes through reducing variability, identifying issues, and taking corrective actions.
This document discusses quality management issues and provides resources on the topic. It begins by outlining common quality management issues organizations may face and provides questions to help assess an organization's quality management processes. It then discusses specific issues in more depth, including nurturing a quality culture, assessing metrics, integrating disparate quality systems, handling increasing data volumes, and closing the quality loop. The document also introduces several quality management tools, such as check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It concludes by listing additional quality management topics.
Statistical Process Control & Control ChartShekhar Verma
Statistical Process Control (SPC) uses control charts to monitor processes over time and identify sources of variation. Control charts graph key data metrics and establish control limits to determine whether the process is in a state of statistical control or if special causes are present. The benefits of SPC include early detection of quality issues, reduced waste, and improved process cycle times and customer satisfaction through a diminished likelihood of rework.
This document provides an overview of statistical process control (SPC) and design of experiments (DOE). It defines SPC as monitoring production processes to prevent poor quality using techniques like control charts. DOE is presented as a systematic approach to engineering problem solving using planned experiments and statistical analysis. Key aspects of both SPC and DOE are described, including control chart types, variables in experimentation, steps in planning a DOE, and Taguchi methods which aim to optimize robustness through orthogonal array experiments. The document serves as an introduction to these important quality control and improvement tools.
Control charts are a statistical tool used to determine if a process is in or out of control. There are two main types of control charts: variable control charts which deal with measurable items and attribute control charts which factor in quality attributes. Control charts help improve processes by making defects visible and determining what adjustments are needed. They are calculated by finding the average, upper control limit, and lower control limit of a sample data set and plotting the points to check if they fall within the control limits.
Control charts are a statistical tool used to determine if a process is in or out of control. There are two main types of control charts: variable control charts which deal with measurable items and attribute control charts which factor in quality attributes. Control charts help improve processes by making defects visible and determining what adjustments are needed. They are calculated by finding the average, upper control limit, and lower control limit of a sample data set and plotting the points on a chart.
Control charts are a statistical tool used to determine if a process is in or out of control. There are two main types of control charts: variable control charts which deal with measurable items and attribute control charts which factor in quality attributes. Control charts help improve processes by making defects visible and determining what adjustments are needed. They are calculated by finding the average, upper control limit, and lower control limit of a sample data set and plotting the points on a chart.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
An investment in quality management systems (QMS) software can help organizations achieve both superior quality and reduced costs by enabling automated, interactive quality control processes tailored to each organization. TrackWise by Sparta Systems is an enterprise QMS that optimizes quality, ensures compliance and reduces costs and risks across industries. The document then discusses several common quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It concludes with additional topics related to quality management systems.
This document discusses quality management KPIs (key performance indicators) that factories can use to measure and improve product quality. It provides examples of important KPIs like customer complaints, defect percentage levels, and right first time quality. The document also lists several quality management tools that can be used, such as control charts, Pareto charts, scatter plots, and histograms. Additional links are provided for free quality management resources.
PART B Please response to these two original posts below. Wh.docxsmile790243
PART B
Please response to these two original posts below. When
responding to these posts, please either expand the
thought, add additional insights, or respectfully disagree
and explain why. Remember that we are after reasons
and arguments, and not simply the statement of
opinions.
Original Post 1
Are human lives intrinsically valuable? If so, in virtue of what? (Is
it our uniqueness, perhaps, or our autonomy, or something else?)
To begin, I would like to remind us that being intrinsically valuable
means having values for just being us and nothing else. I believe
that human lives are intrinsically valuable in virtue of our
uniqueness. As a bio nerd, I would like to state the fact that there
are a lot of crossover events during meiosis, which create trillions
of different DNA combinations. Hence, from a biological
standpoint, without considering other aspects, being you is
already valuable because you are that one sperm that won the
race and got fertilized. On a larger scale, there are hardly two
people whose look and behaviors are the same in the same
family, unless they are identical twins. However, identical twins
still act differently and have differences (such as fingerprints).
Since we are raised in different families, we are taught different
things and have different cultures. In general, we all have
different genetic information, appearances, personalities, senses
of humor, ambitions, talents, interests and life experiences. These
characteristics make up our “unique individual value” and make
us so unique and irreplaceable.
I would also love to discuss how our diversities enrich and
contribute to society, but that would be a talk about our extrinsic
values.
Original Post 2
Are human lives intrinsically valuable? If so, in virtue of what? (Is
it our uniqueness, perhaps, or our autonomy, or something else?)
I believe that human lives are intrinsically valuable due to a
number of reasons. Firstly, human lives aren’t replaceable. You
can’t replace a human being with another just like you can
replace a broken laptop with brand new one. Part of the reason
why we tend to think this way is that we were nurtured with the
notion that there is, indeed, a special value to human life. This
could be in virtue of our uniqueness-- the fact that we are
sentient and capable of complex thoughts and emotions
separates us from any other species on this planet. From a
scientific standpoint, this is also one of the reasons as to why
humans became the dominant species in today’s age.
Moreover, human lives aren’t disposable. I think this is largely due
to us humans having the ability to empathize with others. We
understand that it’s morally inappropriate to take the life of
another individual even if they’re complete strangers because
they’re another human being like us who has their own thoughts,
values, memories, and stories. In a way, we have a strong
emotional connection to our own species. As .
Part C Developing Your Design SolutionThe Production Cycle.docxsmile790243
Part C Developing Your Design
Solution
The Production Cycle
Within the four stages of the design workflow there are two distinct parts.
The first three stages, as presented in Part B of this book, were described
as ‘The Hidden Thinking’ stages, as they are concerned with undertaking
the crucial behind-the-scenes preparatory work. You may have completed
them in terms of working through the book’s contents, but in visualisation
projects they will continue to command your attention, even if that is
reduced to a background concern.
You have now reached the second distinct part of the workflow which
involves developing your design solution. This stage follows a production
cycle, commencing with rationalising design ideas and moving through to
the development of a final solution.
The term cycle is appropriate to describe this stage as there are many loops
of iteration as you evolve rapidly between conceptual, practical and
technical thinking. The inevitability of this iterative cycle is, in large part,
again due to the nature of this pursuit being more about optimisation rather
than an expectation of achieving that elusive notion of perfection. Trade-
offs, compromises, and restrictions are omnipresent as you juggle ambition
and necessary pragmatism.
How you undertake this stage will differ considerably depending on the
nature of your task. The creation of a relatively simple, single chart to be
slotted into a report probably will not require the same rigour of a formal
production cycle that the development of a vast interactive visualisation to
be used by the public would demand. This is merely an outline of the most
you will need to do – you should edit, adapt and participate the steps to fit
with your context.
There are several discrete steps involved in this production cycle:
Conceiving ideas across the five layers of visualisation design.
Wireframing and storyboarding designs.
Developing prototypes or mock-up versions.
219
Testing.
Refining and completing.
Launching the solution.
Naturally, the specific approach for developing your design solution (from
prototyping through to launching) will vary hugely, depending particularly
on your skills and resources: it might be an Excel chart, or a Tableau
dashboard, an infographic created using Adobe Illustrator, or a web-based
interactive built with the D3.js library. As I have explained in the book’s
introduction, I’m not going to attempt to cover the myriad ways of
implementing a solution; that would be impossible to achieve as each task
and tool would require different instructions.
For the scope of this book, I am focusing on taking you through the first
two steps of this cycle – conceiving ideas and wireframing/storyboarding.
There are parallels here with the distinctions between architecture (design)
and engineering (execution) – I’m effectively chaperoning you through to
the conclusion of your design thinking.
To fulfil this, Part C presents a detailed breakdown of the many design
.
More Related Content
Similar to Lecture 3 Statistical ProcessControl (SPC).docx
Histogram, Pareto Diagram, Ishikawa Diagram, and Control ChartNicola Ergo
The document provides information on various quality control tools including histograms, Pareto diagrams, Ishikawa diagrams, and control charts. Histograms show the distribution of numerical data by frequency. Pareto diagrams highlight the most important factors by showing variables in descending order. Ishikawa diagrams show causes of a problem in a branching diagram format. Control charts graph process data over time to determine if a process is stable or unpredictable through the use of control limits.
This document provides an overview and examples of quality management systems. It discusses implementing a quality assurance process to reduce defects and costs. It recommends keeping documentation and processes simple using visual diagrams. Several quality management tools are described, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Links are provided to download additional quality management resources.
Seven tools of quality control.slideshareraiaryan448
7 tools of quality control help identify potential problem root cause and then target them for improvements and process optimization. These are widely used in all kind of manufacturing industries along with service industry as well.
This document discusses 5S in quality management. It provides definitions and explanations of the 5S steps - Sort, Straighten, Shine, Standardize, and Sustain. It also lists and briefly describes several quality management tools including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and others. Additional related topics are listed for download.
This document discusses quality management in manufacturing. It provides definitions of quality management systems and how they can help identify potential quality issues. It also lists several quality management tools like check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms that can be used to monitor quality. Finally it provides some additional related topics in quality management in manufacturing that can be downloaded as PDFs.
This document provides information about quality management certificates, including the contents and curriculum of a quality management certificate program. The certificate program covers quality tools, quality management, and six sigma quality improvement methods. It is designed to provide skills at the green belt level and prepare students for the ASQ six sigma certification. The document also lists several quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms.
The document discusses 7 quality management tools that are commonly used in quality control processes. It provides descriptions of each tool, including cause and effect diagrams, flowcharts, checksheets, Pareto diagrams, histograms, control charts, and scatter diagrams. For each tool, it explains what the tool is used for and how it can help identify issues, optimize processes, ensure consistency, prioritize problems, analyze distributions, determine if a process is stable/predictable, and determine relationships between variables. It also includes more detailed explanations and examples of checksheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms as specific quality management tools.
This document summarizes key concepts in quality control and statistical process control. It discusses total quality management, the Malcolm Baldridge National Quality Award criteria, ISO 9000 standards, and Six Sigma methodology. It also describes different types of control charts used in statistical process control, including x-bar, R, p, and np charts. Control charts help determine whether process variation is due to common or assignable causes by comparing output to control limits. Interpreting point patterns on control charts indicates whether a process is in statistical control.
The document discusses the purpose of quality management systems. It defines quality management as a concept used throughout a business to improve product quality by focusing on continuous improvement and high standards at all stages of production. The primary goal of a quality management system is to improve customer satisfaction, increase sales, and further the goodwill of a business by identifying waste and inefficiencies to reduce costs. The document also provides examples of common quality management tools like check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms.
The document provides information about the Asian Institute of Quality Management (AIQM), including that it is run by experts with backgrounds in process excellence training and consultancy. AIQM conducts Lean Six Sigma, TQM, and other quality management training and consultancy services in several countries. The document also lists several quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It provides brief descriptions of each tool.
1. The document presents an overview of seven quality control tools: Pareto diagram, stratification, scatter diagram, cause and effect diagram, histogram, check sheet, and control chart.
2. It describes each tool, including how it is used and the results that can be obtained from its use. For example, a Pareto diagram is used to identify problems and their causes, while a control chart examines whether a process is stable or needs adjustment.
3. Implementing these quality control tools is part of establishing a quality program that continuously improves processes through reducing variability, identifying issues, and taking corrective actions.
This document discusses quality management issues and provides resources on the topic. It begins by outlining common quality management issues organizations may face and provides questions to help assess an organization's quality management processes. It then discusses specific issues in more depth, including nurturing a quality culture, assessing metrics, integrating disparate quality systems, handling increasing data volumes, and closing the quality loop. The document also introduces several quality management tools, such as check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It concludes by listing additional quality management topics.
Statistical Process Control & Control ChartShekhar Verma
Statistical Process Control (SPC) uses control charts to monitor processes over time and identify sources of variation. Control charts graph key data metrics and establish control limits to determine whether the process is in a state of statistical control or if special causes are present. The benefits of SPC include early detection of quality issues, reduced waste, and improved process cycle times and customer satisfaction through a diminished likelihood of rework.
This document provides an overview of statistical process control (SPC) and design of experiments (DOE). It defines SPC as monitoring production processes to prevent poor quality using techniques like control charts. DOE is presented as a systematic approach to engineering problem solving using planned experiments and statistical analysis. Key aspects of both SPC and DOE are described, including control chart types, variables in experimentation, steps in planning a DOE, and Taguchi methods which aim to optimize robustness through orthogonal array experiments. The document serves as an introduction to these important quality control and improvement tools.
Control charts are a statistical tool used to determine if a process is in or out of control. There are two main types of control charts: variable control charts which deal with measurable items and attribute control charts which factor in quality attributes. Control charts help improve processes by making defects visible and determining what adjustments are needed. They are calculated by finding the average, upper control limit, and lower control limit of a sample data set and plotting the points to check if they fall within the control limits.
Control charts are a statistical tool used to determine if a process is in or out of control. There are two main types of control charts: variable control charts which deal with measurable items and attribute control charts which factor in quality attributes. Control charts help improve processes by making defects visible and determining what adjustments are needed. They are calculated by finding the average, upper control limit, and lower control limit of a sample data set and plotting the points on a chart.
Control charts are a statistical tool used to determine if a process is in or out of control. There are two main types of control charts: variable control charts which deal with measurable items and attribute control charts which factor in quality attributes. Control charts help improve processes by making defects visible and determining what adjustments are needed. They are calculated by finding the average, upper control limit, and lower control limit of a sample data set and plotting the points on a chart.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
An investment in quality management systems (QMS) software can help organizations achieve both superior quality and reduced costs by enabling automated, interactive quality control processes tailored to each organization. TrackWise by Sparta Systems is an enterprise QMS that optimizes quality, ensures compliance and reduces costs and risks across industries. The document then discusses several common quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It concludes with additional topics related to quality management systems.
This document discusses quality management KPIs (key performance indicators) that factories can use to measure and improve product quality. It provides examples of important KPIs like customer complaints, defect percentage levels, and right first time quality. The document also lists several quality management tools that can be used, such as control charts, Pareto charts, scatter plots, and histograms. Additional links are provided for free quality management resources.
Similar to Lecture 3 Statistical ProcessControl (SPC).docx (20)
PART B Please response to these two original posts below. Wh.docxsmile790243
PART B
Please response to these two original posts below. When
responding to these posts, please either expand the
thought, add additional insights, or respectfully disagree
and explain why. Remember that we are after reasons
and arguments, and not simply the statement of
opinions.
Original Post 1
Are human lives intrinsically valuable? If so, in virtue of what? (Is
it our uniqueness, perhaps, or our autonomy, or something else?)
To begin, I would like to remind us that being intrinsically valuable
means having values for just being us and nothing else. I believe
that human lives are intrinsically valuable in virtue of our
uniqueness. As a bio nerd, I would like to state the fact that there
are a lot of crossover events during meiosis, which create trillions
of different DNA combinations. Hence, from a biological
standpoint, without considering other aspects, being you is
already valuable because you are that one sperm that won the
race and got fertilized. On a larger scale, there are hardly two
people whose look and behaviors are the same in the same
family, unless they are identical twins. However, identical twins
still act differently and have differences (such as fingerprints).
Since we are raised in different families, we are taught different
things and have different cultures. In general, we all have
different genetic information, appearances, personalities, senses
of humor, ambitions, talents, interests and life experiences. These
characteristics make up our “unique individual value” and make
us so unique and irreplaceable.
I would also love to discuss how our diversities enrich and
contribute to society, but that would be a talk about our extrinsic
values.
Original Post 2
Are human lives intrinsically valuable? If so, in virtue of what? (Is
it our uniqueness, perhaps, or our autonomy, or something else?)
I believe that human lives are intrinsically valuable due to a
number of reasons. Firstly, human lives aren’t replaceable. You
can’t replace a human being with another just like you can
replace a broken laptop with brand new one. Part of the reason
why we tend to think this way is that we were nurtured with the
notion that there is, indeed, a special value to human life. This
could be in virtue of our uniqueness-- the fact that we are
sentient and capable of complex thoughts and emotions
separates us from any other species on this planet. From a
scientific standpoint, this is also one of the reasons as to why
humans became the dominant species in today’s age.
Moreover, human lives aren’t disposable. I think this is largely due
to us humans having the ability to empathize with others. We
understand that it’s morally inappropriate to take the life of
another individual even if they’re complete strangers because
they’re another human being like us who has their own thoughts,
values, memories, and stories. In a way, we have a strong
emotional connection to our own species. As .
Part C Developing Your Design SolutionThe Production Cycle.docxsmile790243
Part C Developing Your Design
Solution
The Production Cycle
Within the four stages of the design workflow there are two distinct parts.
The first three stages, as presented in Part B of this book, were described
as ‘The Hidden Thinking’ stages, as they are concerned with undertaking
the crucial behind-the-scenes preparatory work. You may have completed
them in terms of working through the book’s contents, but in visualisation
projects they will continue to command your attention, even if that is
reduced to a background concern.
You have now reached the second distinct part of the workflow which
involves developing your design solution. This stage follows a production
cycle, commencing with rationalising design ideas and moving through to
the development of a final solution.
The term cycle is appropriate to describe this stage as there are many loops
of iteration as you evolve rapidly between conceptual, practical and
technical thinking. The inevitability of this iterative cycle is, in large part,
again due to the nature of this pursuit being more about optimisation rather
than an expectation of achieving that elusive notion of perfection. Trade-
offs, compromises, and restrictions are omnipresent as you juggle ambition
and necessary pragmatism.
How you undertake this stage will differ considerably depending on the
nature of your task. The creation of a relatively simple, single chart to be
slotted into a report probably will not require the same rigour of a formal
production cycle that the development of a vast interactive visualisation to
be used by the public would demand. This is merely an outline of the most
you will need to do – you should edit, adapt and participate the steps to fit
with your context.
There are several discrete steps involved in this production cycle:
Conceiving ideas across the five layers of visualisation design.
Wireframing and storyboarding designs.
Developing prototypes or mock-up versions.
219
Testing.
Refining and completing.
Launching the solution.
Naturally, the specific approach for developing your design solution (from
prototyping through to launching) will vary hugely, depending particularly
on your skills and resources: it might be an Excel chart, or a Tableau
dashboard, an infographic created using Adobe Illustrator, or a web-based
interactive built with the D3.js library. As I have explained in the book’s
introduction, I’m not going to attempt to cover the myriad ways of
implementing a solution; that would be impossible to achieve as each task
and tool would require different instructions.
For the scope of this book, I am focusing on taking you through the first
two steps of this cycle – conceiving ideas and wireframing/storyboarding.
There are parallels here with the distinctions between architecture (design)
and engineering (execution) – I’m effectively chaperoning you through to
the conclusion of your design thinking.
To fulfil this, Part C presents a detailed breakdown of the many design
.
PART A You will create a media piece based around the theme of a.docxsmile790243
PART A:
You will create a media piece based around the theme of “alternative facts.
Fake News:
Create a
series of 3
short, “fake news” articles or news videos. They should follow a specific theme. Make sure to have a clear understanding of WHY your fake news is being created (fake news is used by people, groups, companies, etc to convince an unsuspecting audience of something. It’s supposed to seem real, but the motivation behind it is to deceive. As part of this option, consider what your motivations are for your deception).
Part A: should be around 750 words for written tasks (or 250 for each 3 part task)
PART B:
The focus for this assignment is to demonstrate a
clear understanding of media conventions
, as well as
purpose
and
audience
. Therefore, along with your media product, you’ll also be required to submit a short
reflection
detailing why you created your product and for whom it was intended. You must discuss and analyze the elements within your media product (including why & how you used the persuasive techniques of ethos, logos and pathos) as well as the other elements of media you used and why.
.
Part 4. Implications to Nursing Practice & Implication to Patien.docxsmile790243
Part 4. Implications to Nursing Practice & Implication to Patient Outcomes
Provide a paragraph summary addressing the topics implications to nursing practice and patient outcomes. This section is NOT another review of the literature or introduction of new topics related to the PICOT question.
You may find if helpful to begin each topic with -
Nurses need to know …
Important patient outcomes include …
Example
– please note this is an older previous students work and so some references are older than 5 years.
Be sure to provide the PICOT question to begin this post.
PICOT Question:
P=Patient Population
I=Intervention
C=Comparison
O=Outcome
T=Time (duration):
In patients in the hospital, (P)
how does frequently provided patient hand washing (I)
compared with patient initiated hand washing (C)
affect hospital acquired infection (O)
within the hospital stay (T)
Implications to Nursing Practice & Patient Outcomes
Nurses need to know that they play a significant role in the reduction of hospital acquired infection by ensuring by health care workers and patients wash hands since nurses have the most interactions with patients. Implementing hand hygiene protocol with patients can enhance awareness and decrease healthcare associated infection (HAI). Both nurses and patients need to know that HAI is associated with increased morbidity and mortality as well cost of treatment and length of hospital stay. Nurses and patients also need to know that most HAI is preventable. Gujral (2015) notes that proper hand hygiene is the single most important, simplest, and least expensive means of reducing prevalence of HAI and the spread of antimicrobial resistance. Nurse and patient hand washing plays a vital role in decreasing healthcare costs and infections in all settings.
References
Gujral, H. (2015.) Survey shows importance of hand washing for infection prevention. American Nurse Today, 10 (10), 20. Retrieved from hEp://www.nursingworld.org/AmericanNurseToday
.
PART AHepatitis C is a chronic liver infection that can be e.docxsmile790243
PART A
Hepatitis C is a chronic liver infection that can be either silent (with no noticeable symptoms) or debilitating. Either way, 80% of infected persons experience continuing liver destruction. Chronic hepatitis C infection is the leading cause of liver transplants in the United States. The virus that causes it is blood borne, and therefore patients who undergo frequent procedures involving transfer of blood are particularly susceptible to infection. Kidney dialysis patients belong to this group. In 2008, a for-profit hemodialysis facility in New York was shut down after nine of its patients were confirmed as having become infected with hepatitis C while undergoing hemodialysis treatments there between 2001 and 2008.
When the investigation was conducted in 2008, investigators found that 20 of the facility’s 162 patients had been documented with hepatitis C infection at the time they began their association with the clinic. All the current patients were then offered hepatitis C testing, to determine how many had acquired hepatitis C during the time they were receiving treatment at the clinic. They were considered positive if enzyme-linked immunosorbent assay (ELISA) tests showed the presence of antibodies to the hepatitis C virus.
Health officials did not test the workers at the hemodialysis facility for hepatitis C because they did not view them as likely sources of the nine new infections. Why not?
Why do you think patients were tested for antibody to the virus instead of for the presence of the virus itself?
Ref.: Cowan, M. K. (2014) (4th Ed.). Microbiology: A Systems Approach, McGraw Hill
PART B
Summary:
Directions for the students: There are 4 essay questions. Please be sure to complete all of them with thorough substantive responses. Current APA Citations are required for all responses.
1. Precisely what is microbial death?
2. Why does a population of microbes not die instantaneously when exposed to an antimicrobial agent?
3. Explain what is wrong with this statement: “Prior to vaccination, the patient’s skin was sterilized with alcohol.” What would be a more correct wording?
4. Conduct additional research on the use of triclosan and other chemical agents in antimicrobial products today. Develop an opinion on whether this process should continue, providing evidence and citations to support your stance.
.
Part A post your answer to the following question1. How m.docxsmile790243
Potential negative reactions from others to an adolescent questioning their sexual identity or gender role could negatively impact their social environment, behavior, and self-esteem. As social workers, we can play a role in creating a supportive environment for these adolescents by educating families and communities, advocating for inclusive policies, and providing counseling and resources to help adolescents accept themselves and develop coping strategies.
PART BPlease response to these two original posts below..docxsmile790243
PART B
Please response to these two original posts below. When responding to
these posts, please either expand the thought, add additional insights, or
respectfully disagree and explain why. Remember that we are after reasons
and arguments, and not simply the statement of opinions.
Original Post 1
"What is moral relativism? Why might people be attracted to it? Is
it plausible?"
First of all, moral relativism is the view that moral truths are
subjective and depend on each individual's standpoints. Based
on this, everyone's moral view is legitimate. This can be attracted
because it sounds liberating and there is no need to argue for a
particular position. Moral relativism seems convincing in some
cases. For example, some people are okay with giving money to
homeless people, thinking that it's good to provide for the people
in need. Some people, on the other hand, claim that they can
work to satisfy their own needs. Moral relativism works well in
these cases because they all seem legitimate. However, there are
cases that moral relativism does not seem reasonable. For
example, child sacrifice in some cultures seems cruel and
uncivilized to most people. Hence, moral relativism is not
absolutely true.
Original Post 2
“Is your death bad for you, specifically, or only (at most) for others? Why
might someone claim that it isn’t bad for you?”
I'd start off by acknowledging what the two ancient philosophers,
Lucretius and Epicurus, outlined about death. They made the
point that death isn't necessarily bad for you since no suffering
takes place and that you yourself don't realize your own death. In
this way, one could make the claim that death isn't intrinsically
bad for you.
Another perspective I wanted to add was the influence of death
(both on you and others around you). Specifically, the event of
death itself may not be bad for you, but the idea of impending
death could impact one's life. Some may live freely, totally care-
free, accepting of death and enjoy life in the moment. Others may
be frightened by the idea of death that they live in constant fear
and hence death causing their mental health to take its toll. In
this way, I'd argue that death could, in fact, be bad for you. One
common reason for being afraid of death is the fear of being
forgotten. Not to mention the death of an individual certainly
affects others; death doesn't affect one's life but also all that is
connected to it. Focusing back to the point, it's clear that the
very idea of death directly affects the concerned individual. The
fact that those who live in fear of death are looking for legacies
and footprints to leave after they leave this world is telling of how
death could be arguably bad for you before it even happens.
PART A
Pick one or more questions below and write a substantive post
with >100 words. Please try to provide evidence(s) to support
your idea(s).
Questions:
• Do we have a duty to work out whe.
Part A (50 Points)Various men and women throughout history .docxsmile790243
Part A (50 Points):
Various men and women throughout history have made important contributions to the development of statistical science. Select any one (1) individual from the list below and write a 2 page summary of their influence on statistics. Be specific in detail to explain the concepts they developed and how this advanced our understanding and application of statistics.
Florence Nightingale
Francis Galton
Thomas Bayes
Part B (50 Points):
Select any one statistical concept you learned in this course and explain how it can be applied to our understanding of the Covid-19 pandemic (2 pages). You should use a specific example and include at least one diagram to illustrate your answer.
Please note: Your work must be original and not copied directly from other sources. No citations are needed. Be sure to submit this assignment in Blackboard on the due date specified.
.
This document discusses urinary tract infections (UTIs). It begins with a matching exercise identifying structures of the urinary system. The second part addresses UTIs in more detail. It defines a UTI, discusses the microorganisms that cause UTIs and where they enter the body. It also explains common signs and symptoms of UTIs, as well as diagnostic tests and treatments. The document concludes by noting that UTIs are more common in women and describes some ways women can reduce their risk.
Part A Develop an original age-appropriate activity for your .docxsmile790243
The document describes developing two original age-appropriate activities for preschoolers. The first activity uses either Froebel's cube gift, parquetry gift, or Lincoln Logs and identifies two skills it develops. The second activity promotes the same skills but is based on the Montessori method. The summary describes each activity and notes two key differences between them.
Part 3 Social Situations2. Identify multicultural challenges th.docxsmile790243
Part 3: Social Situations
2. Identify multicultural challenges that your chosen individual may face as a recent
refugee.
• What are some of the issues that can arise for someone who has recently
immigrated to a new country?
• Explain how these multicultural challenges could impact your chosen individual’s
four areas of development?
3. Suggest plans of action or resources that you feel should be provided to this family to
assist them in proper develop
Part 3: Social Situations
• Proposal paper which identifies multicultural challenges that your chosen individual may face as a recent refugee.
• Suggested plan of action and/or resources which should be implemented to address the multicultural challenges.
• 2-3 Pages in length
• APA Formatting
• Submission will be checked for plagiaris
.
Part A (1000 words) Annotated Bibliography - Create an annota.docxsmile790243
Part A
(1000 words): Annotated Bibliography - Create an annotated bibliography that focuses on ONE particular aspect of current Software Engineering that face a world with different cultural standards. At least seven (7) peer-reviewed articles must be used for this exercise.
Part B
(3000 words):
Research Report
- Write a report of the analysis and synthesis using the
(Part A
) foundational
Annotated Bibliography
.
Part C (500 words): Why is it important to try to minimize complexity in a software system.
Part D (500 words): What are the advantages and disadvantages to companies that are developing software products that use cloud servers to support their development process?
Part E (500 words): Explain why each microservice should maintain its own data. Explain how data in service replicas can be kept consistent?
.
Part 6 Disseminating Results Create a 5-minute, 5- to 6-sli.docxsmile790243
Part 6: Disseminating Results
Create a 5-minute, 5- to 6-slide narrated PowerPoint presentation of your Evidence-Based Project:
· Be sure to incorporate any feedback or changes from your presentation submission in Module 5.
· Explain how you would disseminate the results of your project to an audience. Provide a rationale for why you selected this dissemination strategy.
Points Range: 81 (81%) - 90 (90%)
The narrated presentation accurately and completely summarizes the evidence-based project. The narrated presentation is professional in nature and thoroughly addresses all components of the evidence-based project.
The narrated presentation accurately and clearly explains in detail how to disseminate the results of the project to an audience, citing specific and relevant examples.
The narrated presentation accurately and clearly provides a justification that details the selection of this dissemination strategy that is fully supported by specific and relevant examples.
The narrated presentation provides a complete, detailed, and specific synthesis of two outside resources related to the dissemination strategy explained. The narrated presentation fully integrates at least two outside resources and two or three course-specific resources that fully support the presentation.
Written Expression and Formatting—Paragraph Development and Organization:
Paragraphs make clear points that support well-developed ideas, flow logically, and demonstrate continuity of ideas. Sentences are carefully focused—neither long and rambling nor short and lacking substance. A clear and comprehensive purpose statement and introduction is provided which delineates all required criteria.
Points Range: 5 (5%) - 5 (5%)
Paragraphs and sentences follow writing standards for flow, continuity, and clarity.
A clear and comprehensive purpose statement, introduction, and conclusion is provided which delineates all required criteria.
Written Expression and Formatting—English Writing Standards:
Correct grammar, mechanics, and proper punctuation.
Points Range: 5 (5%) - 5 (5%)
Uses correct grammar, spelling, and punctuation with no errors.
Evidenced Based Change
Leslie Hill
Walden University
Introduction/PurposeChange is inevitable.Health care organizations need change to improve.There are challenges that need to be addressed(Baraka-Johnson et al. 2019).Challenges should be addressed using evidence-based research.These changes enhance professionalism therefore improving quality of care and quality of life.The purpose of this paper is to identify an existing problem in health care and suggest a change idea that would be effective in addressing the problem. The paper also articulates risks associated with the change process, how to distribute the change information and how to implement change successfully.
Organizational CultureThe Organization is a hospice facilityOffers end of life care for pain and symptom managementThe health care providers cu.
Part 3 Social Situations • Proposal paper which identifies multicul.docxsmile790243
Part 3: Social Situations • Proposal paper which identifies multicultural challenges that your chosen individual may face as a recent refugee. • Suggested plan of action and/or resources which should be implemented to address the multicultural challenges. • 2-3 Pages in length • APA Formatting • Submission will be checked for plagiarism
Part 3: Social Situations 2. Identify multicultural challenges that your chosen individual may face as a recent refugee. • What are some of the issues that can arise for someone who has recently immigrated to a new country? • Explain how these multicultural challenges could impact your chosen individual’s four areas of development? 3. Suggest plans of action or resources that you feel should be provided to this family to assist them in proper development.
.
Part 3 Social Situations 2. Identify multicultural challenges that .docxsmile790243
Part 3: Social Situations 2. Identify multicultural challenges that your chosen individual may face as a recent refugee. • What are some of the issues that can arise for someone who has recently immigrated to a new country? • Explain how these multicultural challenges could impact your chosen individual’s four areas of development? 3. Suggest plans of action or resources that you feel should be provided to this family to assist them in proper development.
Part 3: Social Situations • Proposal paper which identifies multicultural challenges that your chosen individual may face as a recent refugee. • Suggested plan of action and/or resources which should be implemented to address the multicultural challenges. • 2-3 Pages in length • APA Formatting • Submission will be checked for plagiarism
.
Part 2The client is a 32-year-old Hispanic American male who c.docxsmile790243
Part 2
The client is a 32-year-old Hispanic American male who came to the United States when he was in high school with his father. His mother died back in Mexico when he was in school. He presents today to the PMHNPs office for an initial appointment for complaints of depression. The client was referred by his PCP after “routine” medical work-up to rule out an organic basis for his depression. He has no other health issues except for some occasional back pain and “stiff” shoulders which he attributes to his current work as a laborer in a warehouse. the “Montgomery- Asberg Depression Rating Scale (MADRS)” and obtained a score of 51 (indicating severe depression). reports that he always felt like an outsider as he was “teased” a lot for being “black” in high school. States that he had few friends, and basically kept to himself. He also reports a remarkably diminished interest in engaging in usual activities, states that he has gained 15 pounds in the last 2 months. He is also troubled with insomnia which began about 6 months ago, but have been progressively getting worse. He does report poor concentration which he reports is getting in “trouble” at work.
· Decision #1: start Zoloft 25mg orally daily
· Which decision did you select?
· Why did you select this decision? Support your response with evidence and references to the Learning Resources.
· What were you hoping to achieve by making this decision? Support your response with evidence and references to the Learning Resources.
· Explain any difference between what you expected to achieve with Decision #1 and the results of the decision. Why were they different?
· Decision #2: Client returns to clinic in four weeks, reports a 25% decrease in symptoms but concerned over the new onset of erectile dysfunction
*add Augmentin Wellbutrin IR 150mg in the morning
· Why did you select this decision? Support y our response with evidence and references to the Learning Resources.
· What were you hoping to achieve by making this decision? Support your response with evidence and references to the Learning Resources.
· Explain any difference between what you expected to achieve with Decision #2 and the results of the decision. Why were they different?
· Decision #3: Client returns to clinic in four weeks, Client stated that depressive symptoms have decreased even more and his erectile dysfunction has abated
· Client reports that he has been feeling “jittery” and sometimes “nervous”
*change to Wellbutrin XL 150mg daily
· Why did you select this decision? Support your response with evidence and references to the Learning Resources.
· What were you hoping to achieve by making this decision? Support your response with evidence and references to the Learning Resources.
· Explain any difference between what you expected to achieve with Decision #3 and the results of the decision. Why were they different?
Explain how ethical considerations might impact your treatment plan and communication with clients.
Conclusion.
Part 2For this section of the template, focus on gathering deta.docxsmile790243
Part 2:
For this section of the template, focus on gathering details about common, specific learning disabilities. These disabilities fall under the IDEA disability categories you researched for the chart above. Review the textbook and the topic study materials and use them to complete the chart.
Learning Disability Definition Characteristics Common Assessments for Diagnosis Potential Effect on Learning and Other Areas of Life Basic Strategies for Addressing the Disability
Attention Deficit Hyperactivity Disorder (ADHD)
Auditory Processing Disorder (APD)
Dyscalculia
Dysgraphia
Dyslexia
Dysphasia/Aphasia
Dyspraxia
Language Processing Disorder (LPD)
Non-Verbal Learning Disabilities
Visual Perceptual/Visual Motor Deficit
.
Part 2 Observation Summary and Analysis • Summary paper of observat.docxsmile790243
Part 2: Observation Summary and Analysis • Summary paper of observation findings for each area of development and connection to the observed participant. • Comprehensive description of the observed participant. • Analyzed observation experience with course material to determine whetherthe participant is developmentally on track for each area of development. • 4 Pages in length • APA Formatting • Submission will be checked for plagiarism
Part 2: Observation Summary and Analysis 1. Review and implement any comments from your instructor for Part 1: Observation. 2. Describe the participant that you observed. • Share your participant’s first name (can be fictional name if participant wants to remain anonymous), age, physical attributes, and you initial impressions. 3. Analyze your observation findings for each area of development (physical, cognitive, social/emotional, and spiritual/moral). • Explain how your observations support the 3-5 bullets for each area of development that you identified in your Development Observation Guidefrom Part 1: Observation. • Explain whether or not your participant is developmentally on track for each area of development. 4. What stood out the most to you about the observation? 5. Include at least 2 credible sources
.
Part 2 Observation Summary and Analysis 1. Review and implement any.docxsmile790243
Part 2: Observation Summary and Analysis 1. Review and implement any comments from your instructor for Part 1: Observation. 2. Describe the participant that you observed. • Share your participant’s first name (can be fictional name if participant wants to remain anonymous), age, physical attributes, and you initial impressions. 3. Analyze your observation findings for each area of development (physical, cognitive, social/emotional, and spiritual/moral). • Explain how your observations support the 3-5 bullets for each area of development that you identified in your Development Observation Guidefrom Part 1: Observation. • Explain whether or not your participant is developmentally on track for each area of development. 4. What stood out the most to you about the observation? 5. Include at least 2 credible sources
Part 2: Observation Summary and Analysis • Summary paper of observation findings for each area of development and connection to the observed participant. • Comprehensive description of the observed participant. • Analyzed observation experience with course material to determine whetherthe participant is developmentally on track for each area of development. • 4-6 Pages in length • APA Formatting • Submission will be checked for plagiarism
.
Part 2Data collectionfrom your change study initiative,.docxsmile790243
Part 2:
Data collection
from your change study initiative, sample, method, display of the results of the data itself, process, and method of analysis (graphs, charts, frequency counts, descriptive statistics of the data, narrative)
Part 3: Interpretation of the results of the Data
Collection and
Analysis, address likely resistance, and provide recommendations for continuing
the study
or evaluating your change study/initiative.
.
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
1. Lecture 3
Statistical Process
Control (SPC)
Data collection for Six SigmaData are simply facts and figures
without context or interpretation.Information refers to useful or
meaningful patterns found in the data.Knowledge represents
information of sufficient quality and/or quantity that actions can
be taken based on the information.If data are not collected and
used wisely, their vary existence can lead to activities that are
ineffective and possibly even counterproductive.An
organization collects data & reacts whenever an out-of-
specification condition occurs.
“Common cause” & “ special cause” variation
There are two causes of process variations:
1) Common cause variation: This variation is due to the process
only. It may not tell you whether the process meets the needs of
the customer unless it is compared with the specification. This
can be improved by focusing on the process.
2. 2) Special cause variation: This variation is due the individual
employee, if the point is beyond specification limits. In this
case the focus should be about what happened relative to the
individual employee as though it were a “special” condition.
Attribute versus Variable Data
Attribute data: It is a data with yes or no decision such
as:whether an iten passed or failed a testpass/fail, go/no go
gaging, true/false, accept/reject. There are no quantifiable
values
Variable data: are related to measurements with quantifiable
values such as:Diameter of a part which has been
machinedlength or thickness of the machined part
The success of Six SigmaThe success of Six Sigma depends
upon knowing the difference between special & common cause
variations and how the organization reacts to the data.If the
management focuses on wrong cause of variation, it can lead to
waste of time (firefighting).It can also effect employee
motivation & morale.Reacting to one data point that do not meet
the specification limit can be counterproductive and very
expensive.Do not use “firefighting” actions just because the
data point is out of specification limits. It must first be
determined whether the condition is common or special cause.
3. Example of variability due to common causeControl limits are
calculated from the sample data.There are no data points outside
the control limits therefore there are no special causes within
the data.The source of variation in this case is “common cause”
due to process.
Type of firefighting done by management before evaluating the
cause of variabilityProduction supervisors might constantly
review production output by employee, machine, product line,
work shift etc.An administrative assistant’s daily output &
memo’s may be monitored.The average time per call may be
monitored in a call center.The efficiency of computer
programmers may be monitored by tracking “lines of code
produced per day”.
All of these actions would be a waste of time if the cause of
variability is “common cause” and due to the process rather
than individual employees who have no control over it.
Goals for Six Sigma projectsThe success of Six Sigma depends
upon how goals are set & any boundaries that are set relative to
meeting those objectives.Arbitrary goals that are set for
employees can be counterproductive and costly, when the
outcome is beyond the employee’s control. If management
extends the scope of what can be done by employees to fix
problems, real productivity improvement can result.
4. Organization sometimes can be trapped into “declaring victory”
with one metric at the expense of another metric.
Strategy for Six Sigma successIt should deal with numbers,
information & knowledge.It should quantify & track
measurements that are vital to the success of the organization.A
wisely applied Six Sigma strategy can facilitate productive
utilization of information.It can be used in manufacturing,
service, product design, and transactional applications.Visual
observations, pictures, data analysis and statistical assessments
are the key to the success of a Six Sigma projects.
CONTROL CHARTS
What is a Control Chart ?
A control chart is simply a run chart with confidence intervals
calculated and drawn in. These “Statistical control limits” form
the trip wires which enable us to determine when a process
characteristic is operating under the influence of a “Special
cause”.
6. Control charts are useful to: determine the occurrence of
“special cause” situations.Utilize the opportunities presented by
“special cause” situations” to identify and correct the
occurrence of the “special causes” .
IMPROVEMENT ROADMAP
Uses of Control Charts
Breakthrough
Strategy
Phase 4:
Control
Characterization
Phase 1:
Measurement
7. Phase 2:
Analysis
Optimization
Phase 3:
Improvement
Control charts can be effectively used to determine “special
cause” situations in the Measurement and Analysis phases
Common Uses
KEYS TO SUCCESS
Use control charts on only a few critical output characteristics
Ensure that you have the means to investigate any “special
cause”
What is a “Special Cause”?
Remember our earlier work with confidence intervals? Any
occurrence which falls outside the confidence interval has a low
probability of occurring by random chance and therefore is
“significantly different”. If we can identify and correct the
cause, we have an opportunity to significantly improve the
8. stability of the process. Due to the amount of data involved,
control charts have historically used 99% confidence for
determining the occurrence of these “special causes”
Special cause occurrence.
X
Point Value
9. +/- 3s = 99% Confidence Band
So how do I construct a control chart?
First things first: Select the metric to be evaluatedSelect the
right control chart for the metricGather enough data to calculate
the control limitsPlot the data on the chartDraw the control
limits (UCL & LCL) onto the chart.Continue the run,
investigating and correcting the cause of any “out of control”
occurrence.
How do I select the correct chart ?
Note: A defective unit can have more than one defect.
What type of data do I have?
Variable
Attribute
Counting defects or defectives?
X-s Chart
IMR Chart
X-R Chart
10. n > 10
1 < n < 10
n = 1
Defectives
Defects
What subgroup size is available?
Constant Sample Size?
Constant Opportunity?
yes
yes
no
no
np Chart
u Chart
p Chart
c Chart
SPC Charts
Steps for SPC implementation
11. Processes ideally suited for SPCAre repetitive (produce
simmilar items)Have a high inspection contentHave higher than
desired reject rateItems (parts) with dimensions that are easy to
measure
SPC charts
The most frequently SPC charts are:
1) Xbar:r charts
2) p-charts
Xbar:r charts are used for controlling processes which have
“variable” data.
p-charts are used for controlling processes which have
“attribute” data
Example of Xbar:r chart for a machined part
Example of a process which is in control
Example of a process which is heading out of control
12. Example of out of control process
(If 7 or more sequential data points are above or below Xbar
line)
Steps to develop Xbar chart
Step 1: Collect 100 different data points as follows:
(20 different subgroups with 5 data points in each subgroup)
Step 2: Calculate the value of the grand average as
follows:Calculate the average value of each of the 20
subgroups.Calculate the average of all 20 subgroups called
Xbar.This is the center-line of the Xbar chart.
Step 3: Calculate the standard deviation (s) of the average
values of 20 subgroups
Step 4: Multiply the standard deviation value by 3
Upper control limit = Xbar + 3s
Lower control limit = Xbar - 3s
Steps to develop r-chart
Step 1: Determine the ranges for each of the 20 subgroups
Step 2: Calculate the value of the grand average as
follows:Calculate the grand average by summing all 20 values
and then dividing the sum by 20. This is called rbar This is the
center-line of the rbar chart.
Step 3: Calculate the standard deviation (s) of the average
r-values values of 20 subgroups
Step 4: Multiply the standard deviation value by 3
Upper control limit = rbar + 3s
13. Lower control limit = rbar - 3s
Example of out of control process
(If a pattern is repeated in the Xbar chart)
Steps to develop p-chart
Step 1: determine the number of samples “n” to develop the
chart.
Step 2: Inspect each sample and identify the number of
defects in each sample.
Step 3 Calculate the percentage defects “p” for each sample.
Step 4: Calculate the paverage as follows:
paverage = (n1p1+n2p2+ ….. nkpk) / (n1+ n2
+…….nk)
Step 5: Calculate the upper and lower control limits as follows:
Components of a control chart
Cases when the process is “out of control”
and should be stopped for investigation
14. What do I do when the process is “out of control”?
Time to Find and Fix the cause Look for patterns in the
dataAnalyze the “out of control” occurrenceFishbone diagrams
and Hypothesis tests are valuable “discovery” tools.
Stages of continuous improvement
UCL =
Paverage
+ 3
(
Paverage
)
(
1
-
Paverage
)
n
LCL =
Paverage
-
3
(
Paverage
)
(
15. 1
-
Paverage
)
n
Unit II Essay
Over the course of this unit, we have discussed the importance
of mission and vision statements. As a part of that discussion,
we analyzed mission and vision statements for their
effectiveness. For the Unit II Essay, you will expand on this
topic.
Using your favorite search engine, research the mission and
vision statements of different Fortune 500 companies. Then, you
will write an essay in which you compare and contrast the
mission statements of two companies and the vision statements
of two companies. You may use the same companies for both
the mission and vision comparisons or separate companies.
Within your essay, include the following: Explain the principle
value of two vision statements. Explain the principle value of
two mission statements. Compare and contrast vision
statements of each organization in terms of composition and
importance. Compare and contrast mission statements of each
organization in terms of composition and importance. Do you
think organizations that have comprehensive mission statements
tend to be high performers? How do mission and vision
statements assist in selecting an industry-specific strategy?
Explain why a mission statement should not include monetary
amounts, numbers, percentages, ratios, goals, or objectives.
Your essay should be a minimum of three pages in length or
approximately 750 words, not including the title and reference
pages. You must also include an outside source from the
Waldorf Online Library to support your explanations. Follow
APA standards for formatting and referencing.
16. Access to the Waldorf Online Library:
https://mywaldorf.waldorf.edu/student/resources/library/
Lecture 2
Review of
basic statistics
IMPROVEMENT ROADMAP
Uses of Probability DistributionsEstablish baseline data
characteristics.
Project UsesIdentify and isolate sources of variation.Use the
concept of shift & drift to establish project
expectations.Demonstrate before and after results are not
random chance.
Breakthrough
Strategy
Phase 4:
Control
Characterization
17. Phase 1:
Measurement
Phase 2:
Analysis
Optimization
Phase 3:
Improvement
Measurements are critical...If we can’t accurately measure
something, we really don’t know much about it.If we don’t
know much about it, we can’t control it.If we can’t control it,
we are at the mercy of chance.
Types of DataData where the metric is composed of a
classification in one of two (or more) categories is called
Attribute data. This data is usually presented as a “count” or
“percent”.Good/BadYes/NoHit/Miss etc.Data where the metric
consists of a number which indicates a precise value is called
Variable data.TimeMiles/Hr
Use Minitab to have students record the results and have the
students display using Graph..HistogramNote how “rough” the
18. graph looksRedo using Basic Statistics …. Descriptive Statistics
and display using the Graphical Summary. Walk through the
normal curve transform:Mean (Arithmetic Average)Standard
DeviationSkew (How off center the data is skewed -
=left)Kurtosis (How flat or peaked the data is -=flat)Show the
Box Plot:Quartile (25% of the Data Points)Median (50% of the
Data Points on Each Side)Show the 95% Confidence Interval
and Explain how it relates to the data.
Probability and Statistics Probability and Statistics influence
our lives daily Statistics is the universal language for science
Statistics is the art of collecting, classifying,
presenting, interpreting and analyzing numerical
data, as well as making conclusions about the
system from which the data was obtained.
Population Vs. Sample (Certainty Vs. Uncertainty) A sample is
just a subset of all possible values
population
sample
Since the sample does not contain all the possible values, there
is some uncertainty about the population. Hence any statistics,
such as mean and standard deviation, are just estimates of the
true population parameters.
19. Descriptive Statistics
Descriptive Statistics is the branch of statistics which
most people are familiar. It characterizes and summarizes
the most prominent features of a given set of data (means,
medians, standard deviations, percentiles, graphs, tables
and charts.
Descriptive Statistics describe the elements of
a population as a whole or to describe data that represent
just a sample of elements from the entire population
Inferential Statistics
Inferential Statistics is the branch of statistics that deals with
drawing conclusions about a population based on information
obtained from a sample drawn from that population.
While descriptive statistics has been taught for centuries,
inferential statistics is a relatively new phenomenon having
its roots in the 20th century.
We “infer” something about a population when only information
from a sample is known.
Probability is the link between
Descriptive and Inferential Statistics
20. USES OF PROBABILITY DISTRIBUTIONS
Primarily these distributions are used to test for significant
differences in data sets.
To be classified as significant, the actual measured value must
exceed a critical value. The critical value is tabular value
determined by the probability distribution and the risk of error.
This risk of error is called a risk and indicates the probability of
this value occurring naturally. So, an a risk of .05 (5%) means
that this critical value will be exceeded by a random occurrence
less than 5% of the time.
21. Critical Value
Critical Value
Common Occurrence
Rare Occurrence
Rare Occurrence
WHAT IS THE MEAN?
The mean is the most common measure of central tendency for a
population. The mean is simply the average value of the data.
n=12
Mean
24. -1
0
0
0
0
0
1
3
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
4
If we rank order (descending or ascending) the data set for this
distribution we could represent central tendency by the order of
the data points.
If we find the value half way (50%) through the data points, we
have another way of representing central tendency. This is
called the median value.
Median Value
Median
50% of data points
25. WHAT IS THE MODE?
If we rank order (descending or ascending) the data set for this
distribution we find several ways we can represent central
tendency.
We find that a single value occurs more often than any other.
Since we know that there is a higher chance of this occurrence
in the middle of the distribution, we can use this feature as an
indicator of central tendency. This is called the mode.
Mode
Mode
ORDERED DATA SET
-5
-3
-1
-1
0
0
0
0
0
1
3
-6
-5
30. 5
6
4
n/2=6
n/2=6
}
Mode = 0
Mode = 0
MEDIAN
(50 percentile data point)
Here the median value falls between two zero values and
therefore is zero. If the values were say 2 and 3 instead, the
median would be 2.5.
MODE
(Most common value in the data set)
The mode in this case is 0 with 5 occurrences within this data.
Median
n=12
SO WHAT’S THE REAL DIFFERENCE?
MEAN
The mean is the most consistently accurate measure of central
tendency, but is more difficult to calculate than the other
measures.
MEDIAN AND MODE
The median and mode are both very easy to determine. That’s
the good news….The bad news is that both are more susceptible
to bias than the mean.
31. SO WHAT’S THE BOTTOM LINE?
MEAN
Use on all occasions unless a circumstance prohibits its use.
MEDIAN AND MODE
Only use if you cannot use mean.
Example of tossing a coin 200 time
(probability of getting heads)
1
3
0
1
2
0
1
1
0
1
0
0
9
0
8
0
7
0
34. What are some of the ways that we can easily indicate the
dispersion (spread) characteristic of the population?
Three measures have historically been used; the range, the
standard deviation and the variance.
WHAT IS THE RANGE?
ORDERED DATA SET
-5
-3
36. 4
5
9
(
)
The range is a very common metric which is easily determined
from any ordered sample. To calculate the range simply
subtract the minimum value in the sample from the maximum
value.
Range
Max
Min
WHAT IS THE VARIANCE & STANDARD DEVIATION?
The variance (s2) is a very robust metric which requires a fair
amount of work to determine. The standard deviation(s) is the
square root of the variance and is the most commonly used
measure of dispersion for larger sample sizes.
DATA SET
-5
-3
-1
-1
0
0
0
0
0
1
3
-6
46. STANDARD DEVIATION (s)
(Absolute deviation around the center point)
SO WHAT’S THE REAL DIFFERENCE?
VARIANCE/ STANDARD DEVIATION
The standard deviation is the most consistently accurate
measure of central tendency for a single population. The
variance has the added benefit of being additive over multiple
populations. Both are difficult and time consuming to calculate.
RANGE
The range is very easy to determine. That’s the good
news….The bad news is that it is very susceptible to bias.
SO WHAT’S THE BOTTOM LINE?
VARIANCE/ STANDARD DEVIATION
Best used when you have enough samples (>10).
RANGE
Good for small samples (10 or less).
SO WHAT IS THIS SHIFT & DRIFT STUFF...
The project is progressing well and you wrap it up. 6 months
later you are surprised to find that the population has taken a
shift.
-12
USL
LSL
52. Time
All of our work was focused in a narrow time frame. Over
time, other long term influences come and go which move the
population and change some of its characteristics. This is
called shift and drift.
Historically, this shift and drift primarily impacts the position
of the mean and shifts it 1.5 s from it’s original position.
Original Study
VARIATION FAMILIES
Variation is present upon repeat measurements within the same
sample.
Variation is present upon measurements of different samples
collected within a short time frame.
Variation is present upon measurements collected with a
significant amount of time between samples.
53. Sources of Variation
Within Individual Sample
Piece to Piece
Time to Time
SO WHAT DOES IT MEAN?
To compensate for these long term variations, we must consider
two sets of metrics. Short term metrics are those which
typically are associated with our work. Long term metrics take
the short term metric data and degrade it by an average of 1.5s.
IMPACT OF 1.5s SHIFT AND DRIFT
Z
PPM
ST
C
pk
PPM
LT
(+1.5
s
)
0.0
500,000
0.0
933,193
0.1
56. term ppm to be as bad as 460,000 ppm.
IMPROVEMENT ROADMAP
Uses of Probability Distributions
Breakthrough
Strategy
Phase 4:
Control
Characterization
Phase 1:
Measurement
Phase 2:
Analysis
Optimization
Phase 3:
57. Improvement
Baselining Processes
Verifying Improvements
Common Uses
Data points vary, but as the data accumulates, it forms a
distribution which occurs naturally.
Location
Spread
Shape
Distributions can vary in:
PROBABILITY DISTRIBUTIONS,
WHERE DO THEY COME FROM?
67. Original Population
Subgroup Average
Subgroup Variance (s2)
Continuous Distribution
Normal Distribution
c2 Distribution
Population and Sample Symbology
s
2
s
2
x
P
P
Cp
Value
Population
74. 99.73%
2 tail = 0.3%
1 tail = .15%
+/- 3s = 99.7%
Common Test Values
Z(1.6) = 5.5% (1 tail a=.05)
Z(2.0) = 2.5% (2 tail a=.05)
The Focus of Six Sigma…..
Y = f(x)
All critical characteristics (Y) are driven by factors (x) which
are “downstream” from the results….
Attempting to manage results (Y) only causes increased costs
due to rework, test and inspection…
Understanding and controlling the causative factors (x) is the
real key to high quality at low cost...
Probability distributions identify sources of causative factors
(x). These can be identified and verified by testing which
shows their significant effects against the backdrop of random
noise.
HOW DO POPULATIONS INTERACT?
77. mnew
snew
Population means interact in a simple intuitive manner.
m1
m2
Means Add
m1 + m2 = mnew
Population dispersions interact in an additive manner
s1
s2
Variations Add
s12 + s22 = snew2
HOW DO POPULATIONS INTERACT?
SUBTRACTING TWO POPULATIONS
78.
79. mnew
snew
Population means interact in a simple intuitive manner.
m1
m2
Means Subtract
m1 - m2 = mnew
Population dispersions interact in an additive manner
s1
s2
Variations Add
s12 + s22 = snew2
80. TRANSACTIONAL EXAMPLEOrders are coming in with the
following characteristics:
Shipments are going out with the following characteristics:
Assuming nothing changes, what percent of the time will
shipments exceed orders?
X = $53,000/week
s = $8,000
X = $60,000/week
s = $5,000
TRANSACTIONAL EXAMPLE
X
X
X
shipments
orders
shipments
orders
-
=
-
83. model the situation posed in the problem. Since we are looking
for shipments to exceed orders, the resulting distribution is
created as follows:
The new distribution looks like this with a mean of $7000 and a
standard deviation of $9434. This distribution represents the
occurrences of shipments exceeding orders. To answer the
original question (shipments>orders) we look for $0 on this new
distribution. Any occurrence to the right of this point will
represent shipments > orders. So, we need to calculate the
percent of the curve that exists to the right of $0.
TRANSACTIONAL EXAMPLE, CONTINUED
X
X
X
shipments
orders
shipments
orders
-
=
-
=
-
=
$60
,
$53
,
$7
,
000
000
85. =
X
s
s
$0
$7000
$9434
.
Look up .74s in the normal table and you will find .77.
Therefore, the answer to the original question is that 77% of the
time, shipments will exceed orders.
$7000
Shipments > orders
CORRELATION ANALYSIS
86. Correlation Analysis is necessary to:
show a relationship between two variables. This also sets the
stage for potential cause and effect.
IMPROVEMENT ROADMAP
Uses of Correlation AnalysisDetermine and quantify the
relationship between factors (x) and output characteristics (Y)..
Common Uses
Breakthrough
Strategy
Phase 4:
Control
Characterization
Phase 1:
Measurement
Phase 2:
Analysis
87. Optimization
Phase 3:
Improvement
KEYS TO SUCCESS
Always plot the data
Remember: Correlation does not always imply cause & effect
Use correlation as a follow up to the Fishbone Diagram
Keep it simple and do not let the tool take on a life of its own
WHAT IS CORRELATION?
88.
89.
90.
91.
92.
93. Input or x variable (independent)
Output or y variable (dependent)
Correlation
Y= f(x)
As the input variable changes, there is an influence or bias on
the output variable.
A measurable relationship between two variable data
characteristics.
Not necessarily Cause & Effect (Y=f(x))
Correlation requires paired data sets (ie (Y1,x1), (Y2,x2), etc)
The input variable is called the independent variable (x or
KPIV) since it is independent of any other constraints
The output variable is called the dependent variable (Y or
KPOV) since it is (theoretically) dependent on the value of x.
The coefficient of linear correlation “r” is the measure of the
strength of the relationship.
The square of “r” is the percent of the response (Y) which is
related to the input (x).
WHAT IS CORRELATION?
TYPES OF CORRELATION
123. -
-
-
å
1
r
s
s
CALC
s
xy
x
y
=
s
xy
While this is the most precise method to calculate Pearson’s r,
there is an easier way to come up with a fairly close
approximation...
APPROXIMATING “r”
Coefficient of Linear Correlation
W
L
142. 9
10
11
12
13
14
15
6.7
12.6Plot the data on orthogonal axisDraw an Oval around the
dataMeasure the length and width of the OvalCalculate the
coefficient of linear correlation (r) based on the formulas below
HOW DO I KNOW WHEN I HAVE CORRELATION? The
answer should strike a familiar cord at this point… We have
confidence (95%) that we have correlation when |rCALC|>
rCRIT.Since sample size is a key determinate of rCRIT we need
to use a table to determine the correct rCRIT given the number
of ordered pairs which comprise the complete data set.So, in
the preceding example we had 60 ordered pairs of data and we
computed a rCALC of -.47. Using the table at the left we
determine that the rCRIT value for 60 is .26. Comparing
|rCALC|> rCRIT we get .47 > .26. Therefore the calculated
value exceeds the minimum critical value required for
significance. Conclusion: We are 95% confident that the
observed correlation is significant.
Ordered
Pairs
r
CRIT
147. CENTRAL LIMIT THEOREM
For this module you will need 12 dice and flip charts.
The Central Limit Theorem is: the key theoretical link between
the normal distribution and sampling distributions.the means by
which almost any sampling distribution, no matter how
irregular, can be approximated by a normal distribution if the
sample size is large enough.
IMPROVEMENT ROADMAP
Uses of the Central Limit Theorem
Common Uses
Breakthrough
Strategy
Phase 4:
Control
148. Characterization
Phase 1:
Measurement
Phase 2:
Analysis
Optimization
Phase 3:
Improvement
The Central Limit Theorem underlies all statistic techniques
which rely on normality as a fundamental assumption
WHAT IS THE CENTRAL LIMIT THEOREM?
Central Limit Theorem
For almost all populations, the sampling distribution of the
mean can be approximated closely by a normal distribution,
provided the sample size is sufficiently large.
149.
150. Normal
What this means is that no matter what kind of distribution we
sample, if the sample size is big enough, the distribution for the
mean is approximately normal.
This is the key link that allows us to use much of the inferential
statistics we have been working with so far.
This is the reason that only a few probability distributions (Z, t
and c2) have such broad application.
If a random event happens a great many times, the average
results are likely to be predictable.
Jacob Bernoulli
151. HOW DOES THIS WORK?
As you average a larger and larger number of samples, you can
see how the original sampled population is transformed..
163. ANOTHER PRACTICAL ASPECT
s
n
x
x
=
s
1
n
This formula is for the standard error of the mean.
What that means in layman's terms is that this formula is the
prime driver of the error term of the mean. Reducing this error
term has a direct impact on improving the precision of our
estimate of the mean.
The practical aspect of all this is that if you want to improve the
precision of any test, increase the sample size.
So, if you want to reduce measurement error (for example) to
determine a better estimate of a true value, increase the sample
size. The resulting error will be reduced by a factor of .
The same goes for any significance testing. Increasing the
sample size will reduce the error in a similar manner.
Team experimentBreak into 3 teams
164. Team one will be using 2 dice
Team two will be using 4 dice
Team three will be using 6 dice
Each team will conduct 100 throws of their dice and record the
average of each throw.
Plot a histogram of the resulting data.
Each team presents the results in a 10 min report out.
(
)
X
X
i
-
2
X
Lecture 1
Introduction to
Six Sigma
History & experiences with Six SigmaMotorola is a birthplace
of Six Sigma and launched the program in January 15, 1987.GE
expanded the application of Six Sigma from manufacturing to
service and increased the operating income to $750 million in
1998.Other successes of Six Sigma in GE:
a) Produced a 10-fold increase in the life of CT scanner.
b) Quadrupled the ROI in Industrial Diamond business.
165. c) 62% reduction in turnaround time at repair shop in railcar
leasing process.
d) Added 300 million pounds of new capacity in plastic
business.Since their success, many other fortune 500 companies
have implemented Six Sigma with significant financial benefits.
Pioneers of Six Sigma quality revolution
1) Dr. Edward Deming (USA): Introduced basic quality
philosophy, that productivity improves as variability decreases.
2) William Convey (USA): Introduced the tools to eliminating
waste in processes in order to improve quality.
3) Joseph Juran (USA): Introduced the concept of Total Quality
Management (TQM) by adding “human dimension” to quality.
4) Philip Crosby (USA): Introduced the role of management in
quality improvement by using simple tools & statistical quality
control.
5) Dr. Genichi Taguchi (Japan): Introduced the philosophy of
“design of experiments” to improve quality by introducing
variance-reduction strategies and robust design techniques.
6) Shugeo Shingo (Japan): Introduced the idea of improving
process design so that mistakes are impossible to make or at
least easily detected and corrected (Poka-yoke).
Comments by Jack Welch who started Six Sigma at GE
“In just three years, Six Sigma had saved the company more
than $2 billion”.
166. “At GE, it’s almost an obsession to keep external customer’s
need in plain sight, driving the improvement effort”.
“We did not invent Six Sigma, but we learned it and applied it
effectively to manufacturing and service departments”.
“The cumulative impact on the company’s numbers is not
anecdotal, nor a product of charts. It is the product of all
employees executing & delivering the results of Six Sigma to
our bottom line”.
Comment by Juran (1988) about “Cost of poor quality”
(COPQ)Quality-related cost is about 20% - 40% of sales in an
organization.Quality costs are incurred not only in
manufacturing but also in support areas (service).There is no
person or department in an organization which is directly
responsible for reducing quality cost. Most of the functions of
the quality department are involved with measuring quality but
not reducing it.
The hidden cost of quality
167. Examples of Quality costs
1) Prevention 3) Internal Failure
Training Scrap & Rework
Capability Studies Design Changes
Vendor Survey Retyping Letters
Quality Design Excess Inventory Cost
2) Appraisal 4) External Failure
Inspection & Test Warranty cost
Test Equipment & Maintenance Customer
Complaints
Inspection & Test Reporting Returns & Recalls
Other Expense Reviews Liability Suits
Two critical components of Six Sigma
Reduces dependency on “Tribal Knowledge”
- Decisions based on facts and data rather than opinion
Attacks the high-hanging fruit (the hard stuff)
- Eliminates chronic problems (common cause variation)
- Improves customer satisfaction
Provides a disciplined approach to problem solving
168. - Changes the company culture
Creates a competitive advantage (or disadvantage)
Improves profits!
Why Companies Need Six Sigma
Six Sigma is not another quality programSix Sigma is a
technique which fixes identifiable, chronic problems that
directly impacts the bottom line.Six Sigma projects are selected
to reduce or eliminate waste, which improves productivity.Six
Sigma is not a theory but a tool which defines, measures,
analyzes, improves, and controls the processes that matter most,
to tie quality improvement directly to the bottom-line results.
Motorola ROI
1987-1994
• Reduced in-process defect levels by a factor of 200.
• Reduced manufacturing costs by $1.4 billion.
• Increased employee production on a dollar basis by 126%.
• Increased stockholders share value fourfold.
AlliedSignal ROI
1992-1996
• $1.4 Billion cost reduction.
• 14% growth per quarter.
• 520% price/share growth.
• Reduced new product introduction time by 16%.
• 24% bill/cycle reduction.
Six Sigma ROI
169. Six Sigma as a Philosophy
Internal &
External
Failure
Costs
Prevention &
Appraisal
Costs
Old Belief
4s
Costs
Internal &
External
Failure Costs
Prevention &
Appraisal
Costs
New Belief
Costs
4s
5s
6s
Quality
Quality
Old Belief
High Quality = High Cost
New Belief
High Quality = Low Cost
s is a measure of how much variation exists in a process
170. Input-output of the process
Key Process Output Variables (Y) Profits Customer satisfaction
Expense Production cycle time Defect rate Critical dimension
on the part
Key Process Input Variables (X) Actions taken Out of stock
items Amount of WIP inventory Amount of internal rework
Inspection procedure Process temperature
Principles of Six Sigma
1) Focus on the customer
2) Collect data and facts about the processes
3) Continue to improve the performance of the process
4) Act in advance of events rather than reacting to them
5) Collaborate across organizational lines
6) Drive for perfection & tolerate failure
POSITIONING SIX SIGMA
THE FRUIT OF SIX SIGMA
Ground Fruit
Logic and Intuition
Low Hanging Fruit
Seven Basic Tools
Bulk of Fruit
Process Characterization and Optimization
Process Entitlement
171. Sweet Fruit
Design for Manufacturability
Definition of Six Sigma“Six Sigma is a strategic business
improvement approach that seeks to increase both customer
satisfaction and an organization’s financial health” “Six Sigma
puts customer first and uses facts and data to drive better
solutions”.“Six Sigma is about making every area of the
organization better able to meet the changing needs of
customer, markets, and technologies”.Six Sigma efforts target
three main areas:
a) Improving customer satisfaction
b) Reduce cycle time
c) Reducing defects
Key characteristics of Normal DistributionThe normal curve is
totally independent of the LSL and USL.This curve summarize
the empirical quantification for the variability that exists within
the manufacturing process.The normal distribution in science,
engineering & manufacturing is typically used when the
measuring characteristic falls outside the range of + 3 standard
deviation about the target mean.The process outside of the
customer specification limits are defined as defects, failures, or
nonconformities.+ 3s unit of standard deviation represents
99.73% of the total area under the normal distribution curve.
This corresponds to 0.27% outside the curve or 2700 defects per
million. For 6s the defective rate is 0.002 ppm.
172. What is a defect?Six Sigma is a statistical concept that
measures a process in terms of defects.A defect is a measurable
characteristic of the process or its output that is not within the
acceptable process specifications.Six Sigma is about practices
that help you eliminate defects.Sigma level is calculated in
terms of number of defects in ratio to the number of
opportunities for defects.
The statistical definition of Six SigmaSpecification limits are
the tolerances or performance ranges that customers demand of
the products or processes they are purchasing.It falls within the
range between customer-specified lower specification limit
(LSL) and upper specification limit (USL).
Definition of Sigma Quality level
To address “typical” shifts of a process mean from a
specification centered value, Motorola added a shift value +
1.5s to the mean. This shift of the mean is used when computing
a process “sigma level” or “sigma quality level”. 3.4 ppm rate
corresponds to 6s quality level.
173. Defective ppm with & without shift
Spec limitwithout shift with shift
(Defective ppm) (Defective ppm)
+ 1 sigma 317,300 697,700
+ 2 sigma 45,500 308,700
+ 3 sigma 2,700 66,800
+ 4 sigma 63 6,210
+ 5 sigma 0.57 233
+ 6 sigma 0.002 3.4
What is the sigma level for on-time delivery 2 sigma = 68% on-
time delivery 3 sigma = 93% on-time delivery 4 sigma = 99.4%
on-time delivery 5 sigma = 99.98% on-time delivery 6 sigma =
99.9997% on-time delivery
Keep in mind that the sigma level measures how well you are
meeting customer requirements.
3 Sigma vs. 6 Sigma
parameter conformance
How good is good enough?
99.9% is already VERY GOOD
174. But what could happen at a quality level of 99.9% (i.e., 1000
ppm),
in our everyday lives (about
•
4000 wrong medical prescriptions each year
175. More than 3000 newborns accidentally falling
from the hands of nurses or doctors each year
•
177. Two long or short landings at American airports each day
Measurements are critical...If we can’t accurately measure
something, we really don’t know much about it.If we don’t
know much about it, we can’t control it.If we can’t control it,
we are at the mercy of chance.
THE ROLE OF STATISTICS IN SIX SIGMA..
WE DON’T KNOW WHAT WE DON’T KNOW
IF WE DON’T HAVE DATA, WE DON’T KNOW
IF WE DON’T KNOW, WE CAN NOT ACT
IF WE CAN NOT ACT, THE RISK IS HIGH
IF WE DO KNOW AND ACT, THE RISK IS MANAGED
IF WE DO KNOW AND DO NOT ACT, WE DESERVE THE
LOSS.
178. TO GET DATA WE MUST MEASURE
DATA MUST BE CONVERTED TO INFORMATION
INFORMATION IS DERIVED FROM DATA THROUGH
STATISTICS
179. Learning ObjectivesHave a broad understanding of statistical
concepts and tools.
Understand how statistical concepts can be used to improve
business processes.
Understand the relationship between the course and the four
step six sigma problem solving process (Measure, Analyze,
Improve and Control).
Three key characteristics of
Six Sigma projects at GE.
1) It must be customer focused.
2) It must produce major returns on investment (ROI).
3) It changes how management operates.
How to identify Six Sigma projects?
1) Projects which have potential for significant monetary
savings.
2) Projects which are of strategic importance.
3) Projects which involve key output issues.
4) Projects which are of vital concern to the customer.
5) Projects which can be quantified with poor quality within
180. strategic area of business.
Key focus areas for Six Sigma program
1) Measurement: It should focus on statistical measure of the
performance of a process or a product.
2) Goal: It should focus on the goal that reaches near perfection
for performance improvement.
3) System of management: It should focus on a system of
management to achieve lasting business leadership and World
class performance.
Six Sigma Tools
Process MappingTolerance AnalysisStructure TreeComponents
SearchPareto AnalysisHypothesis TestingGauge R &
RRegressionRational SubgroupingDOEBaseliningSPC
181. Problem Solving Methodology
Breakthrough Strategy
Characterization
Phase 2:
Measure
Phase 3:
Analyze
Optimization
Phase 4:
Improve
Phase 5:
Control
Projects are worked through these 5 main
phases of the Six Sigma methodology.
Phase 1:
Define
Unlocking the hidden factory
VALUE STREAM TO THE CUSTOMER
PROCESSES WHICH PROVIDE PRODUCT VALUE IN THE
CUSTOMER’S EYES
182. FEATURES OR CHARACTERISTICS THE CUSTOMER
WOULD PAY FOR….
WASTE DUE TO INCAPABLE PROCESSES
WASTE SCATTERED THROUGHOUT THE VALUE STREAM
EXCESS INVENTORYREWORKWAIT TIMEEXCESS
HANDLINGEXCESS TRAVEL DISTANCESTEST AND
INSPECTION
Waste is a significant cost driver and has a major impact on the
bottom line...
Common Six Sigma Project AreasManufacturing Defect
ReductionCycle Time ReductionCost ReductionInventory
ReductionProduct Development and IntroductionLabor
ReductionIncreased Utilization of ResourcesProduct Sales
ImprovementCapacity ImprovementsDelivery Improvements
Six Sigma scorecard
FINANCIALInventory levelsCost per unitHidden
factoryActivity-based costingCost of poor qualityCost of doing
nothing differentOverall project savings
INTERNAL PROCESSDefects: inspection data, DPMO, Sigma
quality levelOn-time shippingRolled throughput yieldSupplier
qualityCycle timeVolume hoursBaseline measurementsKPIVs
CUSTOMERCustomer satisfaction (CTW)On-time
deliveryProduct quality (KPOV)SafetyCommunications
183. LEARNING AND GROWTHSix Sigma tool utilizationQuality
of trainingMeeting effectivenessLessons learnedNumber of
employees trained in Six Sigma datesNumber of project
completedTotal dollars saved on Six Sigma projects to date
Six Sigma applicationManufacturingServicesEngineering and
R&DSales and marketingHealth careGovernmentCorporate
functions
Typical Six Sigma projects in ManufacturingIncreasing
manufacturing yieldReducing assembly cycle-timeMinimizing
changeover timeReducing variations in machining
processEliminating need for regular testing
Typical Six Sigma projects in ServicesReducing time to open a
new accountEliminating statement errorsMinimize wait time for
customerReducing cycle time for check-inReducing invoice
errors
Typical Six Sigma projects in Engineering and R&DReducing
time for engineering drawing changeReducing the time for
approval and changesReduce the time to procure test
materialsIncreasing the utilization of test equipment
Typical Six Sigma projects in Sales and MarketingOptimizing
sales force allocationMinimizing promotion timeImproving
184. response rate from customerImproving communication rate of
proposalsReducing time to prepare for complex bids
Typical Six Sigma projects in Health careReducing inpatient
length of stayReducing ER wait timeReducing outstanding
accounts receivableReducing fall and injury claimsImprove and
streamline medical procedure
Typical Six Sigma projects in GovernmentReducing the amount
of waste activitiesIncreasing the number of
inspectionsStreamlining the permit processReducing the number
of change orders in contracts
Typical Six Sigma projects in Corporate functionsReducing
time for monthly closingDecreasing patent-filing timeReducing
time for hiring processIncreasing response rate of employee
surveyIncreasing the accuracy of tracking of assets
Characteristics of Six Sigma success
Very successful programCommitted leadershipUse of top
talentFormal project selection and review processDedicated
resourcesFinancial system integration
Mediocre programSupportive leadershipUse of whoever was
availableNo formal project selection and review processPart-
time resourcesNo financial system integration
185. Following two approaches are used in the market- place to
improve manufacturing operations
1) Lean Manufacturing
2) Six Sigma
They are dependent upon each other for success.They both
battle “variation” from two different point of views.Generally
Lean manufacturing program is implemented before Six Sigma
program.Lean manufacturing strategy builds a strong foundation
for the effective use of Six Sigma tools.Lean manufacturing
focuses on the entire operation of a factory to reduce
“variation” while Six Sigma focuses on a specific part number
or a specific process to reduce “variation“.
Lean Manufacturing Versus Six Sigma
Lean Manufacturing Focuses on the product flow & on the
operator Standardization of operator method Reduce
communication barriers through flow-focused cells Focuses on
process speed & feedback of information
Six Sigma Uses data driven technique for problem solving It is
a method to measure deviation from the standard Focuses on
part variation & overall process variation Addresses process
variations which builds up over time
Lean manufacturing creates the standard and Six Sigma
analyzes variations from the standard
186. What is DMAIC?Define the projects and the goals of internal
and external customers.Measure the current performance of the
process.Analyze and determine the root cause(s) of the
defects.Improve the process to eliminate defects.Control the
performance of the process.
USL
T
m
LSL
Six Sigma BME/IHE 6850-90 (lectures 1-3)
Distance case study #1
(Due date: Saturday, September 24, 2016)
Use 12 size font, single spaced
Case study 1.1: The P.T. Company located in Florida makes
electronic rocker-recliner chairs. The success of these chairs
over the past three years has been remarkable. However, for the
past couple of months, sales have been in a dramatic and
alarming slide. Distributors have been flooding the home office
with e-mails about returns and customer complaints. As the
biggest contributor to the company’s profits, any problems with
these chairs (called the E-Rock) created big worries for the
management. The top product management group and the senior
executive team met to discuss strategies for dealing with the
declining sales. “This product has outlived its welcome,”
exclaimed the head of marketing. “We need to get going on a
whole new generation of E-chairs!”
“That’s going too far,” countered the field sales director. “I
can’t believe the entire market suddenly decided that the E-
Rock is outmoded in a period of weeks.” The head of seat
engineering weighed in: “We need to give more incentives to
those distributors or threaten to drop them. It’s clear they’re
getting lazy.”
187. After a few more minutes of discussion (using some better
meeting management tools) the group was able to agree to get a
Six Sigma DMAIC team started on trying to find out the causes
of the sales decline.
Case study objective: You have been appointed the DMAIC
team leader. Using information in lectures 1-3, describe how
you will proceed to solve the problem of declining sales.
(Do not exceed 1 page, 12 font size, single spaced).
Case study 1.2: Refer to the slide “Common Six Sigma Project
Areas”. There are following 10 areas described in the slide:
1. Manufacturing Defect Reduction
2. Cycle Time Reduction
3. Cost Reduction
4. Inventory Reduction
5. Product Development and Introduction
6. Labor Reduction
7. Increased Utilization of Resources
8. Product Sales Improvement
9. Capacity Improvement
10. Delivery Improvement.
For each of the above project areas, write a statement (one
sentence) about strategic project goal
188. See the examplebelow:
Six Sigma Project Area 10:
Delivery Improvement
Strategic Project Goal:
Ship customer orders within one week of receiving the order.
Case study 1.3: Refer to the slide “Six Sigma application”,
which lists the 7 areas of application:
1. Manufacturing
2. Services
3. Engineering and R&D
4. Sales and Marketing
5. Healthcare
6. Government
7. Corporate function
Refer to the following 7 slides, which lists the typical Six
Sigma projects in each of the above areas. Each slide has 4 or 5
projects.
Case study objective: Select one project from each slide (area)
and list 2 KPIVs for that project.
See the following example: (note KPIV = Key Process Input
Variable). Note that each KPIV starts with “variation ……..” in
the following example.
Area:
Manufacturing
Project:
Reducing variations in machining process
189. KPIV-1:
Variation in the vendor material reject rate
KPIV-2
Variation in employee skill level (seniority)
Case study 1.4 and 1.5 : Dave Johnson is the manager of an
insurance claims office and serves on a Six Sigma team that
established a goal of making improvements aimed at speeding
up the number of claims processed per day. To get started, the
team gathered data for just over a month (using good sampling
methods) and plotted the data on a run chart (figure 2). Master
Black Belt showed Dave how to determine the expected amount
of variation in the claims process, and to plot the resulting
“control limits” on the chart. When looking at this control chart
(figure 3), Dave saw that many data points were beyond the
control limits.
Figure 2: Run Chart of Insurance Claims
Figure 3: Control Chart of Insurance Claims
Objective 1. 4: Explain why some points are outside (above and
below) the control limits.
Objective 1.5: What actions will you take to bring the process
within control limits.
UCL
LCL