Foundations - 02 - What is Six Sigma Part TWO

What is Six Sigma? Part 2. Here you will deep dive into the six-sigma breakthrough equation, how it is applied to real world processes, its formula elements, and the three key six sigma uses.

Foundations - 03 - The Six Sigma Breakthrough Equation

Here you will learn that six sigma is used to run the business, and becomes a way of life, and that there is a non-linear relationship between defects. You will understand the breakthrough equation and how its elements are used. creating the resulting dependent variable are transformed by a defined function, along with the independent variables, and combined with random error

Foundations - 04 - The Accuracy of six sigma

The Accuracy of Six Sigma. Here you will learn how six sigma is used as a performance measure, as a management philosophy, and as a problem-solving tool. You will grasp how six sigma is used as a measure of accuracy, differences between three sigma and six sigma, and the relationship between sigma and defects per million opportunities (DPMO).

Foundations - 05 - six-sigma-principles-and-improvements

Six Sigma Principles, and Improvements. Here you will learn how six sigma and financial health of an organization is related in terms of turnover, profit and operating costs, why LSS is used in non-manufacturing industries for example, in services, healthcare, financial and educational industries. You will grasp the five key principles of LSS, and how a customer’s requirement is called a CTQ, critical to quality, and why these are written in a way that is measurable.

Foundations - 06 - Six Sigma Benefits

Six Sigma Benefits. Here you will learn all about six sigma benefits such as reduced defects, increased revenue, cost reduction, increased process yield, performance consistency, reduction in capital and labour, and increased profit margin. You will understand the hidden factory and six sigma quality.

Foundations - 07 - Lean Principles

Here you will learn all about the origins and goals of lean and how it leads to high quality, low rework and waste and good flexibility leading to low cost. You will identify the lean six sigma leaders at Motorola, GE, and Allied Signal. You will understand the five key principles of Lean

Foundations - 08 - Lean Fundamentals

Here you will learn the nine Muda's (a Japanese term that means "waste.") Lean is about eliminating waste in the manufacturing process. There are 9 areas of most identified “fatty” areas, or Muda's, in traditional manufacturing systems, and be given examples of Muda. You will understand the nine lean fundamentals of people power, levelling, sequencing, standardising, autonomation, stop at every abnormality and just in time (JIT).

Foundations - 09 - Combining Lean and Six Sigma

Here you will learn all about combining Lean and Six Sigma, and how Lean provides more value with less effort, and eliminates wasteful activities, variation and inconsistency in Processes/system, and that Lean builds flexibility in organizations to meet customer requirements and demands. You will grasp that Lean is focussed on improving and shortening process flow where the timeline between customer request and delivery while eliminating waste, whereas six sigma is a rigorous and structured approach to help manage and improve quality and performance.

Foundations - 10 -Lean Six Sigma Principles

Here you will learn the seven lean six sigma principles, the benefits and goals of lean six sigma and quantifiable improvements that this brings. You will grasp the benefits and goals of lean six sigma, and the five important terms in six sigma.

Foundations - 11 - Customer Specification and Defects Part 1

Here you will learn the customer specification and defects where there are many simple ratios to understand – drawn from the world of manufacturing and production. You will understand that at the root of lean six sigma is the effort to reduce process failures by eliminating, or at least reducing the factors that cause them You will understand how well a process performs by identifying the resulting (per manufactured unit) defects per unit, defect opportunity attributes, and defects per opportunity (DPO)

Foundations - 12 - customer-specification-and-defects-part-2

Here you will learn about Defectives Per Million Units (DPM), which is used to measure and report defectives, Defects per Million Opportunities (DPMO), which uses the data from DPO. It differs from a calculation of (defective) Parts per Million (PPM) because there are multiple failure opportunities in a single unit. You will grasp Parts per Million (PPM) which counts the quantity of defective parts per million parts produced, First Pass Yield (FPY) or Throughput Yield (TPY) which are synonymous terms that define the number of units successfully produced in a process step. Finally you will understand Specification and Defect Examples

Foundations - 13 - Yield and Product Acceptance

Here you will learn how First Time Yield (FTY) is a calculation used to determine when the total number of acceptable products leave the process when rework/scrap are counted, but only if the rework performed corrects the defect(s). And how Rolled Throughput Yield (RTY) is an accumulated total, by multiplication, of the results of all FPY/FTY-measured processes.

Foundations - 14 - cycle-time-and-takt-time

Here you will learn how cycle time and takt time (and manufacturing lead time), differ. cycle time and takt time (and manufacturing lead time), differ. You will grasp that Cycle Time is simply the time from beginning to completion of a process step, and Manufacturing Lead Time is the measure of the total time to manufacture a part through its complete value stream, or entire manufacturing process. You will understand that TAKT Time is the pace (or rhythm) needed to exactly meet customer demand, and that inconsistency has many outcomes. Finally, you will learn some examples of business problems and Six Sigma.

Foundations - 15 - Variation and six sigma calculation

Here you will learn how the significance of variation is critical to quality and grasp several examples of how this is applied. You will understand the six steps to calculating six sigma, and learn the double diamond principle.

Foundations - 16 - Approaches to using Lean Six Sigma

Here you will learn how Lean, and six sigma works everywhere and understand nine different examples. You will be able to answer the question of why you will always need to apply lean six sigma in some way, and understand that a DPIC methodology is designed to tackle problems with obvious solutions or "Just do it" fixes and DMAIC and DFSS approach, and if your process is so badly broken that you need to start over from scratch, or you're designing a new product, service, or process, use Design For Six Sigma (DMEDI)

Foundations - 17 - The Quality Culture

Here you will learn how quality culture is basically incorporation of quality in the overall system of an organization which leads to a positive internal environment and creation of delighted customers, and that cultural awareness of quality, its importance to the business and its customers must be led and supported by senior management. You will grasp several examples of how this occurs. You will understand the four pillars of a quality culture, and how existing processes will be improved using DMAIC

Foundations - 18 - DMAIC and Project Selection

Here you will be able to describe the five steps of DMAIC and the detailed actions for each of them. You will grasp the need for the 6 Sigma project selection criteria and understand two commonly used tools of the pareto chart, and the criteria-based matrix.

Define Phase - 03 - The DMAIC Define Phase Process Mapping Tools Part ONE

Module three is the first of two lessons looking at process mapping tools. Here you will learn the use of the process map/flowchart where a flowchart is a visual tool that depicts a complete flow of actions and procedures from start to finish of a specific process. This will help understand how the work gets done now (current state) and the degree of process complexity. This helps establish where the inefficiencies are in the process and then eliminate them. You will go on to grasp the four steps of creating a process map. You will understand the tool and technique of value stream mapping Used to capture all key flows of work, information and material in a process and important process metrics, and helps in identifying and visualizing improvement opportunities

Define Phase - 04 - The DMAIC Define Phase Process Mapping Tools Part TWO

Define Phase - 04 - The DMAIC Define Phase Process Mapping Tools Part TWO. In this, the second of two lessons you will continue to learn about the DMAIC define phase process mapping tools. You will dive into the details of deployment flow charts (cross-functional flowcharts) which displays the function group or individual that carries out each step and where interfaces occur between process participants, and can reveal duplication, waste, over processing or unnecessary steps. You will compare flow chart comparisons of a process flow chart, , and alternative path flowchart, A deployment flowchart (also referred to as Swimlane flow chart or cross-functional flowchart) - describing the roles of different departments/stakeholders involved in the process. You will understand the structure of deploying a process map, and why it is a great tool for Business Process Reengineering (BPR).

Define Phase - 05 - The DMAIC Define Phase Project Charter Part ONE

The DMAIC Define Phase Project Charter. In this, the first of three lessons you will learn all about the project charter. The Project Charter is the foundation of the project and serves as a reference point during the planning and implementation stage Here, you will grasp the purpose and the eight sections, composition, detailed structure and high level content of this document. In this first lesson the focus of your learning will be on the business case and the problem statement.

Define Phase - 06 - The DMAIC Define Phase Project Charter Part TWO

The DMAIC Define Phase Project Charter Part 2. In this, the second of three lessons you will learn all about the project charter. The Project Charter is the foundation of the project and serves as a reference point during the planning and implementation stage Here, you will grasp the purpose, composition, and detailed structure of the goal statement, the project scope, and the team composition.

Define Phase - 07 - The DMAIC Define Phase Project Charter Part THREE

The DMAIC Define Phase Project Charter Part 3. In this, the last of three lessons you will learn all about the project charter. The Project Charter is the foundation of the project and serves as a reference point during the planning and implementation stage Here, you will understand how the team regularly updates the Project Sponsor, or Champion, of their progress in a one-page document called A3 consisting of current conditions, required outcome, changes and measures, current actions and decision criteria, follow-up actions, outcomes, timeline and responsibilities. Stakeholder Analysis and management begins in the Define Phase and the team communicates with this key group throughout the project.

Define Phase - 08 - The DMAIC Define Phase SIPOC Model

The DMAIC Define Phase SIPOC Model. In module 8, you will learn all about the SIPOC Model. This starts in the define the process by developing process maps. One type of high-level map is the SIPOC which stands for suppliers, inputs, process, outputs and customers. Another high-level map, closely aligned with cycle time reduction projects, is the Value Stream Map Detailed mapping can be done with a Swimlane Map format—which uses lanes representing departments—or it can be depicted with a simple flow-chart You will grasp the information contained within the construction of a SIPOC, and how it helps the sponsor and team agree on project boundaries and scope, by verifying that process inputs match upstream process outputs and inputs and their expectations of downstream processes. You will understand the six step SIPOC method as well as learning from two examples of its application.

Define Phase - 09 - The DMAIC Define Phase Voice of the Customer Part ONE

The DMAIC Define Phase Voice of the Customer. In this lesson, the first of two, you will learn how The Voice of the Customer (VOC) helps you understand customer requirements Critical to Quality (CTQ) shows how well you are performing in meeting your customers requirements You will understand external customers (individual or group not part of the organization, but is impacted by organization’s product or output, internal customers, (individual, group or department in an organization that receives partially processed products/outputs from others in the organization). In this and the next lesson you will grasp the voice of the customer (VOC). Here you will examine all potential customers, identify the key drivers of customer satisfaction, the four tools used, and how the data is clarified, going on to examine customer segmentation, customer prioritization, and the creation of a customer research plan.

Define Phase - 10 - The DMAIC Define Phase Voice of the Customer Part TWO

In this lesson, the second of two, you will learn how the voice of the customer translation matrix is used to define the customer and their requirements, turning vague comments into measurable customer requirements. You will grasp the VOC data gathering methods such as surveys, interviews, focus groups, observation or mystery shopping (to determine your customer requirements, NOT to determine solutions to those requirements). Finally, you will be introduced to segmenting your customers, helps you develop the right products and services for each group going on to develop measures and performance criteria for each group, leading you to decide on method of obtaining VOC.

Define Phase - 11 - The DMAIC Define Phase Critical to Quality

In this lesson, you will learn about the voice of quality and their specifications, which is where a specification represents the range of values at which a characteristic is still acceptable to the customer. Here, you will review each specification against RUMBA (reasonable, understandable, measurable, believable and achievable). You will grasp the total cost of poor quality (the hidden factory), how to develop good customer requirements), where the Voice of the Customer (VOC) must translate what the customer says into a measurable requirement – the Critical To Quality (CTQ). CTQ’s must be in a measurable form and provide a foundation for your measurement data. Finally you will understand the critical to quality tree which helps you translate broad customer needs into specific, actionable, measurable performance requirements.

Define Phase - 12 - The DMAIC Define Phase Pareto Chart

In this lesson, you will learn about the pareto chart developed by Vilfredo Pareto was an economist who is credited with establishing the Pareto Principle or 80/20 rule. The Pareto principle is a widely accepted technique for prioritizing effort and activity, and will typically be applied in every phase of a Lean Six Sigma project, including this initial Define phase where it is used to prioritize the areas of focus for the project You will grasp how based on the selected metric, a count of problem occurrences or the magnitude is determined A vertical bar is created with the highest column at the left, followed by columns of decreasing height, making it easy to communicate rather than just numbers with regards to process improvement.

Define Phase - 13 - The DMAIC Define Phase KANO Model

The DMAIC Define Phase KANO Model. In this lesson, you will start by looking at some commonly used CTQs, and then introduce the Kano Model, which is a tool used to classify customer requirements based on their perceived importance to customers and is used to classify these requirements. You will understand how to evaluate these requirements using KANO analysis, going on to prioritize using powerful paired comparisons.

Define Phase - 14 - The DMAIC Define Phase Customer Satisfaction and Loyalty

In this lesson, you will learn that while customer satisfaction has to do with the basic features and functions of your product or service, while today, we think of customer satisfaction as being more than just the functionality of your product or service, as it is the ability to fulfil customers’ perceived preferences. You will learn the three different types of customer satisfaction, and that Loyalty is defined as an intent to repurchase or recommend to others, and as such demonstrates Trust, plus you will grasp the brand loyalty and net promoter score (NPS) rating scale.

Define Phase - 15 - The DMAIC Define Phase Affinity Diagram

The DMAIC Define Phase Affinity Diagram. In this lesson, you will learn how an affinity diagram collects ideas and identifies natural relationships then sorting these into groups for further review and analysis (great as an input to fishbone diagrams). You will understand how an Affinity diagram helps the team develop their thoughts on the issues involved. It is a visual tool used to organize large amounts of information based on the logical relationships between them. You will understand from examples, and how the diagram can be used to prioritize.

Define Phase - 16 - The DMAIC Define Phase 5S System

In this lesson, you will learn the 5S system Because it focuses on improving a workplace, and different workplaces may have little in common, it can be hard to predict the exact results of using the program The 5S system is a lean manufacturing tool that improves workplace efficiency and eliminates waste. There are five steps in the system, each starting with the letter S. These are sort, set in order, shine, standardized and sustain.

Measure Phase -04-Failure Mode and Effects Analysis

Failure Mode and Effects Analysis. In this lesson, you will learn that Failure Mode & Effect Analysis (FMEA) is a prevention tool helping you identify and prioritize potential opportunities for taking corrective action, that can be created for processes, systems, designs, and safety concerns To help prioritize your actions – you will grasp how to calculate a risk priority number (RPN) and of FMEA example scales. You will understand how to apply this via an example FMEA template.

Measure Phase -05-Statistical Terms

In this lesson, you will learn the definition of vital statistical terms including average, mode, median, mean, standard deviation, 3 sigma and 6 sigma. You will compare and contrast terms such as statistical independence, mutually exclusive, tolerances and control limits

Measure Phase -06- Statistical Modeling

Statistical Modeling. In this lesson, you will learn about the building blocks of statistical modelling, starting with the breakthrough equation where you will grasp that to improve the performance of Y, then you must understand the relationship between Y and the various X's. You will understand the need to carry out a root cause analysis. You will learn how Process Stapling offers one way to really understand the process and the chain of events, by walking through the entire process step-by-step, noting what really happens, who does what and why, where and when they do it The Spaghetti Diagram You will learn how the spaghetti diagram tracks the movement of the thing or things going through the process including the flow of information, materials, and the people carrying out the work. You will understand the key fifteen questions to ask

Measure Phase -07- Process Flows and Value Stream Maps

Process Flows and Value Stream Maps. In this lesson, the first of two, you will learn how to build and apply flow charts and value stream maps. The Deployment Flowchart. builds on the high level SIPOC diagram by identifying who is involved in the process, what they do and what type of errors are occurring, so you can begin the process of improvement You will understand how measuring time can highlight bottlenecks, and how to differentiate between unit time, elapsed time, cycle time, lead time and dead time. You will grasp how the Measurement Selection Matrix is used to find the measure most strongly linked to requirements and customer needs You will grasp how Value Stream Mapping is used to capture all key flows of work, information and material in a process and important process metrics and helps identify and visualizing improvement opportunities and absorb a value stream example.

Measure Phase -08- Process Flows and Value Stream Maps Part TWO

Process Flows and Value Stream Maps Part TWO. In this lesson, the second of two, you will learn how to build and apply flow charts and value stream maps by walking through a detailed example of a loan application process. You will understand how to start with the supplier and their inputs to the process, add the steps and outputs, collect data for each step, quantify what is happening, add the timeline, then include any observations, and develop the future state map.

Measure Phase – 09 – Data Types

Data Types. In this lesson, you will learn the different types of data, and why they need to be classified. You will grasp the three types of data and how to identify them. You will understand they are attribute/discrete data of two different types, nominal data, ordinal data, variable data and locational data. You will grasp how to make tradeoffs for each data type and using the SIPOC diagram for input data verses output data, and the manner special causes and common causes are treated.

Measure Phase -10-Standard Deviation and TAKT Time

Standard Deviation and TAKT Time. In this lesson, you will learn Standard Deviation Definition and its formula described in detail, and understand sigma, mean, variation, variance, spread and range. You will grasp takt tine Many measures help you understand performance and improvement needed, and these include Takt Time It tells you how quickly you need to action things in relation to customer demand. You will learn about the relationship between takt time and cycle time and how rework reduces the takt time in direct proportion.

Measure Phase -11- Measurement System Analysis Part ONE

Measurement System Analysis. In this lesson, the first of two parts, you will learn about measurement system analysis, and start by looking at measurement system capability analysis which is used to quantify how much observed variation is coming from the measurement system itself, and why it is the starting point of knowledge and improvement. You will understand Gauge R & R (spelt as Gage R & R in the USA). Here you will learn many elements of a good measurement system for gauges or instruments, such as resolution, accuracy, precision, repeatability, reproducibility. You will grasp measurement system analysis, and understand Gauge Repeatability and Reproducibility We use the data from measurement systems to make decisions If the measurement system contain errors, then this will lead to process error You will learn about the THREE potential errors elements in any measurement, human, system and environment.

Measure Phase -12-Measurement System Analysis Part TWO

Measurement System Analysis Part TWO. In this lesson, the first of two parts, you will learn about measurement system analysis and start by looking at measurement system capability analysis which is used to quantify how much observed variation is coming from the measurement system itself, and why it is the starting point of knowledge and improvement. You will understand Gauge R & R (spelt as Gage R & R in the USA). Here you will learn many elements of a good measurement system for gauges or instruments, such as resolution, accuracy, precision, repeatability, reproducibility. You will grasp measurement system analysis, and understand Gauge Repeatability and Reproducibility We use the data from measurement systems to make decisions If the measurement system contain errors, then this will lead to process error You will learn about the THREE potential errors elements in any measurement, human, system and environment.

Measure Phase -13-Types of Measurement Scales

Types of Measurement Scales. In this lesson you will learn the various scale types used for measurement, the eight feature situations and which scale would meet those criteria. You will understand the nominal scale, ordinal scale, interval scale and the ratio scale, along with their use and examples.

Measure Phase -14- Data Sample Size

Data Sample Size. In this lesson you will learn how to apply the data sampling size. The total data available is often referred to as the Population, and the obtained sample can help draw conclusions about the population (this is the total group of elements we want to study) You will understand how sampling is the process of selecting a small number of elements from a larger defined target group of elements. You will be able to determine how many samples you need, sample size factors to consider and their tradeoffs, and the four main types of sampling bias.

Measure Phase -15- Data Sampling Approaches Part ONE

Data Sampling Approaches. In this lesson, which is the first of two lessons looking at data sampling approaches. You will learn about data sampling approaches. There are different tools for analyzing population data than for process data – so be clear about which data you are gathering as process data give more information on trends, than population data – forming the foundation of process monitoring and control. You will understand that Data Sampling Approaches for processes are systematic sampling and rational subgrouping, whereas population sampling uses random sampling and stratified random sampling.

Measure Phase -16-Data Sampling Approaches Part TWO

Data Sampling Approaches Part TWO. In this lesson, which is the second of two lessons looking at data sampling approaches. You will learn about the two types of process sampling – systematic sampling (which is sampled data gathered in a systematic manner), and rational subgrouping (where a sample is chosen from a random starting point and then picking every nth element in succession from the starting frame).

Measure Phase -17- Measuring Distribution

Measuring Distribution. In this lesson, you will learn about data distribution and how to measure it, and that the Median can represent this distribution better than the mean, and that an ‘outlier’ is a recorded data point that is well outside the variation range of the rest of the data. Further, you will grasp how range is used and how distribution describes the relative likelihood of observing values for a variable factor You will learn how to determine the data location with three different measures of central tendency – the mode, the mean (a theoretical value, also called the average), and the median for attribute or category data, and continuous or variable data.

Measure Phase-18-Data Sampling Precision and Confidence

Data Sampling - Precision and Confidence. In this lesson, you will learn about Population Sampling Calculations, you will discover how do you determine the sample size and how large does it need to be. The larger the sample size, the more accurate the sampling results. You will learn result accuracy is determined by precision (how narrow you want your range to be for your estimate), where precision is equal to half the width of the confidence interval. You will learn how to do this and sampling for continuous data from detailed examples.

Measure Phase-19-Continuous Data Sampling

Continuous Data Sampling. In this short lesson, you will continue looking at continuous data sampling and learn two more detailed examples of its application. You will finish by summarizing sampling for continuous data.

Measure Phase -20-Discrete Data Sampling

Discrete Data Sampling. In this lesson, you will learn how to Sample for Discrete Data, and that attribute data/discrete data is binary, consisting of real numbers, and that variable/continuous data measures how well the outputs confirm to quality. You will grasp the formula for sampling discrete data and how it is applied to three examples, finally comparing discrete data verses continuous data.

Measure Phase -21-Data Collection Plan Part ONE

Data Collection Plan 1. In this lesson, you will learn that for Six Sigma Projects, data collection is done in the Measure Phase and is performed after you have completed your Ishikawa (Fishbone) Diagram and your "5 Why's", as you will then have a list of causes that you will want to collect the data from. The Data Collection Plan is there to collect the right data for your analysis, and it consists of six key pieces of data. Here you will learn the five steps to develop the plan.

Measure Phase -22- Data Collection Plan Part TWO

Data Collection Plan Part TWO. In this lesson, the second of two lessons, you will continue to learn about the data collection plan, but here you will focus on the four plan sections, the measure title, the operational definition, the collection frequency, the sampling method, the sample time and the time frame. You will understand that Data Collection means you must collect data of the Symptom or Effect (Y), PLUS their corresponding state of Causes (X’s) based on the breakthrough equation, and the data collection format including TAT – Turn Around Time.

Measure Phase -23- Business Statistics and Histograms

Business Statistics and Histograms. In this lesson you will learn how business statistics and histograms are used where descriptive statistics and enumerative statistics examine patterns that appear in the data of the variable and draw conclusions about that data. You will grasp the use of data such as mode, standard deviation, pie charts, scatter diagrams and box plots. You will learn about analytical statistics are used to get to conclusions about a specific sample data, and how inferential statistics are used to draw conclusions or inferences about population characteristics based on sample data. You will understand histograms (which are a specialized type of bar chart, and a very powerful tool to show how your process is performing), and the characteristics of skewness, Bi-Modal Curve, Truncated Curve, and Outliers. Finally, you will learn histogram creation with Microsoft Excel

Measure Phase-24-Probability Distribution and Central Tendency

Probability Distribution and Central Tendency. In this lesson you will learn how business statistics and histograms are used where descriptive statistics and enumerative statistics examine patterns that appear in the data of the variable and draw conclusions about that data. You will grasp the use of data such as mode, standard deviation, pie charts, scatter diagrams and box plots. You will learn about probability distribution and central tendency. You will grasp Measures of Central Tendency (Mean, Median and Mode), Skewness is the tilt in distribution and is undesirable if in excess. If shown graphically, represented by a Histogram, if mathematically, represented by a formula. You will understand that random variables can be either discrete or continuous, discrete variable probability distribution, and bi-modal distribution

Measure Phase - Analysis of variance - ANOVA

Measures of Dispersion. In this lesson you will learn how to use the four ways to measure dispersion which is the variation or spread in the data You will understand range (R) which is the simplest measure of dispersion, showing the difference between maximum and minimum values in a set, span, a measure of dispersion like range, in which the extreme 5% or 1% of the observations on either side are omitted, variance This is the average squared difference (deviation) of each data point from the mean. You will grasp that numerically; the variance is the square of the standard deviation (the average distance of all points from the mean) You will learn the uses of Measures of Dispersion, using parameters such as range, span, standard deviation, variance, etc, to determine the variation in a process, and determine the means for reducing that variation. You will learn how to improve customer satisfaction by reducing variation

Measure Phase-26-Measures of Distribution Part ONE

Measures of Distribution Part ONE. This is the first part of two lessons. In this lesson, you will learn about gaussian/normal distribution, or the Bell-Shaped Curve - these are probability curves that occur from natural phenonium, and that standard deviation as the “average distance from each data point to the mean”. You will grasp the process sigma value using a real-life example.

Measure Phase -27- Measures of Distribution Part TWO

Measures of Distribution Part TWO. This is the second of two lessons, where you will learn about the definition and three types of defects, defect opportunity, defective. Outliers are extreme values, occur in few numbers, and can be defectives. You will understand Kurtosis (represents the flatness/sharpness of the peak of a distribution) You will learn that a normal distribution has kurtosis of zero, however, there are different types of kurtoses including positive and negative, Leptokurtic, Platykurtic and Mesiokurtic. Finally, you will learn how to use Microsoft Excel for statistical functions.

Measure Phase -28- Confidence Levels and Intervals

Confidence Levels and Intervals. With data sampling, you will learn about the Confidence Level which is the degree of certainty in estimating the characteristics of a population using a set of samples and the trade-off of more samples than you need brings waste of time and cost verses less samples than you need brings not enough data about the full population. You will learn about the upper and lower limits of a band within which we find the actual value (true mean), and Z scores which is the number of standard deviations between a data point and its mean

Measure Phase -29- Box Plots (Whisker Plots)

Box Plots (Whisker Plots). Here you will learn that Quartiles are the values that divide a list of numbers into 4 equal intervals. The first quartile (Q1) marks the 25% point in your rank-ordered sequence – three quarters of the data are yet to come The third quartile (Q3) This marks the 75% point in your rank-ordered sequence – one quarter of the data is left You will learn how to apply the Interquartile Range (IQR) and the median, and from this create the structure of a box plot/whisker plot from raw data. You will learn from a real-life example, how box plots are applied, and to create a box plot in Microsoft Excel.

Measure Phase -30- Stem, Leaf Plots and Run Charts

Measure Phase -30- Stem, Leaf Plots and Run Charts. You will learn how the term “stem and leaf” is used to describe the diagram since it resembles the right half of a leaf, with the stem at the left and the outline of the edge of the leaf on the right. A stem and leaf plot of this data can be constructed by writing the first digits in the first column, then writing the second digits of all the numbers in that range to the right column You will understand how a Run Chart is concerned with showing how a process has been evolving through time with no regard to any target parameters, a Control Chart can give you a good overview of the current situation while taking some limits into account, and a trend chart visual tool helps find if there are any patterns in data over a period of time.

Measure Phase -31-Detecting Patterns in Run Charts

Detecting pattens in Run Charts. Here your will learn that a Run Chart identifies FOUR patterns that you will learn: Shift, Trend, Same Value, and Cycle. It is natural both in nature and processes, that the flow of data in chronological order should be random If the data is not random, then some pattern exists, and a Run Chart will show it. You will grasp how to detect patterns in run charts. A Run Table is a standard reference table to check for special causes in a run chart You will learn how to Identify patterns and counting the number of runs with a Run Table

Measure Phase -32- Assessing Process Capability

Assessing Process Capability. Here you will learn how to Understand Process Capability, and the measures used to assess process capability are called Process Capability Indices. You will understand the process capability formula These capability indices compare the process performance and variation to the CTQs and tell you how capable the process is of meeting the CTQs and how it needs to be centered. Process Capability Indices into Context and you will understand the levels of process capability.

Measure Phase -33- Statistical Process Control

Measure Phase -33- Statistical Process Control. Here you will learn both variable charts (These display data that’s been measured on a continuous scale, such as time, volumes, or amounts of money), and attribute charts (These track data that’s counted, or whether a particular characteristic is present, or right or wrong). For statistical process control, you will understand the X moving R or individuals chart, and the formula for the Moving Range part of the chart. When reviewing your progress, you will grasp the ideal situation, the threshold situation, the brink situation, and the chaos situation.

Measure Phase -34- Process Centering and Capability

Process Centering and Capability. Process centering and its capability where Cpk = Process Capability Index. Adjustment of Cp for the effect of non-centered distribution and is a measure of centering (off-centre). You will review the Cpk Levels of Process Capability. You will grasp and review the eight steps of process capability study and show examples of how you can improve Cpk relevant to its number.

Measure Phase – 35 – Long Term Process Capability

Long Term Process Capability

Measure Phase - 36 - The Z Transformation

The Z Transformation. You will learn what the Z Transformation is, and its Z Score (How many standard deviations can you fit between the process mean and its specification limit?). You will learn how to interpret Z-Scores and what to do for Non-standard data – The Z Transformation! You will learn from two detailed examples how to apply the Z Transformation and give you more examples of where problems are addressed by process capability.

Measure Phase Bonus Module Using Z Tables and Z Scores

Bonus Module - Using Z Tables and Z Scores. In this final bonus lesson within the DMAIC Measure Phase, you will learn from several examples of how to calculate the Z-Score Test. Sometimes you’ll want to know the area between the mean and some positive value, so you use the right-hand Z-table, and sometimes with the left-hand table. You will learn from three examples and the use of the Z table, with supporting extracts from the Z table itself.

Analyse Phase -05- Chi Square Degrees of Freedom

Analyse Phase -05- Chi Square Degrees of Freedom. First six sigma data types both quantitative and qualitative. Here you will learn how a chi-square test of independence is used to determine whether two categorical variables are dependent. Here, the degrees of freedom in - Chi Square equals the number of cells in the two-way table of the categorical variables that can vary, given the constraints of the row and column marginal totals.

Analyse Phase -06- Hypothesis Test Examples and Approach

Hypothesis Example and Approach. Here you will first look at using Hypothesis testing for a coin toss (more complex than you might think!), then, for a different example, you will apply first the one-tailed p-value, then the two-tailed p-value. You will then understand a more complex and real-world example of using a new drug trial and determine whether the drug is effective or not.

Analyse Phase – 07 – Hypothesis Test Types Part ONE

Hypothesis Test Type Part ONE. In statistics, you can never prove something RIGHT (because you can never be 100% sure) – but you can prove something wrong, If you want to prove a statement TRUE, you must state the OPPOSITE of it – and then prove that statement WRONG! Here you will learn the hypothesis test of means, the hypothesis test of variances, and the hypothesis test of proportion. You will then understand how the Hypothesis Test for Discrete Data works, and Hypothesis Test for Continuous Data works. Finally, you will contrast and compare discrete and continuous data differences.

Analyse Phase - 08 - Hypothesis Test Types Part TWO

Hypothesis Test Type Part TWO. Here you will learn how to test for continuous data or for discrete data for one, two, or more groups. You will understand the differences and application between parametric and non-parametric testing. You will then look at an overview of the six test types, the test for two variances, the one sample proportion test, Levene’s test, the two proportions test, the chi-squared test and the F-test.

Analyse Phase -09-Significance Level (Alpha)

Significance Level. Here you will learn how to apply the significance level (Alpha) which is the probability of rejecting the null hypothesis when it is true. To calculate the p-value, you need four pieces of data: the test statistic, the sample size, the hypothesis testing type (left tail, right tail, or two-tail), and the significance level alpha (α). You will then learn from a detailed example, of how to carry out the null hypothesis.

Analyse Phase -10- Test Statistics and P-Value

Analyse Phase -10- Test Statistics and P-Value. In this lesson you will learn how to apply the test statistic, P-Value, and Confidence intervals. You will grasp that a Test Statistic is a standardized value calculated from sample data – used to compute p-value, where hypothesis tests can decide whether to reject a null hypothesis or fail to reject a null hypothesis. You will understand the p-value and confidence intervals via a detailed example.

Analyse Phase -11- Critical Values and Distribution

Analyse Phase -11- Critical Values and Distribution. Here you will learn that The critical value is extremely important in terms of evaluating validity, accuracy and the range at which errors or discrepancies within the sample set can occur. You will learn how to compute the alpha value, the critical probability, calculate the elements of normal distribution and critical values, using the bell curve and hypothesis, region of acceptance, and significance level.

Analyse Phase -12 - Hypothesis Testing

Hypothesis Z score and the Two Tailed Test. Here you will learn that the one-tailed hypothesis test specifies a direction of the statistical test, whereas the Two-tailed hypothesis testing doesn’t specify a direction of the test. You will work through a detailed example using both. You will understand the Hypothesis Z-Score There are two ways you can determine whether your value falls in the region of rejection or not - both ways involve a test statistic. You will grasp use of the Z Table because once you calculate the z-score, you find the corresponding p-value in the test statistic table, which in this case would be the Z-table (used when standard deviation is known). Using a detailed worked example, you will grasp the Z-table application.

Analyse Phase -13- Z Test and Critical Value

Z Test and Critical Value. Here you will learn that a Z-test is any statistical test for which the distribution of the test statistic under the null hypothesis, can be approximated by a normal distribution You will understand that the Z-test is a parametric hypothesis test used to determine whether a sample data set comes from a population, and it tests the mean of a distribution. It assumes that the sample data comes from a population with a normal distribution and a known standard deviation. You will learn that the Z-test is a comparison of the means of two independent groups of samples, taken from one population with known variance, and sample is large (>30) You will grasp use of the Z Score from Alpha (Confidence Level), and two detailed calculated examples, and the critical value (from the mean of a standard normal distribution) is the point on a statistical distribution that represents an associated probability level.

Analyse Phase -14- Hypothesis Test Sequence Steps

Hypothesis Test Sequence Steps. Here you will first grasp an overview of the hypothesis test steps You can only prove Ho wrong, or not enough evidence to prove Ho wrong You can never accept Ho – the evidence will never be enough Hypothesis testing is a branch of statistics that specifically determines whether a particular value of interest is contained within a calculated range (the confidence interval) We assume the null hypothesis is true unless we have enough evidence to prove otherwise – if we can prove otherwise, we reject the null hypothesis if we reject the null hypothesis , then practically speaking, we accept the alternative hypothesis Hypothesis Test Procedures For Continuous data, hypothesis testing can detect Difference in Average and Difference in Variance For Discrete data, hypothesis testing can detect Difference in Proportion Defective The Hypothesise Testing Steps are, forming the hypotheses, decide on acceptable risks, collect data, select appropriate test, perform test and draw statistical inferences. The first two steps are considered here in this lesson.

Analyse Phase -15- Null Hypothesis Risks, Power, and Errors

Null Hypothesis Risks, Power, and Errors. When performing hypothesis tests there are TWO possible scenarios of errors - and these can lead to misinterpretation or incorrect decision-making. Here you will first grasp an overview of the Null Hypothesis risks and errors, you will learn there are two main error types, Type 1 Error (Excessive credulity), and Type 2 Error (Excessive scepticism). Next you will learn Why power matter in statistics, and how having enough statistical power is necessary to draw accurate conclusions about a population using sample data. A power analysis helps determine a minimum study sample size by determining statistical power, sample size, significance level, and expected effect size. You will then learn how to carry out statistical power analysis.

Analyse Phase -16- Test and Distribution Part ONE

The T Test and Distribution Part ONE. This is the first of two lessons on the T -Test and Distribution. You will learn how a T-test is commonly used to determine whether the mean of a population significantly differs from a specific value (called the hypothesized mean) or from the mean of another population T-distribution “relaxes” or “expands” the confidence intervals to allow for uncertainty of the mean estimate To determine whether the difference is statistically significant, the t-test calculates a t-value, (The p-value is obtained directly from this t-value), and you will understand the T -Test formula elements, and how the T-Test Noise and Significance is calculated.

Analyse Phase -17-Test and Distribution Part TWO

T Test and Distribution Part TWO. This is the second of two lessons on the T -Test and Distribution. You will learn how it is used when data are approximately normally distributed, (bell-shaped) but the population variance is unknown. The data should be normally distributed You will understand the T-Test Statistical Significance with two types of a distribution, frequency and probability. You will grasp how to decide how they differ in what they tell us about the data

Analyse Phase -18- Chi Square Tests Part ONE

Chi Square Tests Part ONE. This is the first of two parts looking at Chi Square Tests. You will learn why The Chi-Square χ2 distribution is the best method to test a population variance against a known or assumed value of the population variance and may be skewed to the right or with a long tail toward the large values of the distribution. You will grasp what the Chi Square test is used for (by examining the differences between categorical variables in the same population), and will learn from two detailed examples, a coin toss, and rolling a Dice.

Analyse Phase -19- Chi Square Tests Part TWO

Chi Squared Tests Part TWO. Here in part two of Chi-Square tests, you will learn Critical Values and the Chi-Square Table and understand how a chi-square distribution is calculated. The table is used for several applications such as the Goodness of fit test, and the Chi-square test You will be able to carry out Chi-Square Analysis probability with a detailed example, and how to calculate Chi-Square degrees of freedom.

Analyse Phase -20 - Hypothesis Testing Examples

You will learn how to conduct Hypothesis Testing A hypothesis test is a rigorous way of translating the observed result of a test into a statistical inference about the process or population you took the sample from. We express this inference as two mutually exclusive hypotheses You will be able to identify a test statistic You can also specify the alpha level value for the test, which is the risk of falsely rejecting the null hypothesis You will understand by two Hypothesis Testing Examples. Sometimes where you've been segmenting your process data, you need to know whether a statistically significant difference exists between data sets, even if you see a difference you may need to know how different your difference is, and whether the differences are real or just natural variation

Analyse Phase -21- Statistical Hypothesis

Analyse Phase –21– Statistical Hypothesis. Hypothesis is a statistical explanation to any phenomenon, and you will learn how it helps to establish a relationship between two factors, analysing improvement projects, assessing process design change impact, creating a predictive model to determine improvement benefits after a process change. You will understand with a Statistical Hypothesis Test example, Hypothesis testing tells us whether there exists statistically significant difference between the data sets for us to consider that they represent different distributions. You will grasp using a Proportions Test, that a p-value is a measure of the probability that an observed difference could have occurred just by random chance and then take that example and show it graphically.

Analyse Phase -22- T-Distribution Tests

Analyse Phase –22– T–Distribution Tests. Here you will learn The Students t-test uses t-distribution - useful when distribution is normal but samples are less than 30 (resembling normal distribution) When a team only has a small sample size and they want to find the difference between two means, they would use a t-test based on the t distribution (also known as student t distribution). The T-distribution table will show the critical values of the T Distribution. One-tailed 1-sample T-test You will understand the One and Two-tailed Tests that test for differences in both directions, whereas a one-tailed test can only assess one of those directions Two-tailed hypothesis tests are also known as nondirectional and two-sided tests because you can test for effects in both directions (positive and negative effects) Two-tailed 1-sample t-test, To interpret the results, simply compare the p-value to your significance level. You will grasp from an example, the Left-tailed, Right-tailed and Two-tailed Tests

Analyse Phase -23- One and Two Sample T Tests

Analyse Phase -23- One and Two Sample T Tests. You will learn how the One Sample T-test is used for continuous (measurable) data which should be a random sample from a normal population by a detailed example. The two-sample t-test is also known as the independent samples T-test, is a method used to test whether the unknown population means of two groups are equal or not, and you will understand the application with a detailed worked example of two turtle communities.

Analyse Phase -24- Paired T Test

Analyse Phase -24- Paired T Test. You will learn in a paired sample t-test, that each subject or entity is measured twice, resulting in pairs of observations, where common applications include case-control studies or repeated-measures designs. You will evaluate a company training program effectiveness by measuring the performance of a sample of employees before and after completing the program, then analyze the differences using a paired sample t-test. You will grasp how it is used to compare the means of two samples when each observation in one sample can be paired with an observation in the other sample.

Analyse Phase - 25 - Analysis of Variance ANOVA

Analyse Phase - 25 - Analysis of Variance ANOVA. You will learn that ANOVA (analysis of variance) is a type of hypothesis test which is used for comparing means especially when we have means for more than two groups. Note that when we have more than two groups, 2 sample t-tests cannot be applied ANOVA is based on an F distribution or a F test which is the short form of Fisher's test based on a family of distributions called F-distributions. This test is a widely used hypothesis tests because in most situations we may end up having more than two groups – so 2 sample t-tests may not be applicable You will understand ANOVA from a detailed worked example.

Analyse Phase - 26 - F-Statistic Degrees of Freedom and F-Test

Analyse Phase - F-Statistic Degrees of Freedom and F-Test. Analyse Phase –26–Degrees of Freedom and F–Test. You will learn how The F–Statistic is used to compare or perform a hypothesis test against the variances of two populations with a detailed example to show how to apply it (you will also get to learn the Degrees of Freedom (df), the use of its set of tables, and how to set up the F- Test (part of ANOVA) along with a detailed example.

Analyse Phase -27- Chi Square Test and 1 -proportion test

Analyse Phase-27-Chi Square Test and 1 -proportion test You will learn how to carry out the Chi Square test using two detailed examples, apply the proportions test (a type of a hypothesis test used for comparing proportions such as productivity levels, defect rates etc). The p-value tells you how often you would expect to see a test statistic as extreme or more extreme than the one calculated by your statistical test if the null hypothesis of that test was true and used for primarily comparing one observed proportion with its target proportion. Proportion hypothesis testing is applied to test an assumption for a population proportion, and you will grasp its application via two examples.

Analyse Phase -28- The Two Proportions Test

Analyse Phase -28- The Two Proportions Test. Here, you will learn how to carry out the two proportions test, is used when comparing the percentages of two groups, but it only works when the raw data behind the percentages is available since the sample size is a determining factor of the test statistics. You will examine a detailed example of two drugs, drug A and drug B. You will understand how to use Z using the pooled proportion to determine if Z falls within the two rejection regions, to see if there is a significant statistical difference.

Analyse Phase – 29 – Scatter Diagrams and Regression Analysis

Analyse Phase -29- Scatter Diagrams and Regression. Here, you will learn how to Scatter Diagrams/Plots A graphical tool that is used to analyse the relationship between two factors or variables Helps to identify the cause-and-effect relationship and the root causes to problems Used with correlation calculations and scatter plots to predict future performance based on past results You will learn of the use of simple, multilinear, and non-linear regression, weak and strong relationships, the direction of relationships, and a fitted line plot using a detailed example.

Analyse Phase -30- Regression Analysis

Analyse Phase -30- Regression Analysis Here, you will learn how to use Pearson's Correlation Coefficient (r), most often used to interpret a scatter diagram along with the visual interpretation, and being able to determine the relationship strength to calculate the Correlation Coefficient values. You will grasp how to calculate R Squared (Rsq) – Adjusted, which tells you the percentage of variation explained by only those variables that impact your dependent variable

Analyse Phase – 31 – Process Value Analysis

Analyse Phase -31- Process Value Analysis Here, you will learn how the objective of process value analysis is to find out what is the value that you are deriving from the process Process Value Analysis analyses every step of a process in detail and identifies "non-value" adding activities to be eliminated where the concept of value is centred around the customer Value adding are tasks are those the customer is willing to pay for You will understand the difference between Value-Added and Non-Value-Added

Analyse Phase – 32 – Steps to Reduce Waste ERSC Approach

Analyse Phase -32- Steps to Reduce Waste ERSC Approach Here, you will learn what the seven steps to reduce/eliminate wastes are, inventory, waiting, motion, transport, defects, over-production and over-processing. You will understand the steps to reduce waste using the acronym ERSC, eliminate, rearrange, simplify and combine.

Analyse Phase – 33 – Control Impact Analysis

Analyse Phase -33- Pareto and Control Impact Analysis Here, you will learn based on the selected metric, a count of problem occurrences or the magnitude is determined. A vertical bar is created with the highest column at the left, followed by columns of decreasing height, the chart view makes it easy to communicate rather than just numbers with regards to process improvement. You will grasp the tool via a detailed example. You will understand the control impact analysis as a prioritization tool that is used for selection and prioritization of causes after capture using the Cause-and-Effect Diagram and a detailed example is used to show its application.

Analyse Phase – 34 – Process Mapping and Box Plots

Analyse Phase-34-Process Mapping and Box Plots Here, you will learn the construction and use of the Ishikawa Diagram, “As Is” Process Mapping, a graphical representation of all the activities carried out to deliver output for a process, telling us all the activities and inputs being carried out to deliver the output. You will understand/review box plots and a detailed example, that summarize information about the shape, dispersion, centre of process data and also helps spot outliers in the data.

Improve Phase – 04 – Idea Screening Second Pass Tools and Pugh Matrix

Improve Phase -04- Idea Screening Second Pass Tools and Pugh Matrix. Here you will learn The Criteria -Based Matrix (CBM) is used for selecting one solution from a reasonably small list of options based on some important criteria The evaluation of each option is against a weighted criteria, and is an objective unbiased objective method for selecting the best solution You will understand that the Pugh Matrix is a prioritization matrix in which alternative options/proposals are compared against a standard, which can be the current solution that already exists, or a future goal/benchmark to compare and select the best and more valuable solution from a set of alternative proposals

Improve Phase - 05 - Design Of Experiments (DOE)

Improve Phase -05- Design Of Experiments (DOE). Here you will learn to define DOE also known as experimental design is a method of discovering and validating the effect of factors on responses in a process which uses a series of tests or experimental runs where input variables or factors are changed deliberately to observe how they affect the output variables or responses, and reducing manufacturing cost by minimizing process variation and reducing rework, scrap, and the need for inspection You will understand how experimentation improves processes and products by comparing alternatives, Identifying the Significant Inputs (Factors) Affecting an Output (Response), Achieving an Optimal Process Output (Response), Reducing Variability and noise factors and identifying correlation.

Improve Phase – 06 – Management of Risk Part ONE

Management of Risk. Improve Phase -06- Management of Risk Part ONE Here you will learn that a risk is measured by a combination of the probability of a perceived threat or opportunity occurring, and the magnitude of its impact on objectives, and that a threat is an uncertain negative impact on project objects, whereas an opportunity is the same but with a positive impact on objectives. You will grasp Risk Categories (useful after brainstorming), and the relationship between risk drivers, a risk event, and a project objective. You will understand the risk responses – share, accept, avoid, exploit, reduce enhance, transfer a threat and transfer an opportunity.

Improve Phase – 07 – Management of Risk Part TWO

Reactive Risk Management. Here you will learn what is a Reactive Risk Management Response, how to prepare contingency plans for a threat or opportunity These responses are usually associated with the accept option, preparing contingency plans (often referred to as a ‘fallback plan’, that is, what we will do if the original response does not work) states that we will accept the risk for now, but we will plan for what we will do if the situation changes. You will examine risk management tools such as the fishbone diagram, the risk breakdown structure, risk influence diagrams, strengths, weaknesses, threats and opportunities (SWOT). Finaly, you will understand the structure and use of the probability impact grid.

Improve Phase -08- Failure Modes and Effects Analysis (FMEA) Part ONE

Improve Phase -08- Failure Modes and Effects Analysis (FMEA) Part ONE Here you will learn FMEA is a risk prevention tool that helps you identify and prioritise potential opportunities for taking preventative action, and identifying the things that might go wrong, the failure modes, is the first step By identifying the failure modes, you can assess the impact of what happens (the effects )when it goes wrong, how often it is likely occur, and how likely you are to detect the failure before its effect is realised The FMEA Process identifies process risks, their impact on CTQ, real cause for their occurrence, and the mechanisms to detect them Root cause analysis is performed after something has gone wrong in your process FEMA is a proactive mechanism to identify what could go wrong in a process and that is why we use the term potential You will learn that for each of these potential events (failure modes) you assign a value, usually on a scale of 1 to 10, to reflect the risk, going on to calculate the RPN Scores (The severity score multiplied by the occurrence score multiplied by the detection score)

Improve Phase - 09 -Failure Modes and Effects Analysis - Part TWO

Failure Modes and Effects Analysis - Part TWO Improve Phase -09- Failure Modes and Effects Analysis (FMEA) Part TWO Here you will learn the one-to-ten severity rating scale representing the seriousness of the effects of the potential failure mode on the customer or business, Occurrence is the rating scale that represents the likelihood or frequency of occurrence of a specific cause. Detection is the rating that represents the ability of the current control mechanism to detect or report occurrences of the cause, and RPN is a composite risk rating calculated on the product of severity rating, occurrence rating, and detection rating. You will then calculate RPN Examples, where these three different scales act as a yardstick for us to prioritize and proactively mitigate (reduce) our risks

Improve Phase - 10 - Failure Modes and Effects Analysis - Part THREE

Failure Modes and Effects Analysis - Part THREE. Improve Phase -10- Failure Modes and Effects Analysis (FMEA) Part TWO Here you will learn the construction and use of a typical FMEA template, and the process step, potential failure mode, potential failure effects, a severity rating for each of the failure modes and the potential causes that triggered that failure mode. You will grasp the failure mode effect, the current process controls, who is responsible and the actions taken. If the RPN values haven't been reduced, it means your proposed action did not have any impact on the risks, the risk should then be reassessed with revised RPN values.

Improve Phase – 11 – Using Design FMEA (DFMEA)

Improve Phase -11- Using Design FMEA (DFMEA) Here you will learn Another commonly used application of FMEA is a new product development - called DFMEA. It is a method which is applied for a product instead of a process You will understand where Design FMEA is used in the lifecycle of a new product development, in a manufacturing kind of environment, or creation of a new service Design FMEA is used from concept creation up to pilot completion, to identify potential failure modes at each final products creation step and find how we can reduce them, it would be best to employ FMEA right at the design stage of new product development rather than using it in a product that’s already rolled out You will understand how some organizations look at what machines are used for manufacturing the final product, and whether they are good enough to deliver the final product as per design, so, they do a Machine FMEA to improve the quality of product as well

Improve Phase – 12 – Lean Concepts and Tools

Improve Phase - 12 - Lean Concepts and Tools Here you will learn and be reminded of the Five Key Principles of Lean, and to eliminate the Nine Kinds Of Wastes. You will grasp Lean as a philosophy focussing on shortening the timeline between the customer request and service delivery by eliminating waste. You will understand how Value Stream Mapping is used to capture all key flows of work, information and material in a process and important process metrics, and how it helps in identifying and visualizing improvement opportunities. It is used in both the DMAIC Measure and Improve Phases

Improve Phase – 13 – SMED and Poka-Yoke

SMED and Poka-Yoke. Improve Phase - 13 - Lean Concepts and Tools You will learn that the term SMED is an acronym for a Single Minute Exchange of Dies and is useful to drive Lean thinking. SMED is a quick changeover technique for dies in manufacturing, but they have great relevance to all sectors, as it can be used to reduce set-up or changeover times, and in manufacturing, smaller lots, and in services more frequent changes can be made possible You will grasp the goal of SMED is to complete as many steps as possible while the equipment is running (or processing), to save time and quickly change-over to processing the next product, and the main benefit of implementing SMED is reduced equipment changeover time, which creates a positive domino effect on other aspects of production You will understand Poka Yoke helps us to prevent occurrence of defects, by applying techniques that minimize errors, especially in more repetitive activities, and can be broken down into four types, information, sequence, grouped, and physical.

Improve Phase – 14 – Final Four Important Techniques

Improve Phase -14- Final Four Important Techniques Jidoka, Heijunka, Kaizen and TMP. Here you will learn how to perform the following techniques : Jidoka, sometimes called autonomation, meaning automation with human intelligence and machines and operators with the ability to detect when an abnormal condition has occurred and immediately stop work. Heijunka involves smoothing processing and production levelling and sequencing You will understand Muda: Japanese for waste, in any process, some steps add value, and some don’t, Mura: describes unevenness in an operation (e.g., speed up/slow down), Muri: means overburdening equipment in some way, Kaizen is an approach that focuses on continuous, simple, and small improvements to business processes rather than a few major and complex improvements or re-engineering initiatives You will grasp Total Productive Maintenance (TPM) is a maintenance management system where every employee, from top management to the production equipment operator, maximizes the effectiveness of the production system by preventing accidents, defects, and breakdowns.

Improve Phase – Bonus Content – Partial/Fractional Factorial Design + Handbook PDF

Bonus Content - Partial/Fractional Factorial Design + Handbook PDF. Improve Phase Bonus Content Partial Fractional Factorial Design Handbook PDF Here you will learn about DOE - fractional factorial design. This uses a series of tests or experimental runs where input variables or factors are changed deliberately to observe how they affect the output variables or responses; it also considers the settings or levels of each factor to analyse its effect on responses. You will grasp the elements such as factors, a treatment factor, a nuisance factor, levels and interaction.

Control Phase - 03 - Control Chart and Specification Limits

Control Phase - 03 - Control Chart and Specification Limits. Control Phase - 03 - Control Chart and Specification Limits. Control limits are derived from the normal distribution theory and are computed based on the three sigma limits on either side of the normal distribution curve. You will learn that Specification limits are set by the customers, whereas a control limit is computed from the performance of the process, and has nothing to do with the customers are the voice of the process The customer specification limits are decided by the customers, so we must ensure the process variation has been brought to a level to provide a buffer between the customer specification limits and the control limits, giving us leeway if there is a special cause of variation, to take control measures so the process comes back to control You will grasp the dynamics of a control chart through three detailed examples.

Control Phase - 04 - Continuous Control Charts

Control Phase – 04 - Continuous Control Charts. They are a basic tool for studying components of variation. First up, a reminder for you about the need to select the right control chart for a given scenario, which is dependent on the type of data that you are going to use for plotting the control charts, and the definition of discrete data verses continuous data. All continuous control charts will appear in pairs - one chart on the top and the other at the bottom You will learn about the I-MR chart (the Individual and Moving Range chart) Used when data is continuous and not collected in subgroups (collect a single observation at a time) You will learn the interaction between actual cause verses assumed cause, common causes and special causes. You will grasp real time process monitoring and rational subgrouping with a detailed example.

Control Phase - 05 - I-MR Charts and XBAR Charts

Control Phase - 05 - I-MR Charts and XBAR Charts. In this lesson you will learn how to use and apply three important charts: The I-MR Chart which provides process variation over time and helps to identify when the process goes out of control and indicates where to focus for source of assignable cause. The X bar R chart is used to monitor the process performance of continuous data and the data to be collected in subgroups at a set time periods This dual chart helps understand process stability and detects the presence of special cause variation The X Bar S-Chart is used when we have a subgroup size greater than 10 and are often used to examine the process mean and standard deviation over time.

Control Phase - 06 - Attribute and Discrete Charts

Control Phase - 06 - Attribute and Discrete Charts. Here you will learn the definition and attributes of five types of charts and their application. Attribute control charts are used for attribute data, which is the data that counts the number of defective items or the number of defects per unit A Binomial Distribution describes the probability of an event that only has 2 possible outcomes (E.g., Heads or tails) You will understand the difference between defect and defective, and the Poisson distribution (a discrete probability distribution stating the probability of a given number of events occurring in a fixed interval of time or space. Finally, you will grasp the four types of control charts that exist for attribute data both for Binomial and Poisson distribution.

Control Phase – 07 – P-Chart and NP Chart

In this lesson you will learn about the P Chart and the NP Chart. The P chart is also known as the control chart for proportions, and used to analyze the proportions of non-conforming or defective items in a process, using the binomial distribution to measure the proportion of defectives or non-conforming units in a sample The P Chart can be used to find out if your process is within control, however, the control limits are not constant, but they vary because the sample size is changing for every data point, (representing a value of proportions) The nP chart (chart for defectives) where the sample size is constant throughout the data collection and uses binomial distribution to measure the number of defectives or non-conforming units in a sample. You will grasp that the np chart plots the number of items, while a p chart plots the proportion of defective items

Control Phase - 08 - U CHARTS, C CHARTS AND EWMA.

U CHARTS, C CHARTS AND EWMA. Here you will learn that the U chart (also known as the control chart for defects per unit chart), monitors the count type of data where the sample size is greater than one, but it tracks the average number of defects per unit, and it assumes the underlying data approximate the Poisson distribution. Potential defects must be identified before data collection A C-chart (known as the control chart for defects) is again a chart in which defect data is plotted, but unlike a U-chart, the sample size remains constant, so the control limits are constant and a straight line The C chart is also known as the control chart for defects and used to monitor the number of defects in constant size units The EWMA – Exponentially Weighted Moving Average chart is used to monitor variables that use the entire history of a given output. It displays the data geometrically (discrete), so it can handle small and large values

Control Phase – 09 – CUMSUM/CUSUM

Cumulative Sum (CUSUM) Control Charts You will learn how the CUSUM chart is used to monitor the mean of a process based on samples taken from the process at given times (hours, shifts, days, weeks, months, etc.) CUSUM charts improve the ability to detect small shifts (i.e., less than 1.5σ) by charting a statistic including current and previous data values from the process It plots the cumulative sums of the deviations of the sample values from a target value. You will grasp the details and differences with three examples; you will learn how to make control chart choice and control chart data choice along with the formula for different control charts.

Control Phase – 10 – Process Control Plan Part ONE

Control Phase -10- Process Control Plan Part ONE. The Control Plan improves product quality by identifying the sources of variation in a process and establishing controls to monitor them. You will learn the six different approaches commonly used to make sure you establish some level of control in your process. They are audit mechanism, mistake proofing, inspection, auto monitoring, manual monitoring and statistical process control. You will understand pre-defined actions are to be taken when process goes out of control. You need to have a standard set of actions (reaction plan) that needs to be taken if a process parameter goes out of control (to avoid panic!). You will learn the six steps to implement the control plan.

Control Phase – 11 – Process Control Plan Part TWO

Process Control Plan Part TWO. Building on the previous lesson, you will learn the seven sections included in the control plan in the form of a template. The Control Plan (describes which process must be monitored, what parameter must be monitored within that process, and which includes the few important X’s and the Y) describes which process must be monitored, what parameter must be monitored within that process, and which includes the few important X’s and the Y The Reaction Plan describes if something goes wrong, what actions/responses must be taken. You will understand the tools to check improvement, such as the two-sample T Test, the Chi-square test, the Chi-Square χ2 distribution, ANOVA, and you will carry out a post improvement Process Capability assessment, using indices such as Cp, Cpk, and sigma (σ) capability are used to assess the improved process (as we did in the measure phase)

Control Phase – 12 – Closing DMAIC

Closing DMAIC. Project closure is the last step where the project team has standardized and documented the new process, created training and reference materials and established a plan for ongoing process and has delivered an improvement the team is now ready to transition and close out their DMAIC project and hand the process off to the Process Owner for the return to business as usual Realizing Financial Benefits. You will understand the seven Investment Appraisal Techniques, and the new processes and standards that have been created within this project must be documented, which includes the Project Charter, the root causes, tools that were used, the outcome of the analyze phase, solutions, FMEA, pilot results are all put together in the form of a project deck.

Lean Six Sigma Masterclass - Handbooks

Here, you will find all of the resources for the Lean Six Sigma Masters series.