Target Knowledge Sheets (TKS) Issue 1 · Presentation and reporting are necessary skills for the...

Level 4 Improvement Practitioner apprenticeship standard (Lean & Six Sigma Green Belt) Target Knowledge Sheets (TKS) Issue 1.2

Version 1.1 - Level 4 Improvement Practitioner apprenticeship standard (Lean & Six Sigma Green Belt) © 2019 United Centre of Excellence Limited

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CONTENTS

WELCOME .......................................................................................................................................................................... 3

TKSGB01 Compliance ......................................................................................................................................................... 4

TKSGB02 Team formation & leadership ............................................................................................................................ 5

TKSGB03 Presentation & reporting ................................................................................................................................... 6

TKSGB04 Project management .......................................................................................................................................... 7

TKSGB05 Change Management ......................................................................................................................................... 9

TKSGB06 Principles & methods .......................................................................................................................................10

TKSGB07 Project selection and scoping...........................................................................................................................12

TKSGB08 Problem definition............................................................................................................................................13

TKSGB09 Process mapping & analysis .............................................................................................................................14

TKSGB10 Data analysis – basic tools ................................................................................................................................16

TKSGB11 Basic statistics & measures ..............................................................................................................................17

TKSGB12 Process capability & performance ...................................................................................................................19

TKSGB13 Root cause analysis ..........................................................................................................................................21

TKSGB14 Experimentation & optimisation ......................................................................................................................23

TKSGB15 Identification & prioritisation ...........................................................................................................................25

TKSGB16 Sustainability & control ....................................................................................................................................27

TKSGB17 Measurement systems .....................................................................................................................................29

TKSGB18 Data analysis & statistical methods .................................................................................................................31


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WELCOME We are very please that you have got yourself a copy of the Target Knowledge Sheets (TKS) which will help you guide your training material to assist with the End Point Assessment of your learner. We expect that you are keen to get on with your job at hand so that we will keep the welcome introduction brief. First, we would like to draw your attention to the terms and conditions of usage. It’s a condition of printing these Target Knowledge Sheets that you agree to the terms and condition of usage. These are available to view at www.UCE.org.uk. Essentially, we want to help learners/training providers get through their exams and end point assessment. If you are a learner and you are using TKS or TWS for your own use only, you will have no problem complying with our fair use policy. You will, however, need to get our written permission in advance if you want to use TKS as part of a training programme that you are delivering. WARNING! These Target Knowledge Sheets (TKS) are not designed to cover everything in the syllabus! They are designed to help you assimilate and understand the most important areas for the exam as quickly as possible. If you study from these TKS only, you will not have covered everything that is in the Improvement Apprenticeship Standards; this is the accountability of the training provider. The training provider will be provided with the following:

• UCE set of Target Knowledge Sheets (TKS)

• UCE set of Target Work Sheets (TWS)

• UCE Target Portfolio Kit (TPK)

• UCE web e-portal link to various external web links and supporting material Our portfolio of materials is as follows:

TKS (Knowledge) TWS (Skills) TPK (Behaviour) The Target Knowledge Sheets… Will provide a base understanding of the most critical areas of the syllabus the training provider will train according to the Apprenticeship Improvement Standard.

The Target Work Sheets…Will provide a primary example and framework to test the knowledge of a particular tool.

The Target Portfolio Kit…will provide guidance on how to create your project report and an example of presentation & questioning. The portfolio kit will include example questions in preparation for the leaner's multiple-choice exam.

To maximise the chances of success for the learner in the exam, we recommend the trainer to visit the UCE website on www.UCE.org.uk where they will be able to access additional free resources to help them support their learners.

http://www.uce.org.uk/


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TKSGB01 Compliance

What is it? Staying up to date with legislation is a crucial part of effective compliance. The HSWA (Health and Safety at Work Act) is the primary legislation governing health and safety at the workplace in the UK and protects the health, safety and welfare of employees at the workplace. The Act outlines the employer’s general duties to their employees and establishes the role and obligations of the Health and Safety Executive (HSE), as well as setting out the how secondary health and safety regulations and approved codes of practices are to be implemented.

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Why do you use it? HSE Compliance is a service for management and assessment of Health & Safety and Environmental regulatory compliance. Regulations are needed to protect people at work, but to avoid unnecessary burdens on business it is important to strike the right balance. The main aim of HSE Management is to reduce a company’s risk of litigation. This is achieved by making sure the company complies with relevant regulations and by helping reduce the number of accidents occurring in the workplace. Health and safety at work is one of the fundamental British values according to Ofsted; the rule of law, the values are:

• Democracy

• The rule of law

• Individual liberty

• Mutual respect

• Tolerance of those with different faiths and beliefs and for those without faith.

All these values are vital when considering team development and continual improvement.

When do you use it? • In general, health and safety laws apply to all businesses. As an employer, or a self-employed person, you are

responsible for health and safety in your business. Health and safety laws are there to protect you, your employees and the public from workplace dangers.

• It is worth considering, prior to any business improvement activity or change to the working environment, what effects that potential change could have on health and safety and as such identify what risk assessments need to be conducted

How do you use it? • The approach you take should be proportionate to the size of your business and the nature of your business activity.

For most small, low-risk businesses the steps you need to take are straightforward. If you have fewer than five employees you don’t have to write down your risk assessment or your health and safety policy.

• The identification of appropriate health and safety measures are worth considering and how the impact will be communicated.


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TKSGB02 Team formation & leadership

What is it? Decision making techniques are an important part of team formation and leadership. The decision-making process and method used is essential for the success of the business improvement activity. It is a requirement of the standard that when a team are considering a major decision that the following methods are considered by the leader in order to achieve success:

• Consensus decision making is where all the team members have an opportunity to air their opinions as to the best course of action

• Authority rule is when a decision is predetermined and cannot be deviated

• Majority rule is a democratic decision-making method

Why do you use it? Any of these methods of decision making are used in order to drive a successful outcome for the business improvement project.

• Consensus decision-making is a creative approach of reaching agreement between all members of a group. It breaks down hierarchical barriers within the team and promotes shared power and ownership.

• Authority rule can either be with or without discussion but is usually implemented when there is no room for discussion such as implementation of legislation.

• Majority rule is used when decisions need to be democratically reached in a timely manner.

When do you use it? • Consensus decision-making is used when groups need to collectively take control over decisions that affect the whole

team. It is method of making better decisions as the whole group buy-in to the potential solution and is a driver for achieving the success of the project.

• Authority rule is used when there is no alternative to use any other method

• Majority rule is used where more practical and quicker decisions are required

How do you use it? The decision-making methods for consensus should be used based on the circumstances of the individual project and should follow a similar decision-making process such as:

• Identify the Discussion

• Identify the Emerging Proposal

• Identify any unsatisfied Concerns

• Collaboratively Modify the Proposal • Assess the degree of Support

• Finalise the Decision or circle back to step 1 or 3


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TKSGB03 Presentation & reporting

What is it? Presentation and reporting are necessary skills for the business improvement practitioner. With respect to this standard presentation and reporting includes reporting templates, message mapping and case for change. The reporting templates refers to the type of medium the project message, or the improvement idea are presented on, within the organisation. Message mapping can be a useful technique for effectively planning and preparing communicating in order to clearly explain the situation, risks and remedies during presentations. The case for change is the method for clearly and effectively communicating to the organisation the need for change in a presentation or reporting format.

Why do you use it? Standardised reporting templates, presentation development techniques and standard presentation and reporting methodologies are used to professionally deploy the message for explaining the improvement project. These can be deployed at the start of a project to identify the need for change to get buy-in for the project. They can be used throughout the duration of the project to provide project reviews and finally at the end of projects to clearly demonstrate the improvements made.

When do you use it? • At the start of a project to create a compelling business case

• Throughout the business improvement project at various review stages

• At the end of the project

How do you use it? • Identify a suitable template method for reporting

• Employ suitable presentation delivery skills to ensure the method is communicated

• Use a standard approach for presenting the project


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TKSGB04 Project management

What is it? Project management is the application of processes, methods, knowledge, skills and experience to achieve the project objectives. A project is a unique, temporary activity, undertaken to achieve planned objectives, which could be defined in terms of outputs, outcomes or benefits. A project is usually deemed to be a success if it achieves the objectives according to their acceptance criteria (Quality), within an agreed timescale (Time) and budget (Cost). It is an extension of detail with respects to the requirements of the Improvement Technician standard standards.

The specifications of the standard fall within and across these boundaries:

• The business case captures the reason and justification why the project has been chosen

• Risk analysis and management is a tool that is used at each stage of the project management process and analyses the risks that could occur such as financial, health and safety, completion date, availability of resources as examples.

• Tollgate reviews are used between each project phase, it provides specific checkpoints to ensure all elements of the previous stages have been completed.

• Work breakdown structure (WBS) and Network diagrams help to evaluate the viability of the project breaking down the deliverables so that all the things needed to accomplish the project can be visually and graphically displayed.

• Lessons learned are traditionally assigned to the end of the project however it is important to keep lessons learned in mind throughout the whole project.

• Pilot studies are conducted to reduce risk on projects and are usually used at the initiation phase of the project.

• Project reviews can be introduced at each phase of the project, not just at project closing. It helps identify whether the project is currently timely and delivering to budget, it ensures deliverables have been produced and approved, risks have been controlled and mitigated, issues identified and resolved and any changes have been managed.

• Process management and measures occur across the whole project and are a set of clearly defined, actionable and measurable goals that help identify project progress.

• Benefits tracking is used throughout the project and is the identification, definition, planning tracking and evaluation of business benefits

Why do you use it? Project management from start to finish requires a full range of tools that allows you to define a plan or business case through to lessons learnt.

• Project management is conducted to help bring order to a project. A clear constructive pathway will help reduce chaos and ensure targets and goals are achieved.

• Carrying out project management reduces the risk of delays and cost to an organisation

• It encourages team work and aids the decision-making process

• Utilises resources in the most efficient manner

• Helps integrate across all areas of the business

• Helps keep the focus on quality

• Shares best practices across the organisation and helps create new knowledge that could further improve the organisation


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When do you use it? • Typically, project management should be considered for unique one-time activities that require a team effort to

complete the task. The importance of the project to the organisation should be an important criterion as well as organisational reputation. If a problem is unfamiliar or high-risk project management techniques are employed.

How do you use it? • Identify the appropriate tools at each of the process steps for project management. There are many tools and

techniques that can be applied but the standard for Improvement Practitioner requires understanding of business case, risk analysis and management, toll-gate reviews, work breakdown structure, lessons learned, pilot studies, project review, process management and measures and benefits tracking.


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TKSGB05 Change Management

What is it? Change management is a methodology that shows us how we prepare, provide for and support individuals to successfully adopt change in order to ensure organisational success. Stakeholder identification, analysis and management (RACI). Change curve, resistance characteristics, change sponsorship, compelling point of view are some of the areas to be covered.

Why do you use it? Sponsors need to be change leaders and not just managers. While managers focus on planning and short-term horizons, devise processes and structures and solve problems, sponsors establish direction for the future, communicate through vision, and forge aligned, high-performance teams, they surface and manage resistance through positive and negative reinforcements (see TKSYB05 Change Management). The Stories compel people to change. Therefore, building a compelling narrative or point of view for change will help the sponsor and executive team assess what the impact the change will have on the organisation, business or process.

When do you use it? • All of these specifics are used in conjunction with one another and should be implemented at the onset of a strategy.

• Stakeholder identification is necessary at the onset of the improvement project to minimise resistance to change.

• An understanding of the behaviours of change shown on the change curve models and an understanding of resistance characteristics are useful soft skills to know prior to considering any change management initiative.

How do you use it? • Stakeholder identification: Consider a stakeholder map, one on one meetings to engage them with the change.

• Analysis and management (RACI): Identify in your team who is Responsible to doing the task, who is Accountable for the results of the task, who needs Consulting or who needs to be Informed about the task at hand.

• Change Curve: There are typically seven emotions in the change curve that must be managed, (1) Shock/Anxiety, (2) Denial, (3) Frustration, (4) Depression, (5) Experiment, (6) Decision and (7) Integration.

• Resistance characteristics: Resistance to change reside in basic human characteristics such as perceptions, personalities & needs. Some of the internal characteristics include, Fear of loss, security, Status quo and peer pressures. External characteristics include, resource constraints, threat to expertise, politics,

• Identify key sponsors for the change and for them to champion any resistance to change and have options to deal with the potential disruption.

• Ensure a compelling point of view with the urgency for change with the control on how teams feel, think, act and behave.


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TKSGB06 Principles & methods

What is it? They are principles & methods to reduce waste (Lean focus), solve problems (Six Sigma focus) and thereby improve operational performance through reduction of variance, fewer defects and more efficient processes. The Business value of Lean and Six Sigma improvement methods, which include the 8 Disciplines (8D), practical problem solving (PPS), Define Measure Analyse Improve Control (DMAIC), and for complex engineering Design for Six Sigma (DFSS) can all be used to improve operational performance.

Why do you use it? These methods provide, team-oriented approach to solving critical problems in processes. The goals of 8D are to find the root cause of a problem, develop containment actions to protect customers and take corrective action to prevent similar problems in the future, this is the same for PPS, where as you grasp the situation, contain it and then break it down to find the point of occurrence allowing you to carry out a cause and effect analysis to identify the root cause. Then apply countermeasures, follow-up and checks before standardising the process. DFSS is about designing new processes whereas DMAIC is well-known and the most used system for improving existing processes in a continuous effort to reduce defects.

When do you use it? • PPS: There are many tools within practical problem solving which can be used as standalone tools however the method

of PPS is used on simple or repetitive problems to solve immediate problems in order to achieve goals

• 8D: This methodology is designed to find the root cause of a problem, devise a short-term fix and implement a long-term solution to prevent recurring problems. When it’s clear that your product is defective or isn’t satisfying your customers, an 8D is an excellent first step to improving Quality and Reliability.

• DMAIC: Use this method when improving a current process, if the problem is complex or the risks are high, DMAIC should be the go-to method. Its discipline discourages a team from skipping crucial steps and increases the chances of a successful outcome.

• DFSS: This method is intended for use when you must replace a product instead of redesigning. When the current product or process cannot be improved to meet customer requirements.

How do you use it? • PPS: This methodology is meant to be simple and recorded on a A3 sized paper it enables problem solvers to capture all

the relevant information on one page. It is purposefully designed to ensure the approach is concise. If it cannot be conveyed on one page of A3 the solution is likely to be unclear.

• 8D: The process, including the use of tools such as: Ishikawa/ Fishbone, Affinity Diagrams, Is / Is Not, Process Flow and Comparative Analysis are combined in the structure of using the 8D methodology.


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• DMAIC: Each stage has a different set of tools which measures the current state and increases the performance of the business process to a new and statistically significant improved state using statistical tools. Before beginning any Six Sigma improvement project, it is necessary to select a process that, if improved, would result in reduced cost, superior quality or increased efficiency. The process also must possess measurable data because what you cannot measure you cannot improve. The process selected may currently be experiencing quality problems or generating a large amount of scrap, so you must Define, Measure, Analyse, Improve and then Control.

• DFSS: This is more of an approach to product design rather than one methodology. There are some fundamental characteristics that each of the methodologies share. The DFSS project should involve a cross functional team from the entire organisation. It is a team effort that should be focused on the customer requirements and Critical to Quality Characteristics (CTQCs). The DFSS team should invest time studying and understanding the issues with the existing systems prior to developing a new design. There are multiple methodologies being used for implementation of DFSS. One of the most common techniques is DMADV (Define, Measure, Analyse, Design, Verify).


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TKSGB07 Project selection and scoping

What is it? The project selection and scoping elements require understanding of business score cards for translating business strategy into operational objectives. In order to identify the outputs of the score card cascade at each section it is necessary to identify the input variables. The business scorecard is a framework for cascading the business strategy to each level of the organisation, providing metrics in the form of KPI’s, all of which link to the goals of the business. Sometimes referred to as a transfer function Y = f(X), it shows how the dependant variable ‘Y’ is affected by the independent input variables ‘X’ or root causes. It simply states that Y is a function of all the input variables. Some of those input variables will have a greater impact on the output ‘Y’ than the others. The business scorecard metrics will be influenced by this relationship, Y = f(X) and it is important to identify the vital few input variables that have the greatest impact on the output ‘Y’ of that metric. Y being the problem that is wanting to be fixed.

Why do you use it? It is a relationship that is used to show how the vital few inputs into a process have a marked effect on the dependent output variable, as such it is an important equation with regards to understanding the concept of problem solving. With regards to business score or balanced score cards this equation helps to define the key independent variables that are impacting the business and provides the most efficient way of resolving the problem ‘Y’

When do you use it? • As part of any problem-solving methodology

• Used at each phase of the DMAIC improvement model, from defining the problem through to control phase

How do you use it? • Define the problem to be measured the ‘Y’

• Identify the root causes and measure them - the X’s

• Analyse the effect or impact of the root causes – the X’s

• Implement solutions to the root causes and check the output ‘Y’

• Once the improvement solution has been implemented continually check the output to ensure the improvement has worked – the ‘Y’


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TKSGB08 Problem definition

What is it? With respect to the Practitioner Standard the problem definition is centred on the Cost of Poor Quality and problem analysis models such as Is/Is not charts. Cost of Poor Quality (CoPQ) is the Failure cost’s part of the Cost of Quality Model which refers to the Prevention and Appraisal costs. CoPQ are those costs that are created from producing defective products or processes. They can also be associated with the cost of lost opportunity as a result of having to rework a problem including labour, equipment being used to rectify the problem, parts and materials. A ‘Is/Is Not’ chart is a problem analysis model that defines and bounds a problem such as Cost of Poor Quality in order to decide the scope and range of what needs to be considered at that point in time

Why do you use it? Within a project CoPQ will be used to identify the financial impact to the business and help to write a clear problem statement. It helps to identify the cost of failure both internally and externally to the business, using problem analysis tools to identify CoPQ will help determine better solutions. It will also allow you to think about the less visible failures in the process. The problem definition should describe an undesirable gap between the current-state level of performance and the desired future-state level of performance. The problem definition should include absolute or relative measures of the problem that quantify that gap with visibility of CoPQ but should not include possible causes or solutions.

When do you use it? • Cost of poor quality can be identified throughout each phase of the improvement project using appropriate problem

analysis tools. It is commonly identified within the define stage when initiating a problem statement for the improvement activity.

How do you use it? • Define the operational definitions for each KPI being monitors against internal and external costs

• Identify the priorities, use Y = f(x), the most likely cause of CoPQ

• Distinguish any symptoms from the causes, if the cause is known developing a list of solutions is appropriate using various brainstorming techniques and then define the problem definition.


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TKSGB09 Process mapping & analysis

What is it? Mapping provides a visual way for the team to communicate how products or services flow through a process, it is a graphical representation identifying the steps of the process including inputs and outputs and data. It allows for the identification of waste, what and where to measure and analyse and provides opportunities to create improvements. Various types of process maps and analytics are available

Why do you use it? • Obtain clarity of the physical flow of the process and encourage process improvement within the team

• To facilitate an understanding of the process, the way it should be, the way you think it is, the way it actually is

• Identifies the location and degree of waste, bottlenecks, value and non-value added process steps

• Identifies common elements for measuring and monitoring the process

• Map at a level necessary to address the root cause of the problem

• To identify the key inputs and process steps that affect the outputs

• To gain insight into potential causes of problems, prevent problems, predict future performance

• To describe the future state

When do you use it? • In the define stage we identify the process, the boundaries around the process and create a high level map or SIPOC

• In the measure phase we develop a detailed process map where we go and walk the process

• To describe opportunities for improvement and create a future state map

How do you use it? • A process is a sequence of activities or steps or tasks that take input variables (x’s) and transforms them into output

variables (y’s) to deliver value to the customer often described as the big Y. Processes include physical and data flows, formal and informal steps.

• There are many levels of a process including strategic level, business level, high level process and the detailed process map

• The amount of detail you include in a process map depends on what you require from it, too simple and important process steps are not visible, too much detail and the process map becomes difficult to interpret

• There are many different types of process maps including top down, swim lane, spaghetti. Value stream mapping (VSM) takes process mapping to a level where is can be used as a strong analytical tool for process improvement

Top down chart: A high level chart which is expanded from the highest level down to root cause level, start and end points are identified in the SIPOC Swim lane chart: Often used for large complex processes when there are multiple department and functions involved, time sequence is important Product or service focus: If there are many different products or services that flow through a process the scope will need to be narrowed, focus on what service or product are you going to map, eg mortgage applications and student loans are two different processes


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VSM (current state): Begin with the customer at the end of the process and work backwards

o Add process activity boxes including any rework loops o Identify the outputs and inputs for each process activity o Classify inputs as critical, noise or controllable o Identify any operating or target specifications o Add material and information flows o Add data collection boxes, add process and lead time data, defects, efficiency, takt rate etc. o Identify value added, business value added and non-value added steps o Identify any other types of waste o Add a time line at the bottom of the process

• Verify current state map with the process owner

• After solutions have been agreed and implemented the future state map can be finalised VSM (future state): The current state map looks essentially at what currently happens, the future looks at how things should be carried out, to develop and sustain the future map we should consider some of the following tools and concepts Capacity: Capacity is the maximum a process can deliver, any process step that creates a delay is a time trap, any time trap that is unable to meet customer demand is a constraint. Time traps limit the output of the process, there is always a time trap in a process. Causes of time traps should be investigated e.g. poor process flow

• The theory of constraints is a way to manage bottlenecks and improve process flows. The throughput of any system is determined by one constraint the bottleneck, to increase throughput of the process focus on the bottleneck or the constraint Process balancing: This is designed to take a set of process steps and balance or equalise them, the process will have a takt time established and all tasks will be required to be balanced and completed within that takt time. Process steps are made up of value and non-value added steps and opportunities will be identified during the process mapping

Takt rate and takt time

• Takt rate is the number of units that needs to be produced over a period of time to meet customer demand

• Takt time is the rate at which a product needs to be completed in order to meet customer demand. Kanban: The Kanban approach is a stock management method that allows production on demand. The objective is a balance between production and demand. The most downstream process step only produces to meet customer demand and sends a signal upstream to produce

Overall equipment effectiveness (OEE)

• OEE identifies the percentage of manufacturing time that is productive. The calculation is based on availability, *performance *quality*100%

• Availability relates to lost time from breakdowns and waiting

• Performance relates to lost production due to stoppages, reduced running speed or adjustments

• Quality relates to the ratio of good to poor quality product due to scrap, rework and losses


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TKSGB10 Data analysis – basic tools

What is it? There are several spreadsheet and pivot table analysis tools on the market. Statistical Analysis software has become a popular tool used by many organisations.

Why do you use it? To organize data and predict future trends based on the information, many organisations rely on statistical analysis. While organisations have lots of options on what to do with their big data, statistical analysis is a way for it to be examined, as well as broken down into individual samples. The software, which is offered by a number of providers, delivers the specific analysis an organisation needs to better their business.

When do you use it? • To describe the nature of the data to be analysed.

• To explore the relation of the data to the underlying population.

• To create a model to summarise and understand how the data relates to the underlying population.

• To prove (or disprove) the validity of the sample model. • To employ predictive analytics to anticipate future trends.

Statistical Analysis software and services providers, defines statistical analysis as the science of collecting, exploring and presenting large amounts of data to discover underlying patterns and trends. Local analysis of simple data can be done using several spread sheet and pivot table analysis tools (e.g. Microsoft Excel)

How do you use it? While not a cutting-edge solution for statistical analysis, MS Excel does offer a wide variety of tools for data visualization and simple statistics. It’s simple to generate summary metrics and customizable graphics and figures, making it a usable tool for many who want to see the basics of their data. As many individuals and companies both own and know how to use Excel, it also makes it an accessible option in getting started with statistics using pivots tables with data. The Minitab software offers a range of both basic and advanced statistical tools for data analysis. There are a range of different software tools available, and each offer something slightly different to the user – what you choose will depend on a range of factors, including your research question, knowledge of statistics, and experience of coding. These factors could mean that you are at the cutting-edge of data analysis, but as with any research, the quality of the data obtained is reliant upon the quality of the study execution. It’s therefore important to keep in mind that while you might have advanced statistical software (and the knowledge to use it) available to you, the results won’t mean much if they weren’t collected in a valid way.


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TKSGB11 Basic statistics & measures

What is it? Data is collected to learn something about the process, we can collect all the data generated (the population) or we can take a sample of the data and make an inference about the population. Data are measurements or observations we use to describe, optimise or control the process

Why do you use it? • Establish priority and focus • Quantify a baseline

• To identify sources if variation within a process

• Verify cause and effect relationships

• To quantify the impact of the process improvements

• To control the processes over time

When do you use it? • All stages of process improvement after define

• To monitor process performance

• To inform when to take action

How do you use it? Control charts

• Control charts are designed to help detect special cause variation, process stability, changes in the process and identify areas for improvement.

• Different control charts are available to fit different types of data and calculations for control limits are based on the appropriate statistical distribution

• Common cause variation is always present and is the collective influence of many small causes, special cause variation indicates a significant change has taken place in the process

• When the process remains inside the control limits and shows random or common cause variation it is considered in statistical control, stable and predictable.

• Data should be collected over an appropriate duration and recorded in time sequence

• Control limits are different to specification limits

• The goal is to achieve a stable process that has minimal variation

Continuous data control charts • Consisting of the individual and moving range (IMR) used for sample subgroup size of 1 and the X-bar & range (X-bar R)

and , X-bar & standard deviation (X-bar S)

• An important consideration for X-bar R&S is rational subgrouping and an appropriate sample size (for central limit theorem purposes)


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Attribute data control charts

• Binomial distribution charts are used to classify an item as defective or not defective (pass or fail), the P chart is used to plot the proportion defective in a subgroup and the NP chart is used to plot the number of defectives in a subgroup

• Poisson distribution charts are used to classify the number of defects in a sample or unit, the C chart is used to plot the defect count per same sample size and the U chart is used to plot the defect count in a varying sample size

Testing data for normality

• Statistical tests for continuous data often assume that the data set is normally distributed. The normality test transforms the data from a normal distribution to a best fit straight line which can be visually checked and supported with a statistical test

• Any data points that do not follow the straight line should be checked and a decision taken on how to manage

Managing non normal data

• Not all data follows a normal distribution. Normal data is required for many statistical tools such as process capability, t-tests and anova. If the test for normality indicates non normal data there are a number of steps that can be followed

o If outliers are causing the normality test to fail can we identify, explain and remove the data points o Has the data been collected from more than one process, if yes then the data should be stratified o Is there insufficient data discrimination in the measuring o Is the data from a subset that has been sorted and rationalised, how normal is the original data set o If a process has many values close to a natural limit this may cause the distribution to be skewed, transforming

the data may assist in making the data set normal o There are many data sets that naturally do not follow a normal distribution, in these situations a non-normal

distribution should be used, eg Weibull, log normal, exponential, passion and binomial o Use a statistical test that does not require normal data eg mann-whitney, moods median test


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TKSGB12 Process capability & performance

What is it? Process capability is a measure of how well a given process is able to produce products or services that conforms to specifications and allows you to determine units that are in and out of specification. Capability is determined by comparing process spread to the specification spread. Customers, managers or industry standards typically set targets and specification limits. Data is required to be statistically stable before capability is assessed. Capability indices are available for continuous, non-normal and attribute data.

Continuous data: Capability is defined in terms of defects under the curve outside of the specification limits Attribute data: Capability is defined in terms of Pass / Fail (Good or Bad)

Why do you use it? • To determine if a process is capable of producing goods or services that meet customer requirements

• Shift focus from meeting customer requirements to reducing variation relative to customer requirements

• Perform on an existing process to establish a baseline, determine the extent of improvement required, determine if an improvement has occurred and used to confirm sustainability of improvements

• Perform periodically as a means of monitoring process performance

• Compare the performance of different processes

• Determine cost of poor quality

• Make decisions on the process, do nothing, change specifications, centre the process, reduce variability of accept the losses

When do you use it? • It is necessary to understand how the process performs in comparison to a target, an upper or lower specification limit

or specification limits

• Identify process improvement opportunities

• There is no special cause variation present and sufficient data has been collected over a period of time using an adequate measuring system

• When data is continuous, non-normal or attribute

How do you use it? Continuous data

• Data must be collected using an adequate measuring system and data collection plan, consideration should be given to which factors are used for the capability study, it is possible to adjust those factors, over what time period data should be collected, can data be time ordered, what sample size and sub grouping is required

• Where possible long term data should be collected, common approach is small sample sub groups repeated over time to reflect longer term variation

• Data should be in statistical control and come from a normal distribution, otherwise results could be misleading

• Verify target, USL and LSL values are correct

• Calculate Cp to determine how the process is performing relative to the USL and LSL, Cp does not require the mean and shows how the process could perform if centered. Appropriate where a mean can easily be adjusted or is monitored. Typical goals for Cp are > 1.33 (higher if safety related)


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• Calculate Cpk to determine how the mean of process is performing relative to the USL and LSL, Cpk is the lower of the values and will identify how far the process is off target. Appropriate where the mean cannot be easily adjusted. Typical goals for Cpk are > 1.33 (higher if safety related).

• Action plans should be developed where Cpk < 1.0 and monitoring where Cpk <1.33

• Cp and Cpk is calculated from short term StDev and is referred to as potential capability, Pp and Ppk is calculated from long term StDev and is referred to as overall capability. Industry commonly uses Cp and Cpk.

• Calculations are identical except for the StDev but differences between Cpk and Ppk should be investigated

• A short term study is influenced by random causes, a long term study is influenced by all causes.

• In addition to the capability indices PPM and DPMO should be calculated, Z value if required

• Process capability can be converted to a sigma level, caution should be taken with stating the sigma shift

Attribute data: defective data

• Use capability analysis for defective data with a binomial distribution

• Binomial capability analysis examines the proportion of defectives for each sample


• Data should be in statistical control

• Calculate and review the % or proportion defective, PPM defective, DPMO and Z value

• Process capability can be converted to a sigma level, caution should be taken with stating the sigma shift

Attribute data: defect data

• Use capability analysis for defect data with a poison distribution

• Poisson capability analysis examines the count or number of defects per unit of measure

• Use capability analysis for defective data with a binomial distribution


• Calculate and review the mean DPU


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TKSGB13 Root cause analysis

What is it? Root cause analysis (RCA) is a method of problem solving used for identifying the root causes of faults or problems, by selecting and applying the appropriate graphical tool dependent on the data type it will help to identify patterns, trends and signals to establish hypothesis.

Why do you use it? Root Cause Analysis is a useful process for understanding and solving a problem. Figure out what negative events are occurring. Then, look at the complex systems around those problems, and identify key points of failure. ... You can use many tools to support your RCA process. Different types of root cause analysis tools can be used with a range of methods, each of which is appropriate for different situations. Below is a list of the five common root cause analysis tools:

1. Pareto Chart 2. The 5 Whys 3. Fishbone Diagram 4. Scatter Diagram 5. Failure Mode and Effects Analysis (FMEA)

When do you use it? • RCA is typically used as a reactive method of identifying event(s) causes, revealing problems and solving them. Analysis

is done after an event has occurred.

How do you use it? RCA tends to be a four-step process involving the following:

1. Data collection 2. Causal factor charting 3. Root cause identification. 4. Recommendation generation and implementation.

Step 1 - Data collection.

The first step in the analysis is to gather data. Without complete information and an understanding of the event, the causal factors and root causes associated with the event cannot be identified. Majority of time spent analysing an event is spent in gathering data.

Step 2 - Causal factor charting.

Causal factor Charting provides a structure for investigators to organise and analyse the information gathered during the investigation and identify gaps and deficiencies in knowledge as the investigation progresses. The causal factor chart is simply a sequence diagram with logic tests that describes the events leading up to an occurrence, plus the conditions surrounding each event. Preparation of the causal factor chart should begin as soon as investigators start to collect information about the occurrence.


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Data collection should continue until the investigators are satisfied with the thoroughness of the chart. When the entire occurrence has been charted out, the investigators are in a good position to identify the major contributors to the incident, called causal factors. Causal factors are those contributors (human errors and component failures) that, if eliminated, would have either prevented the occurrence or reduced its severity. In many traditional analyses, the most visible causal factor is given all the attention. Rarely, however, is there just one causal factor; events are usually the result of a combination of contributors.

Step 3 - Root cause identification.

After all the causal factors have been identified, the investigators begin root cause identification. This step involves the use of a decision diagram called the Root Cause Map to identify the underlying reason or reasons for each causal factor. The map structures the reasoning process of the investigators by helping them answer questions about why particular causal factors exist or occurred. The identification of root causes helps the investigator determine the reasons the event occurred so the problems surrounding the occurrence can be addressed.

Step 4 - Recommendation generation and implementation. This step is the generation of recommendations. Following identification of the root causes for a particular causal factor, achievable recommendations for preventing its recurrence are then generated. The root cause analyst is often not responsible for the implementation of recommendations generated by the analysis. However, if the recommendations are not implemented, the effort expended in performing the analysis is wasted. In addition, the events that triggered the analysis should be expected to recur. Organisations need to ensure that recommendations are tracked to completion.


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TKSGB14 Experimentation & optimisation

What is it? Design of experiments (DOE) is the manipulation of controllable factors (independent variables or the X’s) at different levels to see their effect on the response (dependent variables or the Y’s). Designed experiment gives a mathematical model relating the variables and the responses. DOE uses real time data that we can observe compared to say regression that uses historical data.

Why do you use it? • Helps identify factors that shift the average, the variation or both

• To determine the best input settings to optimise outputs

• Quick and efficient screening of many factors for significant effects

• Creates a mathematical model relating inputs to outputs

• To improve quality, reduce lead time, reduce rework, reduce costs

When do you use it? • To screen a large number of variables

• To identify critical factors to improve performance

• As a method for setting input tolerances

• In the analyse and improve phases

How do you use it? • When analysing data we want to prove a statistical relationship between the critical input variables or the X’s and the

output variables or the Y’s. Passive analysis refers to collecting historic or new data from the process where the inputs and outputs are allowed to fluctuate in their normal range. Active analysis is purposefully making changes to the inputs and monitoring the outputs

• There are numerous statistical tools to test relationships, DOE is specifically designed for active data to prove a relationship, there are other statistical tools designed for use with passive data such as confidence intervals, hypothesis testing, anova and simple and multiple regression, the objective of regression analysis is to create a prediction model to show how changes in the inputs impact the outputs

• Factors are the things that we change during an experiment in order to observe the impact on the output, levels are the values of the factors that are tested during the experiment

Full factorial design

• Examines every possible combination of the factors, all the factor levels are tested

• Can determine the main effects of factors and factor interactions on the response

• Can estimate the best levels to set factors to achieve best outputs

• Factors can be tested at multiple levels, 2 level designs tend to be popular because of efficiency


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Fractional factorial design

• These are similar to full factorial except the number of runs are reduced eg ½ fractional factorial will have ½ the runs of a full factorial. Outputs are usually driven by main factors and 2 way interactions which often makes fractional factorials practical

• Gives up some information such as factor interactions, the amount we are able to estimate is identified by the resolution, the higher the resolution the more we can measure

Planning

• The output or response variable works better with continuous rather than discrete data

• The output(s) should be base lined, checked for stability and measurable target objectives set e.g. centring or reducing variation, ensure this has a relationship to a business metric

• The measuring system must be adequate

• Plan and prepare the resources for conducting the experiments

• Decide how much change you require to detect in the output and determine what sample size is required

• Classify inputs into uncontrollable factors, controllable factors that can be manipulated for the experiment and constant factors that will not be changed

Design

• Experiments may follow a sequence of screening, refining and optimising. Screening is used to look at a large group of factors to find the vital few. Refining designs are the same except only the critical factors are brought forward. Optimising is used to create the final models which includes response surface methodology (RSM)

• Screening and refining designs are often based on fractional factorials

• Confounding or aliasing in fractional factorials refers to the situation where say 2 factors are confounded but have a significant effect on the output, however there may be no way to tell which factor or if the interaction is causing the effect

• Resolution is a way of describing confounding eg resolution 3 does not alias main effects but does alias main effects with 2 way interactions. The alias structure will describe the impact of the chosen resolution

• A replicate is a non-consecutive run which requires an additional setup, a repeat is an additional run with the same setup. Both will increase the sample size, however replicates attempts to capture the long term process variation and repeats captures the short term variation, there are risks if repeats are treated as replicates in the analysis

• The runs must be conducted in a random order, randomisation reduces the impact of background noise factors

• Blocking allows you to randomise runs within a block eg blocking could be used to separate shifts or time of the day • Centre points can be used on 2 level designs, they increase the sample size and allow to test for curvature

• Power refers to the ability of the DOE to detect a change in the output of a given size, establishing the required sample size and degrees of freedom is important but must be weighed against practical considerations

Analysis

• Various tools and techniques are available to analyse the results

• Hypothesis testing is used to determine if there is a significant relationship between inputs and outputs

• Typically the following graphical analysis is generated to identify important effects; probability plots, pareto charts, main effect plots and interaction plots

• In addition to the graphical plots we will see an ANOVA table and multiple regression analysis that will identify significant factors and interactions

• Where appropriate remove factors that have the smallest effect and re-run the analysis based on a reduced model. P values are often set at <.10 to indicate strong effects

• The next step is the diagnostics which is used to confirm that the model is good, diagnostic plots help detect problems such as special causes and noise factors

• Diagnostics include residual normality plot, fitted values, run charts and histogram

• Response optimiser can be used to determine best settings for inputs to achieve an output

Confirmation • After the factor settings have been confirmed the process should be run for an adequate period to determine

performance.


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TKSGB15 Identification & prioritisation

What is it? With respect to this standard Identification and Prioritisation refers specifically to selection and prioritisation matrices and Failure Mode and Effects Analysis. Selection and prioritisation matrices are tools for helping to prioritise the suitability of ideas and solutions using a matrix that is split into four quadrants. The matrices can have a selection of criteria for the x and y axis. Some typical examples include -

• X = range of importance, y = range of urgency

• X = Value against y = risk

• X = Cost against y = impact If you search around, you’ll find countless articles with recommendations, techniques and approaches to this very hard problem. However, each method’s usefulness will depend on the specific product or project where it’s applied. Your prioritisation needs may vary vastly. Failure Mode Effects Analysis (FMEA) is a process analysis tool for identifying all possible failures in a design, process, product or service. Failure mode represents the way something might fail. They could be actual failures or potential failures. Effects analysis represents the analysis of the consequences of those particular failures identified.

Why do you use it? Selection and prioritisation matrices are simple, organisational tools to help rationalise the suitability of ideas and potential solutions by categorising them into a suitable logical order. For example, comparing risk against value allows us to prioritise which idea or solution to try first. It is an effective filtering tool for prioritising the order for implementing the ideas and solutions to give the greatest impact first. Failure Mode Effects Analysis (FMEA) is used to eliminate or reduce failures by identifying the most likely cause. During the design stage, it is used as a failure prevention tool, logging all the potential risks. During the manufacturing or service phase, it can be used to identify failures within the system, either potential or actual. It can be used to document potential failures.

When do you use it? Selection and prioritisation matrices

• Throughout an improvement project by the improvement team when a selection process is needed.

• In conjunction with Fish Bone diagrams to rank possible causes

• Rank improvement ideas after a go-look-see activity or a kaizen activity

• Rank health and safety suggestions after a risk assessment Failure Mode Effects Analysis (FMEA)

• When a product, process or service is being created or redesigned to prevent failures occurring and to record the results for continuous improvement purposes

• When products, processes and services are being modified or changed in any way.

• When analysing the failures of an existing product, process or service.

• Verify and validate the product, process or service throughout the life cycle to take into consideration external influences that might have occurred over time.


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How do you use it?

Selection and prioritisation matrices

• Carry the prioritisation exercise with the improvement team and all associated with the problem.

• Keep the idea generation, and evaluation separated.

• Generate ideas on post-it notes or appropriate resource, keep the time limited to improve the effect

• Post the notes on the matrix in the appropriate quadrant and review with the team.

• Identify the most frequent suggestions with the team and identify the priority in which the ideas, potential problems or solutions will be addressed.

Failure Mode Effects Analysis (FMEA)

• Assemble an appropriate cross-functional team with a range of skills and experience specific to the product, process or service

• Identify the scope for which the FMEA is to be carried out. Provide appropriate communications to ensure everyone familiar with the specific scope.

• Ensure the sheet data is filled in. Go through each column of the FMEA sheet starting with – 1. Item and function 2. Potential Failure Mode 3. Potential effects of failure 4. Determine a severity rating (SEV) between 1 – 10 (Higher the value, higher the severity) 5. Establish the potential cause of failure 6. Determine an occurrence frequency (OCC) between 1 – 10 (Higher the value, higher the occurrence) 7. Establish detection methods and quality controls 8. Determine a detection rating (DET) between 1 – 10 (Higher the value, reduced chance of detection) 9. Establish the Risk Priority Number – this is the value of the SEV x OCC x DET 10. Create the recommended actions 11. Circulate actions to appropriate staff.


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TKSGB16 Sustainability & control

What is it? With respect to the improvement standards, sustainability and control is the application of appropriate tools and techniques for preventing gained improvements slipping back to the start point. These techniques allow the derived gains to be maintained. The goal for the project team when developing solutions for these gains, has to be to identify solutions that will eliminate the risk of a mistake, using a range of mistake proofing techniques enables the team to drive toward this goal which can be sustainable and remain in your control. Tracking critical inputs is key to applying control measures and sustaining improved performance

Why do you use it? Without appropriate sustainability or control, improvements are likely to drift back to their baseline condition. Sustainability and control measures are used to prevent this from happening. Spending time to implement appropriate sustainable control measures in a project helps to address this (along with change management strategies).

When do you use it? These techniques are used within the Improve and Control phase of a DMAIC project in. order to sustain the improvement that has been made. Much of the data gathered in the measure phase and analysed, within the analyse phase of the DMAIC project will be useful for development of control plans.

How do you use it? • Within the Improvement phase understand and utilise mistake proofing techniques, sometimes referred to as poka-

yoke. These techniques are scaled to suit the application. o Elimination or eradication of the problem is the highest order of mistake proofing, where the product or

process is redesigned so it no longer causes the problem o Control includes techniques to prevent wrong assembly or connection such as keyways, which prevent

assembly errors becoming defects o Shutdown automatically stops the creation of defects by stopping the process o Replacement of existing processes with an improved version that reduces the chance of creating defects o Alleviation or mitigation techniques such as shear pins in clutches or fuses in plugs. These are designed to

minimise the effects of error in the system whilst safeguarding the process and at the same time being easy to replace.

o Andon or warning lights to indicate a process that is out of control o Emergency stops or break points which are activated manually once a fault condition has been identified o Visual control aids showing defects. These facilitation techniques are designed so that things can be easier to

do without error o Standard work includes the development of good 5S practice including colour coding, visual management

quality procedures and standard operating procedures including all relevant training

• If a project team are unable to eliminate the chance of a defect or error by suitable mistake proofing techniques, they can develop a control plan with the project team and process owner. All critical, independent ‘X’ input variables should be logged on the control plan. Tracking the input variables is key to applying control measures and for maintaining improved performance.


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• Automate measurement systems of critical inputs where possible, or at least simplify and standardise the data gathering method and sampling for key input variables

• In order to create the habits for the management of the improvement it is sometimes useful to create check sheets for the team leader, manager or supervisor. This helps drive accountability for actions and maintain improvements.


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TKSGB17 Measurement systems

What is it? Before we conduct any analysis, we must confirm the quality of the data that we are using, MSA refers to a range of techniques that can help identify sources of error in the data, sources may include operator skill, poor equipment, bad material or wrong standards. When a measurement system is poor, we lose the ability to make good decisions about how to improve the process. The total observed variation we see in the data we collect is equal to what is really happening in the process plus the variation due to the way we measure data.

Why do you use it? • The data in our spread sheet may not reflect the process

• Effort can be wasted trying to improve a process when a major source is error is the measurement system

• An incapable measuring system could result in incorrect decisions, rejecting good products or passing defective products to our customers

• Better understand the sources of variation that can influence the results from the observed variation

• Ensure that the measurement system is capable of producing quality data that is adequate to achieve the projects objective

• If the measurement system is not capable it must be improved before data can be used for process improvement

When do you use it? • The measurement system for all sources of data must be checked before any process improvement work

• Originally designed for manufacturing but can be applied to transactional processes, works with continuous and attribute data

How do you use it? MSA for continuous data

• For our measurement system the two key components are accuracy and precision

• Accuracy, the extent to which the average of measurements deviates from the true value

• Linearity, a measure of the range of values that the measuring system could be used over

• Bias, do we see a difference if we use different people or devices to measure the same thing

• Stability, do the measurements change over time

• Precision, the extent to which we get the same values when repeated measurements are made on the same item

• Discrimination, the ability to detect small changes to the process

Repeatability and reproducibility (R&R)

• Repeatability, the variation that occurs when successive measurements are made under the same conditions e.g. same part, same equipment and same person

• Reproducibility, the variation in the average of measurements made by different persons using the same part and same equipment

• The total variation that we observe is compared against repeatability and reproducibility variation, the smaller % the better


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Gage R&R study

• This is an experiment designed to measure how much of the process variation is consumed by the measurement system. It consists of a set of trials to assess repeatability and reproducibility to assess precision. Accuracy is typically managed with calibration. Measurement error is categorised into part to part variation, operator to operator (data collector), equipment variation and the interaction between the operator and the part being measured

• Correct preparation and running are required for the study

• Various outputs are available including % study variation, % tolerance variation, % process variation. The %R&R is considered excellent if <10%

• Gage R&R can also be used with destructive testing e.g. crash testing or plastic lamination testing. In this situation we cannot repeat a test so we would assume that all parts within a batch are nearly identical

Attribute data MSA

• Most physical measurement systems use devices that provide continuous data e.g. temperature and dimensions. There will be instances where only attribute data is available e.g. pass and fail. In this instance the inspection involves classifying the unit as good or bad or counting the number of defects

• Attribute R&R is conducted in a similar fashion to continuous R&R, however more parts are required, typically 30

• Often these types of data are measured visually

• A perfect attribute measurement system would never reject a good part or accept a bad part, a correct decision every time

• Attribute measurement system will examine repeatability and reproducibility and compare each part to a standard or master


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TKSGB18 Data analysis & statistical methods

What is it? Measures of central tendency uses three parts, which include mean, central tendency and standard deviation. The more number of standard deviations between process average and acceptable process limits fits, the less likely that the process performs beyond the acceptable process limits, and it causes a defect. This is the reason why a 6σ (Six Sigma) process performs better than 1σ, 2σ, 3σ, 4σ, and 5σ processes. Mean is the arithmetic average of a process data set. Central tendency is the tendency of

data to be around this mean. Standard Deviation (also known as Sigma or σ) determines the spread around this mean/central tendency.

Why do you use it? Obviously 7 or more σ processes are even better than a 6σ (Six Sigma) process, and yet throughout the evaluation and history of Six Sigma process, the practitioners gained the belief that a 6σ process is good enough to be reliable in almost all major situations except some systems whose defects can cause un-repairable consequences. Six Sigma stands for 6 standard deviations (6σ) between the average and acceptable limits which are derived from the customer requirements, and they specify the minimum and maximum acceptable limits of a process. LSL and USL stand for “Lower Specification Limit” and “Upper Specification Limit” respectively. For instance in a car manufacturing system the desired average length (Mean length) of car door can be 1.37185 meter. In order to smoothly assemble the door into the car, LSL can be 1.37179 meter, and USL can be 1.37191 meter. To reach a 6σ quality level in such a process, the standard deviation of car door length must be at most 0.00001 meter around the mean length.

When do you use it? A measure of Central Tendency is a number helping you ballpark the "middle" or "centre" of a distribution. The most commonly used estimate of Central Tendency is the mean, the arithmetic average. You calculate the mean by adding up all the scores and then dividing by the total number of scores. The second most common measure of Central Tendency is the median. The median marks the 50th percentile. 50% of the scores are above the median and 50% fall below the median. When a distribution of scores is bell-shaped and balanced (a normal distribution) both the mean and the median sit in the exact centre splitting the distribution right down the middle. That is, the mean and median are equal.

How do you use it? • First task is to collect a data from the field of investigation.

• Then use a format to present the data in a table or graph or any diagram where analysis can start.

• Analyse the data to draw conclusion, the mean, median and mode are all valid measures of central tendency but, under different conditions, some measures of central tendency become more appropriate to use than others.

• Such central values are the representatives of the whole data. Calculation of representative values of data is called the measure of central tendency.

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