Term
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Definition
| Developed cause and effect diagram (fishbone) |
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Term
| Net present value formula |
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Definition
| =Futurevalue/(1+r)squared |
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Term
| S.W.O.T acronym stand for |
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Definition
S-Strength
W-Weakness
0-Opportunity
T-Threat |
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Definition
S-Supply
I-Input
P-Process
O-Output
C-Customer |
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| Paretto analysis- 20 percent of cause is 80 percent of effect |
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| A type of variation which is natural and inherent to a process. These causes act randomly and independently of each other, are difficult to eliminate, and often require changes to a process or system. |
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Definition
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Term
| A form of deductive logic that makes an item-by-item comparison using data and facts. |
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Definition
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Term
| Refers to data that is measured on a continuum. It is data that is measured on an infinitely divisible scale (e.g., time, weight, and temperature) such that one half a unit still makes sense; half a minute, half a pound, etc. |
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Definition
| Continuous Data (aka Variable Data) |
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Term
| Broadly describes ongoing, incremental efforts to make products and processes better. |
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Definition
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Term
| Time charts designed to display signals or warnings of special cause variation. |
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Definition
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Term
| Quantifies the negative outcomes (costs) due to waste, inefficiencies, and defects in a process. |
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Definition
| Cost of Poor Quality (COPQ) |
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Term
| A process map that separates process steps by function, department or individual. This provides a visual that displays not just the steps in a process but also which individuals, group or department performs those steps. |
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Definition
| Cross Functional Flowchart (aka Deployment or Swimlane Map) |
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Term
| A statistical concept expressed as the letter "r" that measures the strength and type of the relationship between two factors ('X' and 'Y'). |
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Definition
| Correlation Coefficient (aka Pearson Correlation) |
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Term
| The measurement of the time elapsed from the beginning of a process or a step to its end. |
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Definition
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Term
| Any aspect of a product or service that is critical to the customer. Useful in calculating DPO and DPMO. |
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Definition
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Term
| Term applied to any process, product, or service with one or more defects. |
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Definition
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Term
| Refers to categories or counts that can only be described in whole numbers; i.e. you can't have half a defect or half a customer. This type of data is the opposite of continuous or variable data (temperature, weight, distance, etc.). |
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Definition
| Discrete Data (aka Attribute Data) |
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Term
| A statistical concept that describes the variation between values in a data set. |
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Definition
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Term
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Definition
| A methodology for improving existing processes. Stands for Define, Measure, Analyze, Improve, and Control. |
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Term
| Metric that indicates the number of defects in a process per one million opportunities. Is calculated by the number of defects divided by (the number of units times the number of opportunities), multiplied by one million. |
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Definition
| Defects per Million Opportunities (DPMO) |
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Term
| A metric that indicates the number of defects in a process per opportunity. Calculated by the number of defects divided by the number of units times the number of opportunities. |
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Definition
| Defects per Opportunity (DPO) |
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Term
| Measures the amount of resources used in maximizing the output of a process. |
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Definition
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Term
| A risk management tool that identifies and quantifies the influence of potential failures in a system. |
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Definition
| Failure Modes & Effects Analysis (FMEA) |
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Term
| A structured brainstorming tool designed to assist an improvement team in listing potential causes of a specific effect. |
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Definition
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Term
| A brainstorming method which pits "driving" (positive) and "restraining" (negative) forces that support or oppose an idea. |
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Definition
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Term
| A Japanese term that translates to the "real place" or where the work takes place. |
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Definition
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Term
| States the desired results of a process improvement project. It is a fundamental part of any Project Charter. |
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Definition
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Term
| A visual display of the spread of variation in a process which shows the frequency of each value in the data set. |
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Definition
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Term
| An educated guess about the suspected cause (or causes) of defects in a process. |
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Definition
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Term
| The set of changes to an organization that make any process improvement permanent. These changes not only include procedural ones, but cultural (employee attitude and behavior) changes as well. |
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Definition
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Term
| A system for producing and delivering the right items, at the right time, in the right place, and in the right amounts. This concept is integral to the idea of a Pull system. |
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Definition
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Term
| In practice, generally spans from 1 to 5 days and involves key process participants focusing on solving a narrowly scoped process improvement opportunity. |
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Definition
| Kaizen Event (aka Rapid Improvement Event) |
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Term
| Japanese term that translates to "card" or "board" and indicates some form of signal within a process. Part of Just In Time (JIT) processing where either a physical or electronic device indicates that it's time to order inventory, process a unit or move to the next step in a process. |
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Definition
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Definition
A technique that categorizes customer requirements into three types:
1. Delighters
2. Satisfiers
3. Dissatisfiers. |
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Term
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Definition
| The measure of the cycle time from the moment a customer places an order to the moment they receive the desired goods or services. |
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Definition
| Systematic method for the elimination of waste from a process with the goal of providing what is of value to the customer. Much of what constitutes this method stems from tools developed at Toyota while creating the Toyota Production System. |
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Term
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Definition
| Uses data and measurements in decision-making. |
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Term
| Mistake Proofing (aka Poka Yoke) |
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Definition
| To consciously and diligently try to eliminate defects by preventing human errors before they occur or create alarms to warn of potential defects. |
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Term
| Non-Value Adding Activities |
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Definition
Refer to process steps that fail to meet one or more of the following criteria:
-The step transforms the item toward completion (something changes)
-The step is done right the first time (not a rework step)
-The customer cares (or would pay) for the step to be done |
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Term
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Definition
| Known as Hâ‚€, is the hypothesis statement that maintains there is no difference between two or more data samples. |
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Term
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Definition
| A quality chart of discrete data that helps identify the most significant types of defect occurrences. |
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Term
| PDCA (aka Plan Do Check Adjust) |
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Definition
| It's a method developed by Dr. Deming that favors trial and error over extensive planning and trying for perfection up front with the assumption the each test allows for essential fine tuning. |
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Term
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Definition
| A clear, concise statement about the symptoms of issues being encountered in a process. |
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Term
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Definition
| A measurement of how well a Process' Outputs meet Customer Requirements. |
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Term
| Process Capability Indicators |
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Definition
| Metrics that indicate how closely process outputs align within customer specifications when using continuous data (time, weight, volume, etc). |
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Term
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Definition
| Type of system that refers to the goal of having units moved through the process at the rate of customer demand. |
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Term
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Definition
| Type of system that refers to processes that rely on forecasting or the practice of creating excess goods and services to maintain a buffer. |
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Term
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Definition
| Describes how well a process consistently meets customer requirements. |
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Term
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Definition
| Matrix that outlines different levels of accountability and responsibility as related to an action item list. |
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Term
| Rapid Improvement Event (aka Kaizen Event) |
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Definition
| In practice, generally spans from 1 to 5 days and involves key process participants focusing on solving a narrowly scoped process improvement opportunity. |
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Term
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Definition
| A type of yield that measures how many units "roll through" a process, first pass, without defects. |
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Term
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Definition
| A measurement technique where smaller amounts of representative data can be used to understand the larger population. |
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Term
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Definition
| The result of sampling based on some preconceived judgement or convenience. |
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Term
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Definition
| Chart that shows the relationship between two variables (if any). Also known as an XY Plot since the variables are plotted on the X and Y axis. |
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Term
| Seiketsu (aka Standardize) |
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Definition
| Japanese word for "Standardize" which is the fourth step in the 5S method. |
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Term
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Definition
| Japanese word for "Sort" which is the first step in the 5S method. |
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Term
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Definition
| Japanese word for "Shine" which is the third step in the 5S method. |
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Term
| Seiton (aka Set in Order) |
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Definition
| Japanese word for "Set In Order" which is the second step in the 5S method. |
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Term
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Definition
| The concept that products should flow from operation to operation in the smallest increment, with one piece being the ideal. |
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Term
| SMED (aka Single Minute Exchange of Die) |
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Definition
| The practice of dramatically reducing or eliminating the time to change from one method or unit to another where the goal is to reduce the changeover time to single digits or under 10 minutes. |
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Term
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Definition
| Graphical tool used to track the movement of people and distances travelled in a work process. This tool tracks movement in office spaces as well as manufacturing shop floors. |
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Term
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Definition
| Refers to variation in a process which is sporadic and non-random. |
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Term
| Statistical Process Control (SPC) |
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Definition
| A quality control concept that uses statistical methods to monitor processes and uses Control Charts to gather and analyze data, and helps to determine if processes are "out of control." |
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Term
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Definition
| A statistical measure that shows the average amount that values vary (aka "Dispersion") from the mean. |
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Term
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Definition
| Refers to the goal of eliminating the variation in how a process or process step is completed. |
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Term
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Definition
| Data analysis technique where values are grouped into different layers (i.e., "strata") in order to better understand data. |
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Term
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Definition
| Refers to a visual stocking system used in tandem with Kanban or reorder signals in a Pull system. |
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Term
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Definition
| It is the average unit production time needed to meet customer demand. This is calculated by dividing the time available (minutes of work/day) by the customer demand (units required/day). |
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Term
| Threats & Opportunities Matrix |
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Definition
| A simple 2 x 2 grid that captures the downsides of not implementing a proposed solution and, conversely, the potential upsides if the solution is accepted. |
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Term
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Definition
Any activities that add value to the customer and meet the three criteria:
1. The step transforms the item toward completion
2. The step is done right the first time (not a rework step)
3. The customer cares (or would pay) for the step to be done |
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Term
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Definition
| Involves assessing each process step through the eyes of the customer and determining whether the step is a Value Adding Activity (VA), a Non-Value Adding Activity (NVA) or a Value Enabling Activity (VE). |
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Term
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Definition
| A method of mapping that includes data as well as process steps with the goal of identifying waste in the system. |
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Term
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Definition
| Describes how consistent a process' output is. |
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Term
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Definition
| The practice of making it easy to see how a process flows and what to do at each step. |
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Term
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Definition
| Tool that helps teams take customer comments, determine the underlying issues represented by those comments and use this information to develop measurable customer requirements. |
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Term
| Voice of the Customer (VOC) |
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Definition
| Data that represents the needs and wants of your customers. |
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Term
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Definition
| A decision-making tool that evaluates potential options against a list of weighted factors. |
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Term
| Anderson-Darling Test for Normality (aka Normality Test) |
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Definition
| A statistical test that determines whether or not a data set is normally distributed. A normal distribution is often referred to as a "Bell Curve." Whether a distribution is normal or not determines which tests or functions can be used with a particular data set. |
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Term
| Measurement Systems Analysis (aka MSA, aka Gage R&R) |
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Definition
| An experiment designed to assess various elements of data collection including the procedures of data collection, the measuring device or "gage" being used, the understanding of the operators and any factors that might cause variation. The goal is to reduce defects and variation within the data collection process itself. |
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Term
| Sampling Calculations (aka Sample Size Calculation) |
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Definition
| Sampling calculations are the formulas involved in Sampling that take into account a number of considerations including, the standard deviation of continuous data, the proportion defective of discrete data, the desired precision of the sample and the confidence level appropriate for the data being sampled. The goal of the sampling calculation is to determine the least amount of units that need to be sampled while still reflecting the entire population. |
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Term
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Definition
A hypothesis test that can be used to determine whether a process is performing at the level of an established standard. It provides a way to determine if there is a statistically significant difference between the standard and a particular data set or whether the difference is due to random chance.
An example would be testing if a vendor that has guaranteed 48 hour delivery is performing as promised. |
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Term
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Definition
A hypothesis test that can be used to determine whether the proportion defective of one strata of a process is statistically different from the proportion defective (or yield) of another strata of a process. It is useful for determining whether a particular strata or group could provide insight into the root cause of process issues.
An example would be testing if the product defect rates are truly different for two separate production lines or whether the difference is due to random chance. |
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Term
| F-Test (aka Test for Two Variances) |
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Definition
A hypothesis test that determines whether a statistically significant difference exists between the variance of two independent sets of normally distributed continuous data. It is useful for determining if a particular strata or group could provide insight into the root cause of process issues.
An example would be if Assembly Line A has product weights with a variance of 1 pound whereas Assembly Line B has product weights with a variance of 2 pounds and you want to determine if Line A truly has less variation or the difference is just due to random chance. |
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Term
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Definition
Bartlett's Test is a hypothesis test that determines whether a statistically significant difference exists between the variances of two or more independent sets of normally distributed continuous data. It is useful for determining if a particular strata or group could provide insight into the root cause of process issues.
An example would be if Assembly Line A product weights have a variance of 1 gram, Assembly Line B product weights have a variance of 2 grams and Assembly Line C product weights have variance of 2.5 grams and you want to determine if any of the 3 lines truly has less/more variation than the others or if the difference is just due to random chance. |
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Term
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Definition
A hypothesis test that determines whether a statistically significant difference exists between the average of a normally distributed continuous data set and a standard. It provides a way to determine if there is truly a difference between the standard and a particular data set mean or whether the difference is due to random chance.
An example would be testing whether a supplier, that has guaranteed an average 12-ounce fill rate on their beverages, is performing as promised. |
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Term
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Definition
A hypothesis test that determines whether a statistically significant difference exists between the averages of two independent sets of normally distributed continuous data. It is useful for determining if a particular strata or group could provide insight into the root cause of process issues.
An example would be if Location A has average sales of $3,567 per month whereas Location B has average sales of $3,843 per month and you want to determine if Location B truly has greater averages sales or the difference is just due to random chance |
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Term
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Definition
A hypothesis test that determines whether a statistically significant difference (aka variance) exists between the averages of two or more independent sets of normally distributed continuous data. It is useful for determining if a particular strata or group could provide insight into the root cause of process issues.
An example would be if Assembly Line A products weigh an average of 10.4 pounds, Assembly Line B products weigh an average of 9.2 pounds and Assembly Line C products weigh an average of 11 pounds and you want to determine if any of the 3 lines truly has a different average from the others or if the difference is just due to random chance. |
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Term
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Definition
A hypothesis test that determines whether a statistically significant difference exists between the variance of two or more independent sets of non-normally distributed continuous data. It is useful for determining if a particular strata or group could provide insight into the root cause of process issues.
An example would be if Assembly Line A has cycle times with a variance of 2 minutes where as Assembly Line B has cycle times with a variance of 3 minutes and you want to determine if Line A truly has less variation or if the difference is just due to random chance. |
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Term
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Definition
A hypothesis test that determines whether a statistically significant difference exists between the median of a non-normally distributed continuous data set and a standard. It provides a way to determine if there is truly a difference between the standard and a particular data set median or whether the difference is due to random chance.
An example would be testing whether a call center, that has guaranteed a median hold-time of 1 minute, is performing as promised. |
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Term
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Definition
A hypothesis test that determines whether a statistically significant difference exists between the medians of two independent sets of non-normally distributed continuous data. It is useful for determining if a particular strata or group could provide insight into the root cause of process issues.
An example would be if Pizza Delivery Person A has median delivery time of 15 minutes whereas Pizza Delivery Person B has median delivery time of 17 minutes and you want to determine if Person B is truly slower or if the difference is just due to random chance. |
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Term
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Definition
A hypothesis test that determines whether a statistically significant difference exists between the medians of two or more independent sets of non-normally distributed continuous data. It is useful for determining if a particular strata or group could provide insight into the root cause of process issues.
An example would be if Assembly Line A products have a median production cycle time of 10.3 minutes, Assembly Line B products have a median production cycle time of 9 minutes and Assembly Line C have a median production cycle time of 11.5 minutes and you want to determine if any of the 3 lines truly have different median cycle times from the others or if the difference is just due to random chance. |
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Term
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Definition
A hypothesis test that determines whether there is a correlation between two paired sets of continuous data. It is useful for determining if changes in Y can be attributable to a particular X. It produces a "prediction equation" that estimates the value of Y that can be expected for any given value of X within the range of the data set.
An example would be to test if rainfall and crop yield were correlated and then to calculate approximately how much water is required to achieve the desired yield. |
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Term
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Definition
A hypothesis test that determines whether there is a correlation between two or more values of X and the output, Y, of continuous data. It is useful for determining the level to which changes in Y can be attributable to one or more Xs. It produces a "prediction equation" that estimates the value of Y that can be expected for given values of one or more X values within the range of the data set.
An example would be to test if crop yield were correlated to both rainfall and fertilizer amount, and then to calculate approximately how much water and fertilizer are required to achieve the desired yield. |
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Term
| DOE - One Factor At A Time (aka OFAT) |
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Definition
The simplest form of a Design of Experiments that enables operators to observe the changes occurring in the output (Y Response,) of a process while changing one input (X Factor).
An example would be to test the cycle time (Y) to cook a pizza in one oven as a result of altering the temperature (X) in that oven. |
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Term
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Definition
A form of Design of Experiments that enables operators to observe the changes occurring in the output (Y Response,) of a process while changing more than one input (X Factors). This test highlights shifts in the average response or output associated with multiple factors. It also evaluates how factors in a process might interact.
An example would be to test the cycle time (Y) to cook a pizza in different ovens (X1) at different temperatures (X2). |
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Term
| DOE - Fractional Factorial |
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Definition
A form of Design of Experiments that enables operators to observe the changes occurring in the output (Y Response,) of a process while changing more than one input (X Factors) without running every single potential treatment combination. This test highlights shifts in the average response or output associated with multiple factors with less time and effort than required for a full factorial experiment. It has a diminished ability to evaluate how factors in a process might interact.
An example would be to test the cycle time (Y) to cook a pizza in different ovens (X1), at different temperatures (X2), and with different toppings (X3) without running every single combination of the three factors. |
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Term
| I & MR Chart (aka X & MR or Chart) |
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Definition
Control Charts designed for tracking single points of continuous data. They consist of two separate charts; "I" stands for the "Individual" Chart which tracks the individual data points (or pre-summarized data) and "MR" stands for "Moving Range" Chart which tracks the absolute value of the distance between each pair of consecutive data points. These are considered the most flexible of the Control Charts and are often used to track business performance data.
A classic example is to track daily total sales. |
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Term
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Definition
Control Charts designed for tracking the average of sub-grouped continuous data. They consist of two separate charts; "X-Bar" stands for the "Average" Chart which tracks the mean of sub-groups of up to 6 data points and "R" stands for "Range" Chart which tracks the difference between the maximum and minimum values in the subgroup. These charts are not as sensitive to non-normal data as the I & MR Charts.
A classic example is to track the average cycle time to deliver packages by sampling 5 packages per day. |
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Term
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Definition
Control Charts designed for tracking the average of large sub-groups of continuous data. They consist of two separate charts; "X-Bar" stands for the "Average" Chart which tracks the mean of sub-groups of 6 or more data points and "S" stands for the "Standard Deviation" Chart which calculates the standard deviation within each subgroup. These charts are useful for detecting shifts in the "center" or average with large subgroups.
A classic example is to track the food order cycle time to deliver packages by sampling 10 orders per day. |
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Term
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Definition
Control Charts designed for tracking the proportion defective for discrete data.These charts require both the total population as well as the count of defective units in order to plot the proportion.
A classic example is to track the proportion of defective products returned each month. |
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Term
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Definition
nP Charts are Control Charts designed for tracking the number of defective items for discrete data in consistently sized sub-groups.
A classic example an nP Chart is to track the number of defective products per lot shipped where the lot size was constant. |
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Term
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Definition
C Charts are Control Charts designed for tracking the count of defects for discrete data in consistently sized sub-groups.
A classic example of a C Chart is to track the number of defects per application, which are all 10 pages each. |
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Term
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Definition
Control Charts designed for tracking the number of defects per unit for discrete data.
A classic example is to track the number of scratches on new smart phone cases at a manufacturing facility. |
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Term
| Seven Basic Quality Tools |
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Definition
A list of basic process improvement tools and techniques. The list is generally attributed to Kaoru Ishikawa, a follower of Dr. Edwards Deming, who is also famous for popularizing the Fishbone or Ishikawa Diagram. In an effort to reduce the complexity of Statistical Process Control, and make it more accessible for the average worker, he compiled a shortlist of simple but powerful Lean Six Sigma tools.
The list includes:
- Cause & Effect Diagram (aka Fishbone Diagram)
- Checksheet
- Control Chart
- Histogram
- Pareto Chart
- Scatter Diagram (aka Scatter Plot)
- Stratification (often replaced with Flow Chart) |
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Term
| Flow Chart (aka Process Map) |
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Definition
| A step-by-step diagram that shows the activities needed to complete a process. Creating this is one of the first steps in a Lean Six Sigma process improvement project. |
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Term
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Definition
| It is the method of finding the source of process problems by uncovering their origin or "root." This is in contrast to focusing on fixing the symptoms or effects of process issues. If a "root cause" is removed or neutralized then the undesirable effects will no longer impact the process in question. |
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Term
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Definition
| They are a 3-pronged quality approach developed by the Japanese statistician Genichi Taguchi. By focusing on 1) better design ideas 2) rigorous testing of the design, and 3) reducing the impact of anything that would cause variation. His goal was to create a more robust product prior to full scale production. They are aimed to deliver exactly what the customer wanted by reducing variation and lowering costs in the process. |
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Term
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Definition
| This is an equation that measures the "loss" experienced by customers as a function of how much a product varied from what the customer found useful. His idea rocked the quality world because the common wisdom held that when products met internal measures they were "good" and if they fell outside the limits they were "defects." Taguchi looked at variation from the eyes of the customer and decided to grade on a curve. |
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Term
| Jidoka (aka Autonomation) |
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Definition
This technique was invented by Sakichi Toyoda back in the 1896 so that his power loom invention would stop and and allow workers to intervene and fix the issues. The 4 steps in Jidoka are:
1. Detect the abnormality
2. Stop the machine
3. Fix what is wrong
4. Find and solve the root cause
It is considered one of the pillars of the Toyota Production System. |
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Term
| TOC (aka Theory of Constraints) |
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Definition
| This boils down to the idea that a process is only as good as its weakest link - and the weak links are bottlenecks. This idea was developed by Eliyahu Goldratt and made famous in his book, "The Goal", a novel based on a production plant. Once the major bottleneck is discovered, the idea is to reduce or eliminate it knowing that another, lesser bottleneck will emerge in its place. This systematic approach to rapid improvement states that only by systematically addressing each successively smaller bottleneck will the company reach its financial goal. |
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Term
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Definition
| This is a term coined by Quality guru Armand Feigenbaum to point out the often un-tracked waste of rework. He discovered that up to 40% of the capacity at a typical manufacturing plant was spent on fixing what was not done right the first time. |
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Term
| TQC (aka Total Quality Control) |
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Definition
| This was a concept created by quality guru Armand Feigenbaum in the 1950s. He maintained that Quality was not just the job of engineers or confined to production. TQC holds that every part of the company has to work in a coordinated way to serve the user or customer. |
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Term
| Design of Experiments (aka DOE) |
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Definition
| This is an active method of manipulating a process as opposed to passively observing a process and enables operators to evaluate the changes occurring in the output (Y Response,) of a process while changing one or more inputs (X Factors). |
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Term
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Definition
MOTION
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This is a method, popularized by the quality guru Shigeo Shingo, that proposes removing the need for inspection by eliminating the possibility of human error. Mr. Shingo was a proponent of Poka Yoke or Mistake Proofing processes which is a key component to removing the need for inspection. The idea is that by removing the root causes of errors, it is possible to achieve zero defects.
Zero Quality Control
This is a goal coined by quality guru Philip Crosby which challenged management to commit time and effort to doing things right the first time. This type of goal worked against the conventional wisdom that human error was inevitable. |
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Term
| The Four Absolutes of Quality |
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Definition
These were developed by quality guru Philip Crosby as a way to promote the idea increased quality did not mean increased cost. Quality and cost were not in competition which he expanded on in his best-seller, "Quality Is Free."
The Four Absolutes:
1. Quality is defined as conformance to requirements
2. The system for causing quality is prevention, not appraisal
3. The performance standard must be Zero Defects
4. The measurement of quality is the Price of Nonconformance |
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Term
| Crosby's 14 Steps to Quality Improvement |
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Definition
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These were developed by quality guru Philip Crosby as a way to promote the idea increased quality did not mean increased cost. Quality and cost were not in competition which he expanded on in his best-seller, "Quality Is Free."
The Four Absolutes:
1. Quality is defined as conformance to requirements
2. The system for causing quality is prevention, not appraisal
3. The performance standard must be Zero Defects
4. The measurement of quality is the Price of Nonconformance
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The Four Absolutes of Quality
These are quality guru Philip Crosby's recipe for long-term process improvement. His opinion was that these steps were the responsibility of management but involved the people who did the work. These steps provided guidelines as well as a method for communicating his Four Absolutes.
The 14 Steps to Quality Improvement:
Step 1: Management Commitment
Step 2: Quality Improvement Team
Step 3: Quality Measurement
Step 4: Cost of Quality Evaluation
Step 5: Quality Awareness
Step 6: Corrective Action
Step 7: Establish an Ad Hoc
Committee for the Zero Defects
Program
Step 8: Supervisor Training
Step 9: Zero Defects Day
Step 10: Goal Setting
Step 11: Error Cause Removal
Step 12: Recognition
Step 13: Quality Councils
Step 14: Do It Over Again
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Crosby's 14 Steps to Quality Improvement
They are entities, agencies or departments that work together to provide a product or service to a customer. They can be organizations that are upstream or downstream from the process in question. They are in the process collaborating with others to deliver the end product or service to the customer. They provide information, do the work and produce documents or materials that eventually reach the customers. They might think they are the customer of another unit or agency.
Process Partners
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Crosby's 14 Steps to Quality Improvement
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These are quality guru Philip Crosby's recipe for long-term process improvement. His opinion was that these steps were the responsibility of management but involved the people who did the work. These steps provided guidelines as well as a method for communicating his Four Absolutes.
The 14 Steps to Quality Improvement:
Step 1: Management Commitment
Step 2: Quality Improvement Team
Step 3: Quality Measurement
Step 4: Cost of Quality Evaluation
Step 5: Quality Awareness
Step 6: Corrective Action
Step 7: Establish an Ad Hoc
Committee for the Zero Defects
Program
Step 8: Supervisor Training
Step 9: Zero Defects Day
Step 10: Goal Setting
Step 11: Error Cause Removal
Step 12: Recognition
Step 13: Quality Councils
Step 14: Do It Over Again |
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