Dictionary of Lean Six Sigma Tools/Concepts
There are 23 entries in this dictionary.| Term | Definition |
|---|---|
| CAP Model | The Change Acceleration Process model was developed by General Electric (GE) as a method to help influence changes in the business. GE learned that it's not enough to have the right solution, but implementing such a solution that involves change must be accepted by those impacted by that change; otherwise the change (and ultimately that solution) may fail. The model is based on the equation Quality x Acceptance = Effectiveness (or Q x A = E) which accounts for the multiplicative impact of acceptance (i.e., buy-in for the change) to influence how effective the final change will be. |
| Capability Analysis (Binomial) | A method of testing process capability when you have discrete (a.k.a. attribute or categorical) data. The test involves using either a Binomial or Poisson analysis depending on how the discrete data is setup. |
| Capability Analysis (Non Normal) | A method of testing process capability when you have continuous (a.k.a. variable or numerical) data that forms a non-normal distribution. The test involves a Box-Cox transformation to help normalize the data for proper analysis. |
| Capability Analysis (Normal) | A method of testing process capability when you have continuous (a.k.a. variable or numerical) data that forms a normal distribution. |
| Cause & Effect (C&E) Diagram | Also know as a Fishbone or Ishikawa, it is a collaborative tool used by a team to help identify all possible causes for a particular undesirable effect (e.g., a defect). It digs into potential root causes by exploring effects from different perspectives, such as the 6Ms. |
| Cause & Effect (C&E) Matrix | A tool similar to a QFD Tool that evaluates and prioritizes all potential root causes that may have already been identified via other tools such as the C&E Diagram, 5 Whys, etc. The purpose of the tool is to allow the team to objectively narrow down all potential causes to just the critical few that are most likely the root causes. By doing so, it makes it easier to focus on what data needs to be gathered into the Data Collection Plan for statistical analysis. |
| Central Tendency | A statistical reference to the central area of a distribution for a continuous (a.k.a. variable or numerical) value. It generally describes the "central" area where most data points "tend" to fall in the distribution. The primary measurements for it are the mean (a.k.a. average), the median (50th percentile), and the mode. |
| Champion | An external resource in a project that falls under the Sponsor role, it's usually the role given to mid-level management like a manager, director or higher. They have strong control over the process targeted by the project and often serve as a strong decision maker for getting resources and removing roadblocks. Communication with them by the LSS project leader tends to be frequent (i.e., perhaps every 2 weeks or so). |
| Chi-Square Test | A statistical test (pronounced "KYE square") used for analyzing two or more discrete (a.k.a. attribute or categorical) values. The test compares the actual frequency/proportion of observations to an expected frequency/proportion of those observations. The two basic types of this test are the Goodness-Of-Fit Test (One Variable) and the Association Test (Two Way Table). |
| Closing a Project | Closing out a project isn't as simple as it may seem. There are a set of actions associated with the improvement, post-improvement and pre-closure that need to be reviewed before closing out a project. Once these are complete, there are still more actions that should be reviewed to formally close a project and transition it back to the Sponsor and his/her team. |
| CLT (Central Limit Theorem) | A theorem on which most statistical tests are based that states that the means of random samples from any distribution (normal or non-normal) with a mean of μ and a variance of σ2 will have the following attributes: 1) an approximately normal distribution, 2) a mean equal to μ, and 3) a variance equal to σ2/n. What does this mean? As an example, suppose you roll a pair of dice 10 times and write down the average of all the rolls and repeat that process 10 more times; after doing so you will find that 1) the averages of each of those sets of rolls will be about the same, and 2) the more you roll, the variance between the values will become more and more narrow (i.e., less variation). |
| Common Cause Variation | A type of variation (a.k.a. Noise) that reflects a natural or random variability that generally comes from within the process. Some examples include variation caused by poor design, normal wear and tear, poor environmental factors, poor maintenance, etc. This can be contrasted with Special Cause Variation. |
| Confidence Interval | A statistical term representing the range (lower and upper bounds) in which the population mean should reside based on the data in the sample. |
| Continuous Data | Also known as variable or numerical data, it's a type of data that can be measured on a continuum where it's virtually infinite in scale or divisibility. General examples include numeric values like dollars, time, distance, or degrees. Business examples include revenue, expenses, call duration, product specifications, etc. This can be contrasted with Discrete Data. |
| Control Chart | A statistical graph that plots the continuous data points over time to define observations (the actual plotted data points), the mean (a.k.a. average), lower control limit (LCL), upper control limit (UCL), and special cause tests. The purpose of this chart is to visually depict the process performance to see how "in control" it is and highlight any areas that aren't in control (reflected as special causes). |
| Control Chart Control Limits | These are statistical reference points on a control chart defined as 3σ below the mean (the lower control limit or LCL) and 3σ above the mean (the upper control limt or UCL). Many people confuse these with the upper/lower specification limits (USL/LSL) which are defined by what the customer wants, while control limits are defined by how the process performs. |
| Control Charts Drilldown | A drilldown chart or decision tree that illustrates how to find the right control chart based on the type of data used in the statistical analysis. |
| Control Plan | A defined plan outlining all necessary steps in order to sustain what improvements were implemented in a project. |
| COPIS | See SIPOC |
| Correlation | A measure of linear association between two independent continuous (a.k.a. variable or numerical) values. The nature of the relationship is where as one value changes, the other value moves at a predictable (yet not always equal) rate. It can be measured by a correlation coefficient (a.k.a. Pearson Correlation) and can be positive or negative. A positive correlation means the dependent factors move in the same increasing or decreasing direction as the independent value. A negative correlation means those dependent factors move at an inverse or opposite direction than the independent value. |
| Correlation Coefficient | Also known as the Pearson Correlation, it is a statistical measure of the strength of a relationship (or correlation) between two continuous data values. A sample correlation coefficient is represented as "r" and the population correlation coefficient is represented as "ρ" (rho). |
| Cpk | A measurement of the short-term process performance (a.k.a. voice of the process or VOP) in relation to the spread (or total tolerance) between the customer's lower/upper specification limits (LSL/USL) (a.k.a. the voice of the customer or VOC). It's used for measuring process capability where if it's less than 1, then the process is not capable within the LSL/USL tolerance. The higher it is above 1, the more capable the process is of achieving results within tolerance. It is calculated as the minimum(Zusl/3, Zlsl/3). |
| CTQ Drilldown | A flow illustrating the measurable values of the business defined as that which is critical to quality (CTQ), that is, a measurable characteristic of what the customer requires, expects or considers a priority. The drilldown depicts the typical flow of how these CTQs align through 3 levels to what's most important for the business, which is to satisfy the ultimate customer, i.e., the owner(s) of the business (e.g., corporate shareholders). Many misinterpret that the paying customer is the most important; while it's true they are very important in generating revenue, the drilldown illustrates how the business owner(s) is the final authority to which all other CTQs and aspects of the organization are intended to support. The drilldown can be essential for identifying and prioritizing project opportunities to ensure they meet the needs and priorities of the business. |
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