alphabetized- a selection of tools that may be delivered in support of project work
Components of Variance (COV) Studies: methods to partition the overall process variation into portions assignable to causes at each of several levels. For instance, the total process variation might be attributable to a within piece component of variation, a between piece within lot component of variation, and a between lot component of variation. COV studies are used to evaluate the stability and magnitude of the various components of variation and therefore to provide focus for work to develop process knowledge.
Control Charts: Certain variations in process or product measures belong to the category of chance variations about which little can be done other than change the process or system that produced the data. This category of variation is the sum of the effects of complex interactions between many factors. It is the variation "built" into the process by current practices and managerial behaviors. Besides these "chance" variations, there are variations produced by "assignable" or special causes. These sources of variation can be isolated. Control charts are used to differentiate the variation.
Design of Experiments (DOE): efficient procedures for discovering relationships between independent factors (process parameters), such as temperature, pressure, time, speed, etc., and response variables (product parameters) such as size, variation, ductility, shrink, etc. The independent factors are varied in a specific fashion and the effect of these changes on the response measured. The fashion in which the independent variables are manipulated may be thought of as the experimental design.
Factor Relationship Diagrams (FRD): tools that assist engineers in understanding and making decisions regarding potential information content in an experiment and cost of that information relative to experimental run order and experiment size (experimental cost in terms of production time and possible scrapped units).
Failure Mode & Effects Analysis (FMEA): a systematic method for identifying, analyzing, documenting and prioritizing potential failure modes, the effects of such failures on system performance and possible causes of failures.
Measurement System Evaluation (MSE): methodologies for identifying and quantifying the different sources of variation that affect a measurement system.
Numerical Evaluation of Metrics (NEM): a set of methodologies that combine the use of sampling strategies, sampling trees, and statistical process control (SPC). A proactive methodology, NEM is used to discover dominant sources of variation in product or process and to thereby provide guidance and direction for work. NEM is a discovery tool, whereby SPC has typically been taught as a monitoring tool.
Principal Component Analysis (PCA): A multivariate statistical procedure for investigating variation in process data. Principal components can be used to discover sources of variation among the variables being investigated. Knowledge as to the disturbing effect of the source of variation and knowing when it apparently occurred is usually of considerably help in reducing or eliminating the effect of the source.
Process Map: a tool that displays current process knowledge and is a supplement to many of the traditional process investigation tools. It enhances the usual flowcharts with the type of knowledge captured in Cause and Effect Diagrams. Graphically combining the knowledge typically depicted on a flowchart with that from a Cause and Effect Diagram, the Process Map overcomes the weaknesses of the two tools used independently. Additionally, the Process Map provides a clear understanding of the current state of process management by classifying each parameter as controllable or noise.
Regression Analysis: a set of methodologies used to determine the statistical relationship, if any, between variables in order to utilize the relation so that one variable can be predicted from the others. Regression, or ordinary least squares (OLS), empirically describes variation in a response variable (Y) based on a set of “independent variables” (x’s or y’s).
Thought Map: an ongoing documentation of existing knowledge, the questions asked, the parallel paths of work needed to answer those questions, tools applied to answer questions, knowledge gained from work performed, and the direction of future work. The thought map is invaluable in any focused work effort in order to capture the multitude of questions that arise, the many possible paths that need to be considered in obtaining solutions, the work performed, and the solutions obtained.