Part, Instrument, Standard, Method, Operator, Environment & Assumptions (PISMOEA

P.I.S.M.O.E.A Error Model - P.I.S.M.O.E.A (Part, Instrument, Standard, Method, Operator, Environment & Assumptions) was originally developed by Mr. Gordon Skattum, Senior ASQ CQE, metrologist and Director of Integrated Manufacturing for Rock Valley College Technology Center. The typical statistical assumptions of a Gage R&R study, include: normal process, random and independent trials, stable, and test-retest criteria. When one or more assumption is violated (e.g., non-normal measurement process, operator bias) the tool and analysis ultimately become unstable, confusing, and misleading. %GRR evaluations for product and process control can be overestimated. There are also nonstatistical assumptions related to measurement systems (e.g., calibration, operational, coefficients and rates of expansion, physical laws and constants). The measurement planner should be able to identify, control, correct, or modify the MSA method to accommodate significant violations for the assumptions in the measurement process. Similar to all processes, a measurement system is impacted by random and systematic sources of variation. These sources of variation are due to common and special (chaotic) causes. In order to understand, control and improve a measurement system, the potential sources of variation ought to first be identified. Although the specific causes will depend on the situation, a general error model can be used to categorize sources of variation for any measurement system. There are various methods of presenting and categorizing these sources of variation using simple cause & effect, matrix, or tree diagrams. (source: AIAG's MSA Manual).

[Category=Quality ]

Source: The Quality Portal, 17 April 2011 11:38:10, http://thequalityportal.com/glossary/l.htm