Parts Per Million (PPM)
PPM is a way of stating the performance of a process in terms of actual or projected defective material. PPM data can be used to indicate areas variation requiring attention.
a component of technology, product or process that can assume a nominal value that defines it.
a matrix of problems, faults, failure types vs. occurrence frequency (days / weeks / months) - named after Marvin (Marv) Paynter (Ford Motor Company).
Percentage of Inspection points Satisfying Tolerance (PIST)
the percentage of inspection points that are within the tolerances indicated on the design.
Process Failure Mode and Effects Analysis. Click here for more on FMEA
based upon work by W.A. Shewhart (in the 1930s at Bell Labs) and made popular by W.E. Deming (in the 1950s and on), PDCA (also known as the Deming Cycle, Shewhart cycle, or Deming Wheel) is an iterative four-step quality control strategy is based on the principles of - customer satisfaction, management by fact and respect for people. Click here for more.
Product Improvement Program, Performance Improvement Plan
results-oriented approach to project review that involves stakeholders in a process of evaluation and planning to improve product/project performance
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)
Plant Experiments - a process improvement tool, uses simple factorial designs, two-level designs in two or three factors. Goal is to minimize disruption of production while making big enough changes to realize the benefits.
A unit that provides protection and containment of items plus ease of handling by manual or mechanical means.
80/20 rule - focus on the important stuff....
a statistic, a single number, calculated from a sample. In the absence of a more or better data, a point estimate is the best available estimate for a parameter.
A theoretical, infinitely large sample
The closeness of agreement between randomly selected individual measurements or test results. Also see Accuracy.
Preliminary Bill of Material
An initial Bill of Material completed prior to design and print release.
Prerequisite Tree (PRT)
(part of the Theory of Constraints Thinking Process) a structure describing all of the obstacles to achieving an objective, as well as the responses needed to overcome them, and the sequence in which they must be addressed. It identifies the minimum necessary conditions to achieving a specified objective.