t-test
used to test means or location effects. For example, used to compare the mean output of Line A vs. the mean output of Line B. [Statistical inference tests to compare the quality of different products / processes and compare the performance of different groups.]
3-2-1 Location or n-2-1 Locating Scheme
1. 3 locators position part in a primary
plane / direction
2. 2 locators then position the part in
a secondary direction
3. 1 locator to position part in the
tertiary direction
one could replace 2 & 3 with 2 round pins (one for a circular hole and other for a slot), pin locates part in two directions (in/out and fore aft),
slot locates part in 1-direction (2-way).
Takt Time
Takt is the heartbeat of a lean system. It is the rate of customer demand. It is the tool to link production to the customer by matching the pace of production to the pace of actual final sales. It defines the rate which material and product flow through the value stream. First, you calculate actual takt times for each product and part (takt time = total time available for production / customer demand, where, customer demand = total production requirement / total available production time). It is used to design assembly and other processes, to assess production conditions, calculate pitch, develop material handling processes, determine problem-response requirements, and so on. To run faster than takt time anywhere in the value stream is overproduction, resulting in excess inventory. To run slower than takt time creates the need for accelerated production, overtime, and expedited shipments. (Takt is the German word for musical meter, which came into Japan in the 1930s when the Japanese were learning aircraft production from German aerospace engineers.)
Team Feasibility Commitment
A commitment by the Product Quality Planning Team that the design can be manufactured, assembled, tested, packaged, and shipped in sufficient quantity at an acceptable cost, and on schedule. Click here to read more on
Assessing Team Performance. Timing Plan
A plan that lists tasks, assignments, events, and timing required to provide a product that meets customer needs and expectations.
TGR
Things Gone Right.
TGW
Things Gone Wrong.
TMAP / TMP - Thought Process Map
helps present thoughts and any questions at the start of the project in a structured, visual way with respect to realizing the project goal. It helps identify all information and progress through a DMAIC process
Tolerance Design
A technique using Taguchi Methods or Design of Experiments to identify how much each tolerance contributes to the final Quality and Reliability to help the engineer decide which tolerance to improve and which tolerance to relax. Tolerance design increases product / manufacturing cost and should be done when parameter design has not sufficiently reduced variation.
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Touzen
unnecessary kaizen
TQM
an approach for continuously improving the quality of goods and services delivered through the participation of all levels and functions of the organization.
Transition Tree (TRT)
(part of the Theory of Constraints Thinking Process) step-by-step process from start to finish of a course of action. It shows how specific actions combine with existing reality to achieve new expected effects, and why we expect those actions to achieve the particular outcomes that are required.
Tree Diagram
A visual representation of the major functions performed by a system which shows logical components and sub-components.
TRIZ
TRIZ, the Russian language acronym for the Theory of Inventive Problem Solving is a product of the cataloguing and analysis of empirical data. A structured methodology which contains an algorithm for isolating the principal technology / engineering conflict which limits or prevents satisfactory performance of a system. The identified conflict is then categorized using established physical laws, principles, or relationships, thereby enabling fundamental solutions to be derived based upon proven (sometimes previously unrelated) technology. >>
Type I & Type II Errors
Type I error (also known as alpha error) - conclude a difference exists when no difference exists. (for example, you say two machines produce different mean outputs when they do not.).
Type II error (also known as beta error) - conclude no difference exists when it does. (for example, say two machines produce similar mean outputs when in fact they do).
Notes:
a) for fixed sample size experiments, reducing Type I errors result in higher Type II errors. (and vice versa)
b) increase in sample size (n), generally reduces both types of errors
c) very large sample sizes may result in detecting "statistically significant, but practically insignificant results".
To determine if something is statistically significant, we typically calculate a p-value.
To determine statistical significance - a) if p-value is <= alpha, conclude statistical difference, b) if p-value is > alpha, fail to conclude difference. For most experiments: let alpha = 0.01 or 0.05; may tighten alpha if effect of Type I Error is very severe.
In terms of statistical significance, (1-p) represents your confidence that a statistically significant difference exists.
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