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D

Data: A set of collected facts. There are two basic kinds of numerical data: measured or variable data, such as "16 ounces," "4 miles" and "0.75 inches," and counted or attribute data, such as "162 defects."

Data management: The process by which the reliability, timeliness, and accessibility of an organization's database are assured.

D chart: See "demerit chart."

Decision matrix: A matrix used by teams to evaluate problems or possible solutions. After a matrix is drawn to evaluate possible solutions, for example, the team lists them in the far left vertical column. Next, the team selects criteria to rate the possible solutions, writing them across the top row. Third, each possible solution is rated on a scale of 1 to 5 for each criterion, and the rating is recorded in the corresponding grid. Finally, the ratings of all the criteria for each possible solution are added to determine its total score. The total score is then used to help decide which solution deserves the most attention.

Deduction: An approach to theory development based on modeling.

Defect: A product's or service's nonfulfillment of an intended requirement or reasonable expectation for use, including safety considerations. There are four classes of defects: class 1, very serious, leads directly to severe injury or catastrophic economic loss; class 2, serious, leads directly to significant injury or significant economic loss; class 3, major, is related to major problems with respect to intended normal or reasonably foreseeable use; and class 4, minor, is related to minor problems with respect to intended normal or reasonably foreseeable use (see also "blemish," "imperfection" and "nonconformity").

Defective: A defective unit; a unit of product that contains one or more defects with respect to the quality characteristic(s) under consideration.

Defects per million opportunities (DPMO): The actual number of defects occurring divided by the total number of opportunities for a defect and multiplied by 1 million. Also referred to as ppm (parts per million).

Degrees of freedom: A parameter in the t, F, and x2 distributions. It is a measure of the amount of information available for estimating the population variance, s2. It is the number of independent observations minus the number of parameters estimated.

Delighter: A feature of a product or service that a customer does not expect to receive but that gives pleasure to the customer when received.

Demerit chart: A control chart for evaluating a process in terms of a demerit (or quality score), in other words, a weighted sum of counts of various classified nonconformities.

Deming cycle: Sometimes called the Shewhart cycle (see "plan-do-check-act cycle").

Deming Prize: Award given annually to organizations that, according to the award guidelines, have successfully applied companywide quality control based on statistical quality control and will keep up with it in the future. Although the award is named in honor of W. Edwards Deming, its criteria are not specifically related to Deming's teachings. There are three separate divisions for the award: the Deming Application Prize, the Deming Prize for Individuals and the Deming Prize for Overseas Companies. The award process is overseen by the Deming Prize Committee of the Union of Japanese Scientists and Engineers in Tokyo.

Deming, W. Edwards (deceased): A prominent consultant, teacher and author on the subject of quality. After Deming shared his expertise in statistical quality control to help the U.S. war effort during World War II, the War Department sent him to Japan in 1946 to help that nation recover from its wartime losses. Deming published more than 200 works, including the well-known books Quality, Productivity, and Competitive Position and Out of the Crisis. Deming, who developed the 14 points for managing, was an ASQ Honorary Member.

Dependability: The degree to which a product is operable and capable of performing its required function at any randomly chosen time during its specified operating time, provided that the product is available at the start of that period. (Nonoperation related influences are not included.) Dependability can be expressed by the ratio: time available divided by (time available + time required).

Deployment: Dispersion, dissemination, broadcasting or spreading of a communication throughout an organization, downward and laterally.

Design of experiments (DOE): A branch of applied statistics dealing with planning, conducting, analyzing and interpreting controlled tests to evaluate the factors that control the value of a parameter or group of parameters.

Design for manufacture (DFM): The principle of designing products so that they are cost effective and easy to make.

Design for Six Sigma (DFSS): See "DMADV."

Designing in quality vs. inspecting in quality: See "prevention vs. detection."

Desired quality: The additional features and benefits a customer discovers when using a product or service that leads to increased customer satisfaction. If it is missing, a customer may become dissatisfied.

Deviation: In numerical data sets, the difference or distance of an individual observation or data value from the center point (often the mean) of the set distribution.

DFSS (Design for Six Sigma): A systematic methodology utilizing tools, training, and measurements to enable us to design products and processes that meet customer expectations and can be produced at Six Sigma quality levels. (Steve Littleton)

Diagnosis: The activity of discovering the cause(s) of quality deficiencies; the process of investigating symptoms, collecting and analyzing data, and conducting experiments to test theories to determine the root cause(s) of deficiencies.

Diagnostic journey and remedial journey: A two-phase investigation used by teams to solve chronic quality problems. In the first phase, the diagnostic journey, the team journeys from the symptom of a chronic problem to its cause. In the second phase, the remedial journey, the team journeys from the cause to its remedy.

Discreet data: Data that are counted instead of measured. Only a finite number of values are possible.

Dispersion: The degree of scatter of data, usually about an average value, such as the median.

Dissatisfiers: The features or functions a customer expects that either are not present or are present but not adequate; also pertains to employees' expectations.

Distribution (statistical): The amount of potential variation in the outputs of a process, typically expressed by its shape, average or standard deviation.

DMADV: A data driven quality strategy for designing products and processes, it is an integral part of a Six Sigma quality initiative. It consists of five interconnected phases: define, measure, analyze, design and verify.

DMAIC (define, measure, analyze, improve, and control): A process for continued improvement. It is systematic, scientific, and fact based. This closed-loop process eliminates unproductive steps, often focuses on new measurements, and applies technology for improvement. (Steve Littleton)

Dodge, Harold F. (deceased): An ASQ founder and Honorary Member. His work with acceptance sampling plans scientifically standardized inspection operations and provided controllable risks. Although he usually is remembered for the Dodge-Romig sampling plans developed with Harry G. Romig, Dodge also helped develop other basic acceptance sampling concepts (consumer's risk, producer's risk, average outgoing quality level) and several acceptance sampling schemes.

Dodge-Romig sampling plans: Plans for acceptance sampling developed by Harold F. Dodge and Harry G. Romig. Four sets of tables were published in 1940: single sampling lot tolerance tables, double sampling lot tolerance tables, single sampling average outgoing quality limit tables and double sampling average outgoing quality limit tables.

DOE (design of experiments)

  • Full factorial: Balanced, designed experiments that test each possible combination of levels that can be formed from the input factors.
  • Fractional factorial: Experiments that use only a balanced portion of the full factorial array. A fraction of the runs required by a full factorial experiment are completed to obtain reasonable estimates of the main factors and the lower order interactions.
  • RSM (response surface method): A technique that enables the experimenter to find the point of highest yield (KPOV), given at least two significant factors (KPIVs).
  • EVOP (evolutionary operation): A continuous improvement technique that allows an operator to make minor adjustments to keep a process centered at the optimum using small design of experiments.
  • Screening: Type of DOE used to determine which factors impact the process the most.
  • Characterization: Type of DOE used to produce the Y=f(x) equation using only the most important factors.
  • Optimization: Type of DOE used to find the optimum operating point of a process.
  • Trial & error: Traditional method of design optimization that addresses each parameter individually, ending when a feasible design is found. Generally considered to be inefficient and ineffective at achieving an optimal design, particularly for complex design problems.
  • OFAT (one factor at a time): Methodology in which all factors are held constant and then varied one at a time.
  • Replication: Duplicating an experiment using the same experimental unit. Treatment combinations are not repeated consecutively. Produces enough data to investigate error of the experiment.
  • Repetition: Performing several experimental runs consecutively using the same treatment combinations. Produces enough data to calculate the mean and standard deviation of the experimental results.
  • Blocking: A grouping technique to eliminate factors that are of no interest to an experiment. Isolates and minimizes the impact of factors that could otherwise obscure the main effects.
  • Center points: Center points repeat or replicate a run at the center or midpoint of all quantitative factor levels. Often run at current factor levels, they are used to detect accuracy of linear fit.
  • Randomization: Process that reduces the impact of noise and helps prevent the confounding of effects. Experimental units are assigned to treatments randomly instead of systematically or according to a standard order.
  • Control factors: Input variables that can be set and maintained, or controlled.
  • Noise factors: Input variables that are not controlled.
  • Response: The output of a process. Also called dependent variables.
  • Lack of fit: The variation due to model inaccuracy. If repeated response values are observed at certain settings of the predictors (the factors), the unexplained variation can be divided into two parts: lack of fit and pure error. Lack of fit = SSResiduals - SSPure Error.
  • Fit: The Y value obtained from the prediction equation.
  • Residuals: The difference between the predicted Y value and the observed Y output value.
  • Taguchi signal to noise: Ratio developed by Genichi Taguchi to reflect variability caused by noise in the response of a system. Noise factors interfere with the intended response, or the "signal"; thus, the larger the S/N ratio, the more robust the performance.

DPMO (defects Per million opportunities): The actual number of defects occurring divided by the total number of opportunities for a defect and multiplied by 1million. Also referred to as ppm (parts per million).

DPU: Defects per unit. Calculated by dividing the total number of defects by the number of units or products.

Driving forces: Forces that tend to change a situation in desirable ways.

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