Advisor
Schaffer, Jay
Committee Member
Lalonde, Trent
Committee Member
Holighi, Khalil Shafie
Department
Applied Statistics and Research Methods
Institution
University of Northern Colorado
Type of Resources
Text
Place of Publication
Greeley (Colo.)
Publisher
University of Northern Colorado
Date Created
8-2017
Genre
Thesis
Extent
237 pages
Digital Origin
Born digital
Abstract
Industrial process quality control frequently uses the Exponentially Weighted Moving Average control chart (EWMA CC) and the double EWMA CC (DEWMA CC) to detect small shifts in a process when the sample size 𝑛=1. The EWMA CC was initially developed and evaluated in 1959. In 2005, the EWMA technique was extended to the DEWMA. Continued research into DEWMA has developed and assessed several alternatives, including multivariate control charts. These studies focus on detecting small shifts in process. In practice, however, we occasionally wish to detect small trends instead of shifts in the process. The effectiveness of these methods to determine small trends in a process has not been thoroughly researched in the current literature. This research proposes a new control chart, based on the fundamental theorem of exponential smoothing prediction, first presented by Brown and Meyer in 1961. The new chart is called “The Double Exponentially Weighted Moving Average Based on a Linear Prediction” (DEWMABLP) control chart. This study presents a simulation to contrast the efficiency of DEWMABLP, EWMA, DEWMA, and classical Shewhart control charts when small trends are introduced. A conclusion is the DEWMABLP control chart can be used to monitoring small shifts. Also, results suggest that the new control chart is more efficient than the other control charts not only for small drifts, but also for small shifts.
Degree type
PhD
Degree Name
Doctoral
Language
English
Local Identifiers
PerezAbreuCarrion_unco_0161D_10586
Rights Statement
Copyright is held by the author.
