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IMPLEMENTATION ISSUES OF TIME‐SERIES BASED STATISTICAL PROCESS CONTROL
Author(s) -
ALWAN LAYTH C.,
RADSON DARRELL
Publication year - 1995
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/j.1937-5956.1995.tb00056.x
Subject(s) - control chart , statistical process control , computer science , series (stratigraphy) , autocorrelation , independent and identically distributed random variables , control (management) , statistical model , process (computing) , time series , statistical hypothesis testing , econometrics , industrial engineering , operations research , random variable , statistics , machine learning , mathematics , artificial intelligence , engineering , paleontology , biology , operating system
Underlying standard control‐chart methodology is the assumption of independent and identically distributed (IID) random variables. However, autocorrelation and other time‐series effects frequently occur in manufacturing and service quality applications. As a result, it has been suggested that standard methods be extended by using time‐series modeling. A little statistical analysis can go a long way, especially when time‐series effects are strong enough to frustrate efforts directed toward discovery of special causes. But a practical limitation on the use of time‐series modeling is that its implementation requires some sophisticated statistical skills, whereas standard control charts require only elementary statistical knowledge. The choice raises the questions of management philosophy, statistical techniques, and computation.