According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Economic Control of Quality of Manufactured Product.

The problem of using time-varying trajectory data measured on many process variables over the finite duration of a batch process is considered. Multiway principal-component analysis is used to compress the information contained in the data trajectories into low-dimensional spaces that describe the operation of past batches. This approach facilitates the analysis of operational and quality-control problems in past batches and allows for the development of multivariate statistical process control charts for on-line monitoring of the progress of new batches. Control limits for the proposed charts are developed using information from the historical reference distribution of past successful batches. The method is applied to data collected from an industrial batch polymerization reactor.

https://www.jstor.org/stable/pdfplus/1269152.pdf

Multivariate SPC charts for monitoring batch processes

This paper takes a broad, pragmatic view of statistical inference to include all aspects of model formulation. The estimation of model parameters traditionally assumes that a model has a prespecified known form and takes no account of possible uncertainty regarding the model structure. This implicitly assumes the existence of a 'true' model, which many would regard as a fiction. In practice model uncertainty is a fact of life and likely to be more serious than other sources of uncertainty which have received far more attention from statisticians. This is true whether the model is specified on subject-matter grounds or, as is increasingly the case, when a model is formulated, fitted and checked on the same data set in an iterative, interactive way. Modern computing power allows a large number of models to be considered and data-dependent specification searches have become the norm in many areas of statistics. The term data mining may be used in this context when the analyst goes to great lengths to obtain a good fit. This paper reviews the effects of model uncertainty, such as too narrow prediction intervals, and the non-trivial biases in parameter estimates which can follow data-based modelling. Ways of assessing and overcoming the effects of model uncertainty are discussed, including the use of simulation and resampling methods, a Bayesian model averaging approach and collecting additional data wherever possible. Perhaps the main aim of the paper is to ensure that statisticians are aware of the problems and start addressing the issues even if there is no simple, general theoretical fix.

https://rss.onlinelibrary.wiley.com/doi/pdf/10.2307/2983440

Model uncertainty, data mining and statistical inference

: Scientific learning is an iterative process employing Criticism and Estimation. Correspondingly the formulated model factors into two complimentary parts - a predictive part allowing model criticism, and a Bayes posterior part allowing estimation. Implications for significance tests, the theory of precise measurement, and for ridge estimates are considered. Predictive checking functions for transformation, serial correlation, bad values, and their relation with Bayesian options are considered. Robustness is seen from a Bayesian viewpoint and examples are given. For the bad value problem a comparison with M estimators is made. (Author)

Sampling and Bayes' inference in scientific modelling and robustness

Rationales for process monitoring using some of the techniques of statistical process control and for feedback adjustment using some techniques associated with automatic process control are explored, and issues that sometimes arise are discussed. The importance of some often unstated assumptions are illustrated. Minimum-cost feedback schemes are discussed for some simple, but practically interesting, models.

Tim Kramer

Papers

Statistical process monitoring and feedback adjustment: a discussion

[Statistical Process Monitoring and Feedback Adjustment]: Response

Statistical Process Control and Automatic Process Control: A Discussion