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Modeling and inferring about the process using growth models are the problems of enormous practical importance. Growth behavior of melting point (MP) during hydrogenation is found to be nonlinear. The purpose of this paper is to propose a control chart based method for on-line detection of a growth process becoming dead.

The nonlinear growth kinetics of MP during hydrogenation is modeled as a random walk with drift. In earlier work, the random walk model is developed based on a linear approximation and the control chart is constructed based on this approximate model. Here, an alternative model that does not make use of any such approximation is proposed. The variable drift component of the random walk is estimated following an innovative method of instrumental variable estimation. The model thus obtained is then used to construct a new control chart.

It is shown that both the control charts are able to detect dead batches satisfactorily, but the new chart is superior to the earlier one.

The authors are not aware of any relevant literature which provides an implementable and practitioner friendly approach to model the usually cumbersome variance function using signal-to-noise ratio and then use the same for estimating the parameters of a nonlinear dynamic growth model.

This paper aims to highlight that a define, measure, analyze, improve, and control (DMAIC) project should be carried out keeping the broader business goal of achieving continuous improvement in mind and that a design of experiment (DOE) based improvement approach should be preferred to achieve this goal.

“Ease of control” of the improved process and “gain in process knowledge” from a DMAIC study are identified as two measures for judging the contribution of a DMAIC project towards continuous improvement. Various improvement approaches are classified into seven groups and the likely impact of each of these seven approaches on the above two quality measures are discussed.

The improvement approach adopted during the improve phase is partially determined by the nature of the root cause(s) – type X or type Y. The type Y root cause leads to the adoption of the “innovation‐prioritization” approach, which is very popular but has many limitations. Accordingly, an “analysis strategy” is proposed for efficient identification of the X‐type root causes.

The above findings suggest that one should try to identify as many X‐type root causes as possible. However, in case of service and transactional processes one finds it difficult to do so. Much more research is necessary in the area of service process design before the path of continuous improvement of such processes can be embarked on effectively.

It is expected that an awareness of the broader goal of continuous improvement, the classification of the end states of the analyze phase, the proposed “analysis strategy” and the practical guidelines provided for selecting an appropriate improvement approach will be helpful in executing the analyze and improve phases of DMAIC better.