DCT is a breakthrough process engineering technology that is transforming the landscape of process development.  It represents a dramatic improvement over traditional approaches and methodologies such as trial-and-error, one-factor-at-a-time (OFAT), and Design of Experiments (DOE).

DCT is based on the principles of five powerful modern systems theories. These are:

DCT and its foundational system theories

Unlike traditional methodologies, DCT operates on a new short range optimization model which optimizes the configuration of the system and the parameter values simultaneously.

The optimization of any system using DCT or the traditional methodologies generally requires these three steps:

    1. Define the system objective and its parameters
    2. Design the optimal configuration of the system
    3. Optimize system parameter values
Traditional Long Range Model

Traditional Long Range Model

In the traditional “long range model” these steps proceed sequentially as illustrated here:

With DCT, designing the optimal configuration of system parameters and the optimization of parameter values occurs at the same time as illustrated here:


DCT New Short Range Model

With the traditional methodologies, a subset of parameters must first be selected. This is typically a very time-consuming and resource-intensive process that relies heavily on the expertise of the individuals involved.  It requires a lot of trial and error, is time consuming, expensive and very susceptible to unconscious bias.  Its weakness lies in the very real possibility that unimportant parameters might be included and important ones excluded.  Techniques such as DOE are then applied to try and optimize the values of these parameters.

In the short range model used in DCT, rather than select a manageable number of parameters for adjustment, the process engineer is encouraged to broaden their approach by considering all potentially related parameters.  This, of course, delivers a much wider field of initial parameters and because of this it is much more likely to reveal the optimum configuration, and at a much earlier stage.

DCT allows this optimization of parameters simultaneously with a systematic and comprehensive approach which evaluates and weighs individual parameters based on their contribution to the overall objective. It thus optimizes the configuration of the system and the parameter values at the same time.  This requires many fewer experiments and substantially increases the efficiency of the process while leading to more reliable results achieved at lower cost.

* very complex systems may require more than 10 experiments