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A key process input variable (KPIV) is a process input that provides a significant impact on the output variation of a process or a system or on the key process output variable (KPOV) of a product. This means that the KPOV is determined by the KPIV; so, if the KPIV is held constant, then it would yield a predictable and consistent output. As such, the KPIV determines the overall quality of the KPOV or, simply, the quality of the output of either a process or a product.
The KPIV determines the output of a process or product, which is the KPOV. For example, if the KPOV is the traction provided by a certain model of a car tire, then the KPIVs would be the width of the tire and the compound used to make it. Certain combinations of the two KPIVs will result in a particular KPOV (traction), so if the KPIVs are kept constant, then they will yield a specific traction rating, and if these variables are changed, then the tire model obtained will either have a high traction, which can be more expensive, or have a low traction yet more affordable, and these two variations will fill different segments of the market.
Another good example of KPIV is the size and number of transistors that are placed in a microchip. These two directly affect the capacity, speed and power consumption of the chip. As the size gets smaller, the power consumption of each transistor also decreases and it allows for more transistors to be placed in the same small space. This results in a more efficient and powerful chip that consumes less power.
The challenge is in determining the correct KPIV in a product or system that would yield the most favorable results for the selected KPOVs. This can be done through experimentation, but this would cost too much and would be too imprecise. Design of experiments (DOE) is a tool used for conducting a structured and scientific experimentation in order to efficiently model different process behaviors and understand the underlying cause-and-effect relationships that link KPIV and KPOV.
Examples of KPIV: