Estimating the effect of productivity when implementing new technology into an existing production process can guide a development team during the research and design phase of the system’s life-cycle. The study employs two productivity tools: Data Envelopment Analysis (DEA) to evaluate the current system’s efficiency; and the time constant learning curve to project productivity of the new system.

Productivity measurement and evaluation of the current system is paramount to increasing efficiency over long periods. DEA was used for this evaluation because of its ability to handle units with multiple inputs and outputs and make comparisons of their relative efficiencies. Correcting inefficient practices before the system implementation phase begins will potentially allow greater initial productivity from the new technology.

Future productivity can be projected using the time constant learning curve. This model allows for the estimation of productivity based on initial and steady state processing times, and the expected quantity of inputs and outputs. Based on this data, the system development team can make necessary changes to the system’s design to allow for greater productivity. These changes can be made early in the system's life-cycle to prevent extensive rework after implementation. The method also allows for the production element to anticipate and plan for changes in its operational practices.