A system dynamics modeling framework for the strategic supply chain management of red chile

A new framework for measuring change in the red chile supply chain is proposed. This framework estimates performance improvement in the harvest function based on time measurement as a result of a change in technology from the traditional system. Two models comprise the new framework. The first model estimates process time for each individual component within the traditional system. The second model estimates process time for each individual component in the same system with the inclusion of a moduling component for late season red chile.;The system dynamics modeling framework is based on the concept that endogenous change is fundamental to the system dynamic and that these changes dictate the formulation of the model. Exogenous disturbances merely initiate system behavior. Operational frameworks consisting of traditional implicit mental models limit the ability to measure and analyze interactions.;StellaRTM, a systems thinking modeling software was utilized to simulate and evaluate the overall performance of the visualized red chile supply chain. The dynamic behavior of the system was simulated using stocks and flows based on mathematical models of the major system components that represent the behavior of the system using the Euler integration method.;Simulations were conducted to illustrate a single operating season for an individual red chile processing facility. Yield levels were varied to illustrate the impact of moduling on the harvest season at different levels of production. However, all other constraints and capacities between traditional and moduled simulations remain consistent to avoid skewing any process improvement.;The system dynamics modeling framework in this research yielded several conclusions. First, in the event that harvest capacity exceeds orders and the length of the overall season goes beyond December 10th, modeling of red chile at the field reduces the length of the harvest season. Second, any increase in process improvement to the harvest function when modeling occurs is proportional to the length of the overall season. Finally, this new framework provides a methodology and baseline to aid growers and processors in evaluating process improvement as a function of time measurement in days and can serve as a guide for policy decision makers.