Over the past decade, leasing, as opposed to purchasing, has gained prominence regarding acquiring capital-intensive industrial robots. Investments in plant automation have seen significant growth over this time given the increasing need for higher productivity and quality. To offset the concomitant rise in automation equipment costs, organizations are increasingly relying on leasing such equipment. The leasing approach allows companies to reduce both the risks associated with and costs of new equipment acquisition. Initial automation efforts typically tend to be experimental, and the leasing of equipment allows organizations to test the efficacy of such efforts before committing to a final leasing/purchasing decision without expending heavily. Despite there being vast extant literature on automation through the use of industrial robots, the dynamics and effects of leasing decisions still need to be understood better. Simulation based on System Dynamics (SD) has been used in this study given that it has been demonstrated to be robust to information scarcity, a problem typically associated with data underpinning leasing decisions. A new dynamic archetype based on SD, which is an upgrade of a previously established static technological archetype, modeling the economic effects of leasing an industrial robot for a manufacturing facility is proposed.