Handling uncertainty when and under which conditions products are circulated for remanufacturing is a major challenge. Stochastic models need to address the complexity of the product life cycle and additionally, accomplish the cold start problem, defined by the fact that historical data is often scarcely available. This paper explores how Markov chain modeling captures the stochastic nature of product life cycles under the constraints of the cold start problem. Our analysis reveals that simulated time-inhomogeneous discrete Markov chains with stochastic transition probabilities integrate the aspects of product aging, the property of closed loop product circulation and product remanufacturing by resetting the products to like-new conditions into one holistic model. Moreover, experts can still resort to well-understood probability distributions since they can be converted to transition probabilities needed by Markov chains. These findings suggest that our Markov chain model has the potential to become a reference model for equivalent problems like reusing or repurposing since its flexibility allows for extending to other stochastic distributions and additional states in the circular loop.