Optimal production ramp‐up in the smartphone manufacturing industry

Motivated by challenges in the smartphone manufacturing industry, we develop a dynamic production ramp‐up model that can be applied to economically satisfy nonstationary demand for short‐life‐cycle products by high‐tech companies. Due to shorter life cycles and more rapid evolution of smartphones, production ramp‐up has been increasingly critical to the success of a new smartphone. In the production ramp‐up, the key challenge is to match the increasing capacity to nonstationary demand. The high‐tech smartphone manufacturers are urged to jointly consider the effect of increasing capacity and decreasing demand. We study the production planning problem using a high‐dimensional Markov decision process (MDP) model to characterize the production ramp‐up. To address the curse of dimensionality, we refine Monte Carlo tree search (MCTS) algorithm and theoretically analyze its convergence and computational complexity. In a real case study, we find that the MDP model achieves revenue improvement by stopping producing the existing product earlier than the benchmark policy. In synthetic instances, we validate that the proposed MCTS algorithm saves computation time without loss of solution quality compared with traditional value iteration algorithm. As part of the Lenovo production solution, our MDP model enables high‐tech smartphone manufacturers to better plan the production ramp‐up.