Dynamic decision modeling demonstrates structured threshold policies for agile product development, indicating selective iteration outperforms continuous improvement under uncertainty.
Key Points
Determine the optimal joint dynamic policy for coordinating when a firm should solicit customer feedback versus when it should invest in product quality improvements.
Modeled the coordination problem as a semi-Markov decision process with product quality and accumulated customer feedback as primary state variables.
Analyzed isolated single-action decision baselines before evaluating the full joint optimization problem and conducting parameter sensitivity analyses.
The optimal joint policy preserves a monotone-threshold structure where feedback solicitation follows an optimal stopping rule and quality investment is selectively deployed based on the product state.
The state space partitions into four distinct operational regimes, revealing that selective agility—such as pausing quality investments to await customer input—outperforms continuous iteration.