Conditional Leadtime Flexibility in an Assemble-to-Order System

Problem definition: We introduce and formalize a concept termed as conditional leadtime flexibility (CLF), which refers to the industry practice where a manufacturer requests its upstream suppliers to dynamically adjust the leadtimes. We consider an assemble-to-order (ATO) manufacturer's decisions on inventory replenishment and order adjustment over a finite horizon to minimize total discounted expected cost. Methodology/results: We formulate the problem as a two-stage Markov Decision Process (MDP) with an enlarged state space to track the adjustable pipeline orders. The replenishment and leadtime adjustment decisions are made in the first and second stages, respectively. We show that (i) the multidimensional optimal cost function of the MDP can be decomposed into a sum of convex and univariate functions; and (ii) both the optimal replenishment and leadtime adjustment decisions follow base-stock policies. We extend the analysis to consider costly CLF and relate/contrast our model to the dual-sourcing models. Managerial implications: Based on real industry data, we numerically find that (i) CLF achieves notable cost savings when its probability of being granted is 50% or above; (ii) even when the acceptance probability of CLF is only 30%, its value is equivalent to a 15% reduction in unit ordering cost; and (iii) flexibly adjusting a component with the maximum leadtime saves the most cost. Our results underscore the importance of CLF and provide a handy tool for optimally automating CLF in ATO systems