Inventory control in a fluctuating demand environment
Operations Research
Quick response in manufacturer-retailer channels
Management Science - Special issue on frontier research in manufacturing and logistics
A Single-Item Inventory Model for a Nonstationary Demand Process
Manufacturing & Service Operations Management
The Censored Newsvendor and the Optimal Acquisition of Information
Operations Research
Adaptive Inventory Control for Nonstationary Demand and Partial Information
Management Science
Integrating Replenishment Decisions with Advance Demand Information
Management Science
The Value of Information Sharing in a Two-Level Supply Chain
Management Science
A Time-Series Framework for Supply-Chain Inventory Management
Operations Research
Evolution of ARMA Demand in Supply Chains
Manufacturing & Service Operations Management
Operations Management
Approximate Solutions of a Dynamic Forecast-Inventory Model
Manufacturing & Service Operations Management
Manufacturing & Service Operations Management
Hi-index | 0.01 |
In this paper, we consider a periodic review inventory problem where demand in each period is modeled by linear regression. We use a Bayesian formulation to update the regression parameters as new information becomes available. We find that a state-dependent base-stock policy is optimal and we give structural results. One interesting finding is that our structural results are not analogous to classical results in Bayesian inventory research. This departure from classical results is due to the role that the independent variables play in the Bayesian regression formulation. Because of the computational complexity of the optimal policy, we propose a combination of two heuristics that simplifies the Bayesian inventory problem. Through analytical and numerical evaluation, we find that the heuristics provide near-optimal results.