Determining safety stock in the presence of stochastic lead time and demand
Management Science
The general optimal market area model
Annals of Operations Research
Pricing and the News Vendor Problem: a Review with Extensions
Operations Research
Selling to the Newsvendor: An Analysis of Price-Only Contracts
Manufacturing & Service Operations Management
Coordination and Flexibility in Supply Contracts with Options
Manufacturing & Service Operations Management
Generating Scenario Trees for Multistage Decision Problems
Management Science
Expected Value of Distribution Information for the Newsvendor Problem
Operations Research
Retailer-Supplier Flexible Commitments Contracts: A Robust Optimization Approach
Manufacturing & Service Operations Management
A Robust Optimization Approach to Inventory Theory
Operations Research
Regret in the Newsvendor Model with Partial Information
Operations Research
Estimating Demand Uncertainty Using Judgmental Forecasts
Manufacturing & Service Operations Management
Advanced resource planning as a decision support module for ERP
Computers in Industry
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Decision support tools are increasingly used in operations where key decision inputs such as demand, quality, or costs are uncertain. Often such uncertainties are modeled with probability distributions, but very little attention is given to the shape of the distributions. For example, state-of-the-art planning systems have weak, if any, capabilities to account for the distribution shape. We consider demand uncertainties of different shapes and show that the shape can considerably change the optimal decision recommendations of decision models. Inspired by discussions with a leading consumer electronics manufacturer, we analyze how four plausible demand distributions affect three representative decision models that can be employed in support of inventory management, supply contract selection and capacity planning decisions. It is found, for example, that in supply contracts flexibility is much more appreciated if demand is negatively skewed, i.e., has downside potential, compared to positively skewed demand. We then analyze the value of distributional information in the light of these models to find out how the scope of improvement actions that aim to decrease demand uncertainty vary depending on the decision to be made. Based on the results, we present guidelines for effective utilization of probability distributions in decision models for operations management.