Decision Bias in the Newsvendor Problem: On the Comparison of Managers and Students as Newsvendors with Decision Support System as Debiasing Strategy A growing number of studies in the newsvendor problem provided evidence that subjectsu0027 ordering behavior deviates from the optimal order quantity. Furthermore, almost all existing studies only engage students as subjects, and it leaves an essential question of whether the insights of those studies can be applied in real business practices. In this empirical study, we experimented with investigating decision biases in the newsvendor setting and presenting a structured comparison of the order decisions made by managers and students. We also proposed a Decision Support System as a debiasing strategy to prevent the order decision bias. To provide a piece of evidence about the effectiveness of the proposed DSS, we experimented with comparing the ordering behavior, before and after DSS implementation. This study found that both managers and students showed demand chasing bias, but the magnitude of the bias differed significantly. This study also showed that an informational DSS generally improved inventory decisionmaking performance in terms of adjustment behavior and longterm profitability.

Supply Process Improvement Decisions for a Newsvendor with Random Capacity Abstract   This paper considers a firm with random capacity that makes decisions on supply process improvement in a newsvendor setting. We find that the firm benefits from increased mean capacity, from reduced variance in capacity, and from monotonicity in the increasing concave order of capacity, despite diminishing returns. Moreover, we identify positive moderating effects on process improvements of profit margin and demand magnitude. When the capacity follows specific distributions whose mean and variance are jointly parameterized, we investigate comparative statics via analytical derivation and numerical experiments. The results are consistent with those obtained under a general distribution, where process improvement is more effective when initiated earlier rather than later. Analyzing various capacity distributions reveals that the random capacityu0027s optimal parameters related to its mean increase with the firmu0027s profit margin. However, the associations between these parameters and demand variability are subtle and may change under different procurement costs. The capacityu0027s optimal parameters related to its variance decrease with higher profit margins but increase with greater demand variability. Our findings yield several managerial insights.