Decision Bias in the Newsvendor Problem
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Transcript of Decision Bias in the Newsvendor Problem
Decision Bias in the Newsvendor Problem
with a Known Demand Distribution:
Experimental Evidence
Schweitzer and Cachon, 2000
Introduction
• Newsvendor problem: short selling season with stochastic demand, one order opportunity, balance between underage & overage costs.
• Problem is well-known, but there is little engagement with how managers make decisions.
• Order Q ex-ante is rarely the best order Q ex-post.
Introduction
• Managers’ decisions don’t correspond to expected profit-maximizing order Q (Fisher and Raman 1996), but no explanation provided.
• Anchoring and insufficient adjustment bias (Sterman 1989).
• Influence of Supply chain design (Croson and Donohue 1998).
Introduction
• Preferences other than profit maximization – risk-averse behavior (Eechhouldt et al. 1995).
• Inventory level heuristic.• Biased forecast – done away with
in this paper.
Descriptive Models
• Inefficient order Q not consistent with:- Risk aversion- Risk-seeking preferences- Prospect theory preferences- Loss aversion- Stockout aversion.- Undervaluing opportunity costs• Consistent with: - Preference to reduce ex-post inventory error- Anchoring and insufficient adjustment.
The Newsvendor Problem
• q, D, μ, F, f, c, p, s.
• High- and low-profit products:
Utility Maximizing Orders
Risk Neutral Preferences
• un(w) = w -> max un(w) = max E[π(q,D)]
• qn is the optimal risk-neutral order Q
Risk-averse and Risk-seeking Preferences
Prospect Theory Preferences
• Risk-averse over domain of gains and risk-seeking over domain of losses (Kahneman and Tversky 1979);
• reference point = current wealth.• with all gains = less than qn.• with all losses = more than qn.• with both = depends.
Loss-averse Preferences
q1<qn δq1/δλ<0
Waste-Averse Preferences
qt<qn
Stockout-Averse Preferences
qm>qn
Underestimating Opportunity Costs
qo<qn
Preference for Minimizing Ex-Post Inventory Level
Anchoring and Insufficient Adjustment
• mean anchor heuristic – on mean demand, adjusting towards qn.
• chasing demand – on prior order Q, adjusting towards prior D.
Experiment 1: Uniform Demand
• 34 MBA students• selling “wodgets”• not informed about total # of
rounds, or future price and cost.• 30 inventory decisions• critical fractiles: 25 and 75%-> 75
and 225 qn.
Experiment 1
Experiment 1
Experiment 1: Procedure
• Initial inventory decisions – unconfounded
• Average high-profit order Q = 176.68<225;
• Average low-profit order Q = 134.06>75.
• Double repeated measures generalized linear model.
• Mean and chasing demand heuristics.
Adjustment Scores
• mean anchor heuristic:
• chasing demand heuristic:
Experiment 2: High Demand Distribution
• 44 2nd year MBA students• losses are not possible <-
increased D range: low [1,300] and high [901,1200]
• qn low: 225 or 75• qn high: 1125 or 975
Results
• High-D: 186.88 or 1092.55• Low-D: 142.17 or 1021.81