Task Decomposition and Newsvendor Decision Making

Lee, Yun Shin; Siemsen, Enno

doi:10.2139/ssrn.2232073

Cited by 21 publications

(26 citation statements)

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“…Although this particular form of decision bias is widely observed in practice and has been experimentally validated in related settings (Haran et al. , Lee and Siemsen , Moore and Healy , Ren and Croson ), we acknowledge that there exist other viable forms of decision bias such as anchoring that could be adopted instead; this is discussed in Appendix EC.2. In completing these two extensions, we find that, qualitatively consistent with the results of this study, overconfidence ultimately can result in both firm‐win and channel‐win scenarios (Propositions EC.1–EC.8).…”

Section: Discussionmentioning

confidence: 99%

Overconfident Distribution Channels

2019

Production and Operations Management

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W e study the effects associated with overconfidence in distribution channels, where overconfidence is defined as a decision maker's cognitive bias in perceiving the expected outcome of an uncertain event as more certain than it likely is. Although overconfidence bias always leads to a lower expected profit for a centralized channel, we find that overconfidence can in fact enhance the performance of a decentralized channel comprising one overconfident manufacturer and retailer. That is, overconfidence can reduce the double marginalization effect so that, compared to a decentralized channel managed by unbiased firms, the profit of an overconfident decentralized channel can be higher. In a similar vein, overconfidence bias can benefit, rather than hurt, either or both channel members. Our results shed some light on the design and adoption of strategies aimed at enhancing decisions and curtailing overconfidence bias of supply chain executives.

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Section: Discussionmentioning

confidence: 99%

Overconfident Distribution Channels

2019

Production and Operations Management

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“…In another study, Webby et al (2005) observed similar results when the effects of special events were estimated separately. Also, Lee and Siemsen (2017) demonstrated the value of task decomposition on order decisions, especially when coupled with decision support.…”

Section: Literaturementioning

confidence: 99%

Judgmental selection of forecasting models

Petropoulos

Kourentzes

Νικολόπουλος

et al. 2018

J of Ops Management

Self Cite

135

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In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also examined the efficacy of a judgmental model‐build approach, in which experts were asked to decide on the existence of the structural components (trend and seasonality) of the time series instead of directly selecting a model from a choice set. Our behavioral study used data from almost 700 participants, including forecasting practitioners. The results from our experiment suggest that selecting models judgmentally results in performance that is on par, if not better, to that of algorithmic selection. Further, judgmental model selection helps to avoid the worst models more frequently compared to algorithmic selection. Finally, a simple combination of the statistical and judgmental selections and judgmental aggregation significantly outperform both statistical and judgmental selections.

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“…Finally, REDO can also be viewed as a type of task‐decomposition approach (e.g., see Armstrong , Lee and Siemsen , MacGregor et al. , Raiffa ).…”

Section: A Remedy For the Censorship Bias: Record Estimates Of Demandmentioning

confidence: 99%

“…), forcing individuals to commit to standing orders (Bolton and Katok ), manipulating the salience of the psychological costs of leftovers and stockouts (Ho et al. ), reducing the frequency of feedback (Lurie and Swaminathan ), and decomposing the newsvendor decision into subtasks (Lee and Siemsen ) have also proven to be at least partially effective in reducing the pull‐to‐center effect.…”

Section: Introductionmentioning

confidence: 99%

A Behavioral Remedy for the Censorship Bias

Tong

Feiler

Larrick³

2018

Production and Operations Management

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Existing evidence suggests that managers exhibit a censorship bias: demand beliefs tend to be biased low when lost sales from stockouts are unobservable (censored demand) compared to when they are observable (uncensored demand). We develop a non‐constraining, easily implementable behavioral debias technique to help mitigate this tendency in demand forecasting and inventory decision‐making settings. The debiasing technique has individuals record estimates of demand outcomes (REDO): participants explicitly record a self‐generated estimate of every demand realization, allowing them to record a different value than the number of sales in periods with stockouts. In doing so, they construct a more representative sample of demand realizations (that differs from the sales sample). In three laboratory experiments with MBA and undergraduate students, this remedy significantly reduces downward bias in demand beliefs under censorship and leads to higher inventory order decisions.

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Task Decomposition and Newsvendor Decision Making

Cited by 21 publications

References 0 publications

Overconfident Distribution Channels

Overconfident Distribution Channels

Judgmental selection of forecasting models

A Behavioral Remedy for the Censorship Bias

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