Remediating system neglect in judgmental demand forecasting
Prior research has shown that individuals tasked with judgmental forecasting of demand based on time-series data overreact in stable environments and underreact in unstable environments. Kremer et al. (2011) attributed this to the system neglect hypothesis, which claims that forecasters emphasize fo...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2023
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| Online Access: | https://ink.library.smu.edu.sg/etd_coll/468 https://ink.library.smu.edu.sg/context/etd_coll/article/1466/viewcontent/SVINAKOTA_DBA_2017_Remediating_System_Neglect.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Prior research has shown that individuals tasked with judgmental forecasting of demand based on time-series data overreact in stable environments and underreact in unstable environments. Kremer et al. (2011) attributed this to the system neglect hypothesis, which claims that forecasters emphasize forecast errors over the system parameters.
The present research investigates interventions that mitigate system neglect and address the causal factors for overreaction and underreaction. Given the desire by organizations to move towards touchless planning and automated decision-making, minimizing human judgment and understanding its drivers is of significant practical importance.
We tested four different interventions on an online subject pool and found that the base treatment (simplest method in terms of cognitive load) outperforms all other interventions. In contrast to Kremer et al.’s original work we found a disconnect between subject’s forecast adjustment scores and forecasting performance. |
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