New Product Development and Project Management Decisions

Grushka‐Cockayne, Yael; Wooten, Joel O.

doi:10.1002/9781119138341.ch10

Cited by 8 publications

(1 citation statement)

References 80 publications

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“…From this perspective, the planning fallacy can be considered an instance of a general optimism bias (Lovallo and Kahneman 2003). Extant research (see Grushka-Cockayne et al 2018, for a review) identifies several techniques to mitigate the planning fallacy, such as unpacking/decomposing a project into subtasks (Connolly and Dean 1997, Forsyth and Burt 2008, Kruger and Evans 2004, using predictions by observers instead of self-predictions (Newby-Clark et al 2000), or averaging independent estimates from a large group of individuals (Eubanks et al 2015).…”

Section: Relationship To the Literaturementioning

confidence: 99%

Improving the Accuracy of Project Schedules

Lorko

Servátka

Zhang

2021

Production and Operations Management

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How to avoid project failures driven by overoptimistic schedules? Managers often attempt to mitigate the duration underestimation and improve the accuracy of project schedules by providing their planners with excessively detailed project specifications. While this traditional approach may be intuitive, solely providing more detailed information has proven to have a limited effect on eliminating behavioral biases. We experimentally test the effectiveness of providing detailed specification and compare it to an alternative intervention of providing historical information about the average duration of similar projects in the past. We find that both interventions mitigate the underestimation bias. However, since providing detailed project specification results in high variance of estimation errors due to sizable over-and underestimates, only the provision of historical information leads to more accurate project duration estimates. We also test whether it is more effective to anchor planners by providing historical information simultaneously with the project specification or to provide the historical information only after beliefs regarding the project duration are formed, in which case planners can regress their initial estimates towards the historical average. We find that the timing of disclosing information does not play a role as the estimation bias is mitigated and the accuracy is improved in both conditions. Finally, we observe that the subjective confidence in the accuracy of duration estimates does not vary across the interventions, suggesting that the confidence is neither a function of the amount nor the detail of available information.

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Section: Relationship To the Literaturementioning

confidence: 99%

Improving the Accuracy of Project Schedules

Lorko

Servátka

Zhang

2021

Production and Operations Management

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Mapping quality performance through Lean Six Sigma and new product development attributes

Kumar

Cudney

Mittal³

et al. 2023

TQM

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PurposeNew product development (NPD) is necessary for business sustenance and customer satisfaction. Six Sigma and Design for Lean Six Sigma (DLSS) efficiently employ the repetitive stages for NPD, leading to quality performance and profitability. This study aims to map the quality performance through NPD attributes through the Lean methodology.Design/methodology/approachThe data on NPD were collected from 267 respondents from manufacturing companies to map the relationship between Six Sigma and DLSS for NPD. Confirmatory factor analysis was employed to confirm model fit, while structural equation modeling was employed to analyze the empirical data for framework testing. The study included nine variables and fourteen hypotheses identified from the literature.FindingsThe statistical results of this study show that NPD attributes such as innovation, marketing, organization, customer, product and technology positively influence the Lean Six Sigma structured improvement process (LSSSIP) and DLSS. Moreover, integrating these attributes in Lean planning enhance quality performance. This empirical investigation's findings indicate that ten of the 14 hypotheses were supported, giving the study a strong foundation.Research limitations/implicationsThe data collection was limited to northern India; therefore, the results may not be generalizable to other areas of the world.Practical implicationsNPD involves handling technical issues and factors such as cost, operational bottlenecks, economic changes, competitors' strategy and company policy. This study helps understand the various NPD parameters and their relationship to Lean, which enables an effective NPD implementation strategy.Originality/valueThe current philosophy of NPD calls for a concurrent engineering approach; therefore, the entire organization must be part of this process. This study uses the holistic framework by optimizing NPD with Lean Six Sigma (LSS) principles. The study is unique in that, to date, research does not integrate NPD attributes with the objectives of LSS to develop an efficient NPD implementation strategy.

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From Noise to Bias: Overconfidence in New Product Forecasting

Feiler

Tong

2022

Management Science

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We study decision behavior in the selection, forecasting, and production for a new product. In a stylized behavioral model and five experiments, we generate new insight into when and why this combination of tasks can lead to overconfidence (specifically, overestimating the demand). We theorize that cognitive limitations lead to noisy interpretations of signal information, which itself is noisy. Because people are statistically naive, they directly use their noisy interpretation of the signal information as their forecast, thereby underaccounting for the uncertainty that underlies it. This process leads to unbiased forecast errors when considering products in isolation, but leads to positively biased forecasts for the products people choose to launch due to a selection effect. We show that this selection-driven overconfidence can be sufficiently problematic that, under certain conditions, choosing the product randomly can actually yield higher profits than when individuals themselves choose the product to launch. We provide mechanism evidence by manipulating the interpretation noise through information complexity—showing that even when the information is equivalent from a Bayesian perspective, more complicated information leads to more noise, which, in turn, leads to more overconfidence in the chosen products. Finally, we leverage this insight to show that getting a second independent forecast for a chosen product can significantly mitigate the overconfidence problem, even when both individuals have the same information. This paper was accepted by Charles Corbett, operations management.

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New Product Development and Project Management Decisions

Cited by 8 publications

References 80 publications

Improving the Accuracy of Project Schedules

Improving the Accuracy of Project Schedules

Mapping quality performance through Lean Six Sigma and new product development attributes

From Noise to Bias: Overconfidence in New Product Forecasting

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