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Metrics for Measuring Product Development Cycle Time

Abbie Griffin·1993·Journal of Product Innovation Management

Abstract

As global competitive pressure increases and product life cycles compress, many companies are trying to shorten their product development cycles. Firms are implementing a wide variety of different techniques, management processes and development strategies in their quest for shorter development cycles. We read anecdotal accounts of some efforts that herald great success stories but seldom hear about any failures. Unfortunately, some of the companies changing their development processes do so without any a priori basis for determining whether the process change will have helped or hindered them. The firm implements the new process without having a cycle time performance baseline against which to compare results from the new process. In this article, Abbie Griffin presents a method for obtaining product development cycle time performance baselines. She also demonstrates how to use them to either forecast expected project duration, given that you have not changed your development process, or determine whether a process change has actually decreased development cycle times.

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Metadata

Authors
Griffin, A.
Venue
Journal of Product Innovation Management
Publisher
Wiley
Published
1993-03-01
Volume
10
Issue
2
Pages
112-125
ISSN
0737-6782 (ISSN-L)
OpenAlex
W2060821385
Cited by
338
References
0
FWCI
26.75
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Metrics for Measuring Product Development Cycle Time. https://app.cochat.ai/papers/10.1016/0737-6782(93)90003-9
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Griffin, A. (1993). Metrics for Measuring Product Development Cycle Time. Journal of Product Innovation Management. https://doi.org/10.1016/0737-6782(93)90003-9

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.