<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Although unsuccessful bids are usually discarded once the bids are opened under the typical lowest-price sealed-bid project award auction environment, considering efforts and resources expended by responsive and responsible bidders, it is likely that not only a successful bid but also unsuccessful bids can convey important information about the project at hand. This article documents an effort to answer the question that whether projects that receive more dispersed estimates at the bidding stage are more likely to experience greater project cost changes. Taking collective intelligence as the theoretical framework, a total of 210 projects conducted by the Ohio Department of Transportation between 2008 and 2018 were analyzed to investigate the relationship between the coefficients of variation of bids and project final costs. It was found that large projects with above-average coefficients of variation of bids showed greater deviations from original award amounts than large projects with below-average coefficients of variation. The finding enables project owners to study the projects before execution by recouping valuable insights from the community of bidders on the possibility of greater project cost changes faced by the planned projects without complex and mathematically rigorous models.</p></abstract>

Although unsuccessful bids are usually discarded once the bids are opened under the typical lowest-price sealed-bid project award auction environment, considering efforts and resources expended by responsive and responsible bidders, it is likely that not only a successful bid but also unsuccessful bids can convey important information about the project at hand. This article documents an effort to answer the question that whether projects that receive more dispersed estimates at the bidding stage are more likely to experience greater project cost changes. Taking collective intelligence as the theoretical framework, a total of 210 projects conducted by the Ohio Department of Transportation between 2008 and 2018 were analyzed to investigate the relationship between the coefficients of variation of bids and project final costs. It was found that large projects with above-average coefficients of variation of bids showed greater deviations from original award amounts than large projects with below-average coefficients of variation. The finding enables project owners to study the projects before execution by recouping valuable insights from the community of bidders on the possibility of greater project cost changes faced by the planned projects without complex and mathematically rigorous models.

Unsuccessful bids: Coefficient of variation of bids as indicator of project risk

Department of Civil and Environmental Engineering

Center of Excellence in Project Management, Department of Civil and Environmental Engineering

Organization, Technology and Management in Construction: an International Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Unsuccessful bids: Coefficient of variation of bids as indicator of project risk"}

Although unsuccessful bids are usually discarded once the bids are opened under the typical lowest-price sealed-bid project award auction environment,...

Description	Unit	Value	Remark
Number of case projects	ea.	210
Mean, award bid	U$	20,916,801.39	SD 31,430,304.83
Mean, project final cost	U$	21,324,687.90	SD 31,831,462.92
Mean, number of bidders	ea.	4.28	SD 2.13
Mean, CV of bids	%	7.42	SD 4.76
Mean, project cost change	%	5.36	SD 5.99

Description		2-bid projects	3-bid projects	4-bid projects	5-bid projects	6-bid projects	7-bid projects
50%	Mean, top 50% CV,%	7.61	4.45	6.75	4.24	6.39	7.98
	Mean, bottom 50% CV,%	3.66	6.20	3.91	3.93	3.03	5.03
	p-value	0.01	0.36	0.16	0.87	0.15	0.24

40%	Mean, top 40% CV,%	7.64	4.62	6.52	4.26	6.09	8.94
	Mean, bottom 40% CV,%	3.28	5.47	3.24	4.24	3.13	4.50
	p-value	0.02	0.70	0.12	0.99	0.33	0.14

30%	Mean, top 30% CV,%	6.98	4.15	6.97	4.75	7.27	9.24
	Mean, bottom 30% CV,%	3.36	5.63	3.20	5.27	2.94	2.50
	p-value	0.04	0.60	0.15	0.87	0.27	0.05

20%	Mean, top 20% CV,%	6.11	4.76	8.62	3.18	3.99	6.19
	Mean, bottom 20% CV,%	3.61	5.61	2.97	6.34	4.00	2.19
	p-value	0.31	0.84	0.15	0.35	0.99	0.01

10%	Mean, top 10% CV,%	7.59	8.04	12.67	2.13	2.80	7.30
	Mean, bottom 10% CV,%	4.22	8.54	3.97	8.58	2.95	1.17
	p-value	0.44	0.95	0.26	0.55	0.96	0.02

Description	Unit	Value
Number of case projects	ea.	210
Mean, number of bidders (SD)	ea.	4.28 (2.13)
Mean, CV (SD)	%	7.42 (4.76)
Mean, project cost change (SD)	%	5.36 (5.99)
Mean, project cost change, above-average CV (SD) [A]	%	6.49 (6.73)
Mean, project cost change, below-average CV (SD) [B]	%	4.24 (4.92)
p-value between [A] and [B]	–	0.01

Unsuccessful bids: Coefficient of variation of bids as indicator of project risk

Article Category: Research Paper

Publicado en línea: 22 jun 2020

Páginas: 2193 - 2199

Recibido: 02 sept 2019

Aceptado: 15 abr 2020

DOI: https://doi.org/10.2478/otmcj-2020-0015

Palabras clave
bidding data, collective intelligence, coefficient of variation, Welch's -test, project risk

© 2020 Young Joo Kim et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Characteristics of case projects

Comparison of means of project cost changes against the number of bidders and CV percentile

Summary of analysis results

Unsuccessful bids: Coefficient of variation of bids as indicator of project risk

Article Category: Research Paper

Publicado en línea: 22 jun 2020

Páginas: 2193 - 2199

Recibido: 02 sept 2019

Aceptado: 15 abr 2020

DOI: https://doi.org/10.2478/otmcj-2020-0015

Palabras clavebidding data, collective intelligence, coefficient of variation, Welch's -test, project risk

© 2020 Young Joo Kim et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Characteristics of case projects

Comparison of means of project cost changes against the number of bidders and CV percentile

Summary of analysis results

Palabras clave
bidding data, collective intelligence, coefficient of variation, Welch's -test, project risk