Skip to main content
SpringerLink
Log in

Project risk management from the bottom-up: Activity Risk Index

  • Published:
Central European Journal of Operations Research Aims and scope Submit manuscript

Abstract

Project managers need to manage risks throughout the project lifecycle and, thus, need to know how changes in activity durations influence project duration and risk. We propose a new indicator (the Activity Risk Index, ARI) that measures the contribution of each activity to the total project risk while it is underway. In particular, the indicator informs us about what activities contribute the most to the project’s uncertainty so that project managers can pay closer attention to the performance of these activities. The main difference between our indicator and other activity sensitivity metrics in the literature (e.g. Cruciality, Criticality, Significance, or Schedule Sensitivity Indices) is that our indicator is based on the Schedule Risk Baseline concept instead of on cost or schedule baselines. The new metric not only provides information at the beginning of the project, but also while it is underway. Furthermore, the ARI is the only one to offer a normalized result: if we add its value for each activity, the total sum is 100%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Acebes F, Pajares J, Galán JM, López-Paredes A (2013) Beyond earned value management: a graphical framework for integrated cost, schedule and risk monitoring. Proc Soc Behav Sci 74:231–239

    Article  Google Scholar 

  • Acebes F, Pajares J, Galán JM, López-Paredes A (2014a) Exploring the influence of seasonal uncertainty in project risk management. Proc Soc Behav Sci 119:329–338

    Article  Google Scholar 

  • Acebes F, Pajares J, Galán JM, López-Paredes A (2014b) A new approach for project control under uncertainty. Going back to the basics. Int J Proj Manag 32:423–434

    Article  Google Scholar 

  • Anbari FT (2003) Earned value project management method and extensions. Proj Manag J 34:12–23

    Article  Google Scholar 

  • Bowman ARA (1995) Efficient estimation of arc criticalities in stochastic activity networks. Manag Sci 41:58–67

    Article  Google Scholar 

  • Bowman RA, Muckstadt J (1993) Stochastic analysis of cyclic schedules. Oper Res 41:947–958

    Article  Google Scholar 

  • Cagno E, Caron F, Mancini M (2008) Dynamic analysis of project risk. Int J Risk Assess Manag 10:70–87

    Article  Google Scholar 

  • Chanas S, Zieliński P (2002) The computational complexity of the criticality problems in a network with interval activity times. Eur J Oper Res 136:541–550

    Article  Google Scholar 

  • Chen SP (2007) Analysis of critical paths in a project network with fuzzy activity times. Eur J Oper Res 183:442–459

    Article  Google Scholar 

  • Chen CT, Huang SF (2007) Applying fuzzy method for measuring criticality in project network. Inf Sci (NY) 177:2448–2458

    Article  Google Scholar 

  • Cho JG, Yum BJ (1997) An uncertainty importance measure of activities in PERT networks. Int J Prod Res 35:2737–2770

    Article  Google Scholar 

  • Chou J-S (2011) Cost simulation in an item-based project involving construction engineering and management. Int J Proj Manag 29:706–717

    Article  Google Scholar 

  • Cui W, Qin J, Yue C (2007) Criticality measurement in PERT networks. In: Conference Proceedings of the IEEE international conference systems man cybernetics, vol 1, pp 703–706

  • Dodin BM, Elmaghraby SE (1985) Approximating the criticality indices of the activities in PERT networks. Manag Sci 31:207–224

    Article  Google Scholar 

  • Elmaghraby SE (2000) On criticality and sensitivity in activity networks. Eur J Oper Res 127:220–238

    Article  Google Scholar 

  • Elmaghraby SE, Fathi Y, Taner MR (1999) On the sensitivity of project variability to activity mean duration. Int J Prod Econ 62:219–232

    Article  Google Scholar 

  • Fleming QW, Koppelman JM (1998) Earned value project management. CROSSTALK J Def Softw Eng 11:19–23

    Google Scholar 

  • Ghomi SMTF, Teimouri E (2002) Path critical index and activity critical index in PERT networks. Eur J Oper Res 141:147–152

    Article  Google Scholar 

  • Gutierrez G, Paul A (2000) Analysis of the effects of uncertainty, risk-pooling, and subcontracting mechanisms on project performance. Oper Res 48:927–938

    Article  Google Scholar 

  • Hulett DT (1996) Schedule risk analysis simplified. PM Netw 1996:22–30

    Google Scholar 

  • Jassbi J, Jafari H, Khanmohammadi S (2008) Fuzzy expert system for determining the criticality of activities in mega projects. In: Proceedings of the 8th international conference hybrid intelligent systems, HIS 2008, pp 246–251

  • Klingel AR (1966) Bias in pert project completion time calculations for a real network. Manag Sci 13:B194–B201

    Article  Google Scholar 

  • Kuchta D (2001) Use of fuzzy numbers in project risk (criticality) assessment. Int J Proj Manag 19:305–310

    Article  Google Scholar 

  • Lin FT, Yao JS (2003) Fuzzy critical path method based on signed-distance ranking and statistical confidence-interval estimates. J Supercomput 24:305–325

    Article  Google Scholar 

  • Liu Y, Wang ZF (2013) Analysis of project schedule risk indexes in PERT network using Monte Carlo simulation. Adv Mater Res 760–762:2205–2211

    Article  Google Scholar 

  • Madadi M, Iranmanesh H (2012) A management oriented approach to reduce a project duration and its risk (variability). Eur J Oper Res 219:751–761

    Article  Google Scholar 

  • Martin JJ (1965) Distribution of the time through a directed, acyclic network. Oper Res 13:4–66

    Article  Google Scholar 

  • Pajares J, López-Paredes A (2011) An extension of the EVM analysis for project monitoring: the cost control index and the schedule control index. Int J Proj Manag 29:615–621

    Article  Google Scholar 

  • Project Management Institute (2004) A guide to the project management body of knowledge: PMBoK guide, 3rd edn

  • Project Management Institute (2009) Practice standard for project risk management. Project Management Institute Inc., Newtown Square

  • Project Management Institute (2017) A guide to the project management body of knowledge: PMBoK(R) guide, 6th edn. Project Management Institute Inc., Newtown Square

  • Schonberger RJ (1981) Why projects are “Always” late: a rationale based on manual simulation of a PERT/CPM network. Interfaces (Providence) 11:66–70

    Article  Google Scholar 

  • Tavares LV, Ferreira JA, Coelho JS (2002) A comparative morphologic analysis of benchmark sets of project networks. Int J Proj Manag 20:475–485

    Article  Google Scholar 

  • Tavares LV, Ferreira JA, Coelho JS (2004) A surrogate indicator of criticality for stochastic project networks. Int Trans Oper Res 11:193–202

    Article  Google Scholar 

  • Van Slyke RM (1963) Monte Carlo methods and the PERT problem. Oper Res 11:839–860

    Article  Google Scholar 

  • Vanhoucke M (2010) Using activity sensitivity and network topology information to monitor project time performance. Omega 38:359–370

    Article  Google Scholar 

  • Vanhoucke M (2011) On the dynamic use of project performance and schedule risk information during project tracking. Omega 39:416–426

    Article  Google Scholar 

  • Vanhoucke M (2012a) Measuring the efficiency of project control using fictitious and empirical project data. Int J Proj Manag 30:252–263

    Article  Google Scholar 

  • Vanhoucke M (2012b) Project management with dynamic scheduling: baseline scheduling, risk analysis and project control. Springer, Berlin

    Book  Google Scholar 

  • Vanhoucke M (2015) On the use of schedule risk analysis for project management. J Mod Proj Manag 2:108–117

    Google Scholar 

  • Vanhoucke M (2016) Integrated project management sourcebook: a technical guide to project scheduling, risk and control. Springer, Berlin

    Book  Google Scholar 

  • Williams TM (1992) Criticality in stochastic networks. J Oper Res Soc 43:353–357

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. GIR INSISOC, Dpto. de Organización de Empresas y CIM, Escuela de Ingenierías Industriales, Universidad de Valladolid, Paseo del Cauce 59, 47011, Valladolid, Spain

    Fernando Acebes, Javier Pajares, José M. González-Varona & Adolfo López-Paredes

Authors
  1. Fernando Acebes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Javier Pajares
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José M. González-Varona
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adolfo López-Paredes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fernando Acebes.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Acebes, F., Pajares, J., González-Varona, J.M. et al. Project risk management from the bottom-up: Activity Risk Index. Cent Eur J Oper Res 29, 1375–1396 (2021). https://doi.org/10.1007/s10100-020-00703-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10100-020-00703-8

Keywords

Search

Navigation