Skip to main content
SpringerLink
Log in

The 3Es framework: a project framework for simulating costs for engineering efforts under uncertainties in the healthcare solution industry

  • Production Management
  • Published:
Production Engineering Aims and scope Submit manuscript

Abstract

Estimating costs associated with engineering projects accurately is vital for engineering companies to stay competitive and price their projects correctly. This is especially important in the healthcare industry, which is highly regulated and competitive and has high demands for quality and price competitiveness. This is because many countries have a healthcare sector that is partly or fully funded by the government. However, the significance of engineering costs is often underestimated, leading to cost overruns and resource mismatches. Therefore, this paper contributes to theory and practice by developing a novel framework to estimate the costs of engineering projects under uncertainties, which we name the Engineering-Effort-Estimation framework (3Es-framework). This framework has been tested in a case study in which the COSYSMO-Xpert tool was developed to simulate uncertainties of engineering effort estimates. This tool is an extension of the established Constructive Systems Engineering Cost Model (COSYSMO), which is widely used by practitioners in this field. The COSYSMO-Xpert tool is an extension of COSYSMO, adding expert insights to that model. After having used the 3Es-framework and the COSYSMO-Xpert tool for one year, the case company increased their average accuracy to a ± 5% ratio from a previous average underestimation in the range of 15–20%.

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

Similar content being viewed by others

References

  1. Shi L, Newnes L, Culley S, Gopsill J, Sinder C (2015) Data-driven modelling: Towards interpreting and understanding process evolution of in-service engineering projects. In: IFIP International Conference on Product Lifecycle Management, pp 291–300, Springer

  2. Dan L, Zhi-guo D (2012) The fuzzy evaluation model for opportunity choosing in engineering project bidding. In: Management Science and Engineering (ICMSE), 2012 International Conference, pp 353–358

  3. Yan W, Chen CH, Khoo LP, Pritchard MC, Passey SJ (2004) Customer-oriented product conceptualization in a collaborative bidding process. In Proceedings of the 11th ISPE International Conference on Concurrent Engineering (ISPE-2004), Beijing, China, 26–30 July 2004, pp. 1141–1147

  4. Muench J (2005) Improving cost estimation capabilities in system organizations-transparent cost estimation modeling with CoBRA. In: Conference proceedings of the 4th Workshop of Critical Software (WOCS),Tokyo, Japan

  5. Caputo AC, Pelagagge PM (2008) Parametric and neural methods for cost estimation of process vessels. Int J Prod Econ 112(2):934–954

    Article  Google Scholar 

  6. Tu YL, Xie SQ, Fung RYK (2010) Product development cost estimation in mass customization – study method in international business research. Qual Rep 15(2):370

    Google Scholar 

  7. Wang HL, Hou L, Liu CR, Wang QL (2010) Cost estimation model of modular product family. Appl Mech Mater 37:276–279 Trans Tech Publ

    Google Scholar 

  8. Wei X, Wang W (2009) The cost model and estimation methods of product configuration case in MC. In: Management of e-Commerce and e-Government, 2009. ICMECG’09. International Conference on IEEE, pp 241–245

  9. Dillibabu R, Krishnaiah K (2005) Cost estimation of a software product using COCOMO II. 2000 model–a case study. Int J Proj Manag 23(4):297–307

    Article  Google Scholar 

  10. Valerdi R (2005) The constructive systems engineering cost model (COSYSMO). PhD Thesis, University of Southern California

  11. Kwok ECS, Anderson PM, Ng SHS (2010) Value engineering for railway construction projects: cost driver analysis. Proc Inst Mech Eng F J Rail Rapid Transit 224(1):45–52

    Article  Google Scholar 

  12. Marzouk M, Ahmed RM (2011) A case-based reasoning approach for estimating the costs of pump station projects. J Adv Res 2(4):289–295

    Article  Google Scholar 

  13. Membah J, Asa E (2015) Estimating cost for transportation tunnel projects: a systematic literature review. Int J Const Manag 15(3):196–218

    Google Scholar 

  14. Niazi A, Dai JS, Balabani S, Seneviratne L (2006) Product cost estimation: technique classification and methodology review. J Manuf Sci Eng 128(2):563–575

    Article  Google Scholar 

  15. Dahlborg E, Tengelin E, Aasen E, Strunck J, Boman Ã, Ottesen AM, Dahl BM, Helberget LK, Lassen I (2021) The struggle between welfare state models and prevailing healthcare policy in scandinavian healthcare legislative documents. Int J Health Gov 26(1):51–64

    Google Scholar 

  16. Hooshmand Y, Koehler P, Korff-Krumm A (2016) Cost estimation in engineer-to order manufacturing. Open Eng 6(1):13

    Article  Google Scholar 

  17. Hsiao F-Y, Wang S-H, Wang W-C, Wen C-P, Yu W-D (2012) Neuro-fuzzy cost estimation model enhanced by fast messy genetic algorithms for semiconductor hookup construction. Comput Aided Civ Inf Eng 27(10):764–781

    Article  Google Scholar 

  18. Ivanenko Y, Pasichnichenko I (2014) On one definition of uncertainty. Risk Decis Anal 5(2–3):139–148

    Article  MATH  Google Scholar 

  19. Kadir AZA, Yusof Y, Wahab MS (2020) Additive manufacturing cost estimation models: a classification review. Int J Adv Manuf Technol 107:4033–4053

    Article  Google Scholar 

  20. Trendowicz A, Jeffery R (2014) Constructive cost model COCOMO. In: Software project effort estimation, Springer, pp 277–293

  21. Alstad JP (2022) COSYSMO 3.0’s improvements in estimating and methodology. In: Madni AM, Boehm B, Erwin D, Moghaddam M, Sievers M, Wheaton M (eds) Recent Trends and advances in Model Based Systems Engineering. Springer, Cham

    Google Scholar 

  22. Xu Y, Sanchez JF, Njuguna J (2014) Cost modelling to support optimised selection of the end of life options for automotive components. Springer Science and Business Media LLC. https://core.ac.uk/outputs/222839950

  23. Liu L, Zhu K (2007) Improving cost estimates of construction projects using phased cost factors. J Constr Eng Manag 133(1):91–95

    Article  Google Scholar 

  24. de Andres J, Fernandez-Lanvin D, Lorca P (2015) Cost estimation in software engineering projects with web components development. Dyna 82(192):266–275

    Article  Google Scholar 

  25. Lu Y-F, Yin Y-F (2013) A new constructive cost model for software testing project management, p 545–556. In: The 19th International Conference on Industrial Engineering and Engineering Management, Springer

  26. Tanaka K, Matsumoto C, Tsuda K (2011) Conformity evaluation system based on member capability information in the software projects. In: Knowledge-based and intelligent information and engineering systems, pp 328–335

  27. Yongchang R, Xing T, Chen X, Chai X (2011) Research on software maintenance cost of influence factor analysis and estimation method, pp 1–4. In: Intelligent Systems and Applications (ISA), 2011 3rd International Workshop on. IEEE

  28. Mendes E (2011) Cost estimation of web applications through knowledge elicitation. In: International Conference on Enterprise Information Systems, pp 315–329. Springer

  29. Chang NL, Ho-Baillie Y, Wing A, Basore PA, Young TL, Evans R, Egan RJ (2017) A manufacturing cost estimation method with uncertainty analysis and its application to perovskite on glass photovoltaic modules. Prog Photovolt: Res Appl 25(5):390–405

    Article  Google Scholar 

  30. Mutschler B, Reichert M, Rinderle S (2007) Analyzing the dynamic cost factors of process-aware information systems: A model-based approach. In: International Conference on Advanced Information Systems Engineering, pp 589–603, Springer

  31. Oyewobi LO, Ibrahim AD, Ganiyu BO (2012) Evaluating the impact of risk on contractor’s tender figure in public buildings projects in Northern Nigeria. J Eng Proj Prod Manag 2(1):2

    Google Scholar 

  32. Buertey JIT, Inga E, Kumi TA (2013) The financial impact of risk factors affecting project cost contingency: evidential reasoning method. J Eng Proj Prod Manag 3(2):65

    Google Scholar 

  33. Arditi D, Akan GT, Gurdamar S (1985) Cost overruns in public projects. Int J Proj Manag 3(4):218–224

    Article  Google Scholar 

  34. Jaiswal A, Sharma M (2013) Expert webest tool: a web based application, estimate the cost and risk of software project using function points.Adv Comput Inf Technol77–86

  35. Kansala K (1997) Integrating risk assessment with cost estimation. IEEE Softw 14(3):61–67

    Article  Google Scholar 

  36. Kaliba C, Muya M, Mumba K (2009) Cost escalation and schedule delays in road construction projects in Zambia. Int J Proj Manag 27(5):522–531

    Article  Google Scholar 

  37. Danish Standard Organization (2012) Guidance on project management. Standard, Charlottenlund, DK

    Google Scholar 

  38. Asiedu Y, Besant RW (2000) Simulation-based cost estimation under economic uncertainty using kernel estimators. Int J Prod Res 38(9):2023–2035

    Article  MATH  Google Scholar 

  39. Erkoyuncu JA, Durugbo C, Shehab E, Roy R, Parker R, Gath A, Howell D (2013) Uncertainty driven service cost estimation for decision support at the bidding stage. Int J Prod Res 51(19):5771–5788

    Article  Google Scholar 

  40. Touran A (2006) CMAA Owners risk reduction techniques using a CM, checklist for capital construction projects

  41. Francesco DD, Chryssanthopoulos M, Faber MH, Bharadwaj U (2020) Consistent and coherent treatment of uncertainties and dependencies in fatigue crack growth calculations using multi-level bayesian models. Rel Eng & Sys Saf 204 Online ahead of print. https://doi.org/10.1016/j.ress.2020.107117

  42. Bakhshi P, Touran A (2014) An overview of budget contingency calculation methods in construction industry. Procedia Eng 85:52–60

    Article  Google Scholar 

  43. Kwak YH, Ingall L (2007) Exploring Monte Carlo simulation applications for project management. Risk Manag 9(1):44–57

    Article  Google Scholar 

  44. Bertoni A, Bertoni M (2020) PSS cost engineering: a model-based approach for concept design. CIRP J Man Sci Tech 29(B):176–190

    Article  Google Scholar 

  45. Chou J-S (2011) Cost simulation in an itembased project involving construction engineering and management. Int J Proj Manag 29(6):706–717

    Article  Google Scholar 

  46. Sokolowski JA (2010) Monte Carlo simulation. Modeling and simulation fundamentals. Wiley, Hoboken, NJ, p 131

    Chapter  Google Scholar 

  47. Abdel-Khalik HS, Bang Y, Wang C (2013) Overview of hybrid subspace methods for uncertainty quantification, sensitivity analysis. Ann Nucl Energy 52:28–46

    Article  Google Scholar 

  48. Lambropoulos S, Manolopoulos N, Pantouvakis J-P (1996) SEMANTIC: Smart earthmoving analysis and estimation of cost. Constr Manag Econ 14(2):79–92

    Article  Google Scholar 

  49. Zhang L, Huang Y, Wu X, Skibniewski MJ (2017) Risk-based estimate for operational safety in complex projects under uncertainty. Appl Soft Comput 54:108–120

    Article  Google Scholar 

  50. Morio J (2011) Global and local sensitivity analysis methods for a physical system. Eur J Phys 32(6):1577

    Article  Google Scholar 

  51. Griensven AV, Meixner T, Grunwald S, Bishop T, Diluzio M, Srinivasan R (2006) A global sensitivity analysis tool for the parameters of multivariable catchment models. J Hydrol 324(1):10–23

    Article  Google Scholar 

  52. Wainwright HM, Finsterle S, Jung Y, Zhou Q, Birkholzer JT (2014) Making sense of global sensitivity analyses. Comput Geosci 65:84–94

    Article  Google Scholar 

  53. Yeo KT (1991) Project cost sensitivity and variability analysis. Int J Proj Manag 9(2):111–116

    Article  Google Scholar 

  54. Rodríguez-Pajarón P, Hernández A, Milanović JV (2021) Probabilistic assessment of the impact of electric vehicles and nonlinear loads on power quality in residential networks. Int J Elec Power Energy Syst 129 Online ahead of print. https://doi.org/10.1016/j.ijepes.2021.106807

  55. Vissak T (2010) Recommendations for using the case study method in international business research. Qual Rep 15(2):370

    Google Scholar 

  56. Zainal Z (2007) Case study as a research method. J Kemanusiaan 9:1–6

    Google Scholar 

  57. Creswell JW (2013) Research design: qualitative, quantitative, and mixed methods approaches. Sage Publications, Los Angeles, CA

    Google Scholar 

  58. Krathwohl DR (1993) Methods of educational and social science research: an integrated approach. Longman/Addison Wesley Longman, New York, NY

    Google Scholar 

  59. Mackenzie N, Knipe S (2006) Research dilemmas: paradigms, methods and methodology. Issues Educ Res 16(2):193–205

    Google Scholar 

  60. Thomas RM (2003) Blending qualitative and quantitative research methods in theses and dissertations. Corwin Press, Thousand Oaks, CA

  61. Sofaer S (2002) Qualitative research methods. Int J Qual Health Care 14(4):329–336

    Article  Google Scholar 

  62. Hart S (1987) The use of the survey in industrial market research. J Mark Manag 3(1):25–38

    Article  Google Scholar 

  63. Yin RK (2013) Case study research: design and methods. Sage Publications, Thousand Oaks, CA

    Google Scholar 

  64. Cole R (2014) System cost modeling using proxy estimation and COSYSMO. Lockheed Martin Corporation, Litteton, CO

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lyngby, Denmark

    Zaza Nadja Lee Hansen, Niels Erik Hansen, Michael Bayer, Anders Haug & Lars Hvam

Authors
  1. Zaza Nadja Lee Hansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Niels Erik Hansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Bayer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anders Haug
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lars Hvam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zaza Nadja Lee Hansen.

Additional information

Publisher’s Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hansen, Z.N., Hansen, N.E., Bayer, M. et al. The 3Es framework: a project framework for simulating costs for engineering efforts under uncertainties in the healthcare solution industry. Prod. Eng. Res. Devel. 17, 591–600 (2023). https://doi.org/10.1007/s11740-022-01172-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11740-022-01172-5

Search

Navigation