Tender evaluation through efficiency analysis for public construction contracts

Abstract

Given the European Public Procurement Directive 2014/24/EU, policymakers have ordered the inclusion of various criteria, such as the price, life-cycle costs, environmental, and social aspects, in the evaluation of tenders for public construction projects. Consequently, the relevance of non-monetary award criteria has gained significant value. However, the established evaluation formulas, which are used to obtain the best value for money procurement, have resulted in legal disputes. The existing evaluation formulas exhibit mathematical weaknesses, wherein scoring indices do not express economic efficiency adequately. Thus, a conflict is observed between the political requirement of non-monetary award criteria and their evaluation by contracting authorities. To overcome such dilemma, an extensive literature review is conducted. Specifically, this study explores the essential problems of existing evaluation formulas and develops a more reliable method. The technique from the field of efficiency analysis, i.e., Data Envelopment Analysis (DEA), is adopted in this study. For contract awarding, the DEA is extended by introducing a decision theoretical framework. For public procurement, the proposed method combines two advantages. First, the proposed method ensures the derivation of a robust tender ranking given that with respect to clients’ preferences, irrelevant and insufficiently tailored tenders do not influence the scoring. Second, the proposed method supports the intention of policy makers to promote public goals, such as sustainable aspects. By disclosing the strengths and weaknesses of bidders with respect to their competitors, all bidders can obtain a precise overview of their performance regarding the award criteria. In sum, the proposed method allows a targeted improvement of certain criteria values in future tenders and consequently leads to an enhancement of public goals.

Graphical abstract

Keywords

bid evaluation / public procurement / best value for money / data envelopment analysis / contractor selection

References

[1] Alhola K (2012). Environmental criteria in public procurement: Focus on tender documents. Monographs of the Boreal Environmental Research, 40. Aalto: Department of Real Estate, Planning and Geoinformatics, Aalto University

[2] Ballesteros-Pérez P, Skitmore M, Pellicer E, Zhang X (2016). Scoring rules and competitive behaviour in best-value construction auctions. Journal of Construction Engineering and Management, 142(9): 04016035

[3] Banker R D, Charnes A, Cooper W W (1984). Some models for estimating technical and scale inefficiencies in Data Envelopment Analysis. Management Science, 30(9): 1078–1092

[4] Bratt C, Hallstedt S, Robert K H, Broman G, Oldmark J (2013). Assessment of criteria development for public procurement from a strategic sustainability perspective. Journal of Cleaner Production, 52: 309–316

[5] Chambers R G, Chung Y, Färe R (1998). Profit, directional distance functions, and nerlovian efficiency. Journal of Optimization Theory and Applications, 98(2): 351–364

[6] Charnes A, Cooper W W, Rhodes E (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6): 429–444

[7] Cheaitou A, Larbi R, Al Housani B (2019). Decision making framework for tender evaluation and contractor selection in public organizations with risk considerations. Socio-Economic Planning Sciences, 68: 100620

[8] Cheung S O, Lam T I, Leung M Y, Wan W W (2010). An analytical hierarchy process based procurement selection method. Construction Management and Economics, 19(4): 427–437

[9] Costantino N, Dotoli M, Falagario M, Fanti M P, Mangini A M, Sciancalepore F (2011). Supplier selection in the public procurement sector via a data envelopment analysis approach. In: Proceedings of the 19th Mediterranean Conference on Control and Automation (MED). Corfu: IEEE, 236–241

[10] Darko A, Chan A P C, Ameyaw E E, Owusu E K, Pärn E, Edwards D J (2019). Review of application of analytic hierarchy process (AHP) in construction. International Journal of Construction Management, 19(5): 436–452

[11] Dini F, Pacini R, Valletti T (2006). Scoring rules. In: Dimitri N, Piga G, Spagnolo G, eds. Handbook of Procurement. Cambridge: Cambridge University Press, 293–321

[12] Dreschler M (2008). Analysis of price correction award mechanisms applied in the Dutch construction industry. In: Proceedings of the 3rd International Public Procurement Conference. Amsterdam, 423–440

[13] Eder M (2012). VIP—Tender model for infrastructure projects. Geomechanics and Tunnelling, 5(6): 708–717

[14] Eke G, Wedawatta G, Elgy J (2017). A quantifiable method of assessing the risk of selecting the lowest bidder in construction projects: A literature review. In: Proceedings of the 13th International Postgraduate Research Conference. Salford, 637–646

[15] Erdogan S A, Saparauskas J, Turskis Z (2019). A multi-criteria decision-making model to choose the best option for sustainable construction management. Sustainability, 11(8): 2239

[16] European Commission (2012). Guide to Research and Innovation Strategies for Smart Specialisations (RIS3). Luxembourg: Publications Office of the European Union

[17] European Court of Justice (2013). Judgment of the Court (Second Chamber) from 2013–01–29 in Case T-532/10. ECLI:EU:T:2013:38

[18] European Court of Justice (2016). Judgment of the Court (Fourth Chamber) from 2016–07–14 in Case C-6/15. ECLI:EU:C:2016:555

[19] European Court of Justice (2018). Judgment of the Court (Fourth Chamber) from 2018–05–03 in Case C-376/16. ECLI:EU:C:2018:299

[20] European Parliament, Council of the EU (2014). Directive 2014/24/EU of the European Parliament and of the Council of 26 February 2014 on public procurement and repealing Directive 2004/18/EC. Official Journal of the European Union, 94(57): 65–242

[21] Faire Vergaben (2016). Criteria for Value for Money Procurement. Vienna: GBH-Presse (in German)

[22] Falagario M, Sciancalepore F, Costantino N, Pietroforte R (2012). Using a DEA-cross efficiency approach in public procurement tenders. European Journal of Operational Research, 218(2): 523–529

[23] Farrell M J (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3): 253–281

[24] Federal Court of Germany (2017). Resolution of 2017–04–04. Reference X ZB 3/17

[25] Hanák T, Serrat C (2018). Analysis of construction auctions data in Slovak public procurement. Advances in Civil Engineering, 3036340

[26] Hasnain M, Thaheem M J, Ullah F (2018). Best value contractor selection in road construction projects: ANP-based decision support system. International Journal of Civil Engineering, 16(6): 695–714

[27] Higher Regional Court of Düsseldorf (2010). Resolution of 2010–03–03. Reference VII–Verg 48/09

[28] Higher Regional Court of Munich (2017). Resolution of 2017–03–30. Reference Verg 10/16

[29] International Tunneling Association (2014). Recommendation of a Tender Model for Infrastructure Projects. Vienna: Austrian National Committee of ITA

[30] Liu B, Huo T, Liao P C, Yuan J, Sun J, Hu X (2017). A special Partial Least Squares (PLS) path decision modelling for bid evaluation of large construction projects. KSCE Journal of Civil Engineering, 21(3): 579–592

[31] Lorentziadis P L (2010). Post-objective determination of weights of the evaluation factors in public procurement tenders. European Journal of Operational Research, 200(1): 261–267

[32] Mateus R, Ferreira J A, Carreira J (2010). Full disclosure of tender evaluation models: Background and application in Portuguese public procurement. Journal of Purchasing and Supply Management, 16(3): 206–215

[33] McCabe B, Tran V, Ramani J (2005). Construction prequalification using data envelopment analysis. Canadian Journal of Civil Engineering, 32(1): 183–193

[34] National Agency for Public Procurement (2019). Sustainability criteria. Available at: upphandlingsmyndigheten.se/en/sustainable-public-procurement/sustainable-procurement-criteria

[35] Niewerth S (2019). A Decision-Theoretic Extension of the Data Envelopment Analysis for Tender Evaluation in Public Construction Contracts. Dissertation for the Doctoral Degree. Bochum: Ruhr-University (in German)

[36] Niewerth S, Vogt P, Thewes M (2017). Evaluation of non-monetary award criteria and life-cycle cost for procurement of construction contracts. Bauingenieur, 92(9): 398–405 (in German)

[37] Polat G (2016). Subcontractor selection using the integration of the AHP and PROMETHEE methods. Journal of Civil Engineering and Management, 22(8): 1042–1054

[38] Shyur H J, Shih H S (2006). A hybrid MCDM model for strategic vendor selection. Mathematical and Computer Modelling, 44(7–8): 749–761

[39] Thewes M, Kamarianakis S (2012). Multi-criteria decision making of construction methods using the analytical hierarchy process based on fuzzy scales. In: Proceedings of the 13th World Conference of the Associated Research Centers for the Urban Underground Space (ACUUS)—Advances in Underground Space Development. Singapore

[40] Tran D, Molenaar K R, Gransberg D D (2016). Implementing best-value procurement for design–bid–build highway projects. Transportation Research Record: Journal of the Transportation Research Board, 2573(1): 26–33

[41] UK Cabinet Office (2012). Government Construction—Common Minimum Standards for procurement of the built environments in the public sector. London

[42] Vogt P (2013). Model for Life Cycle Cost Analysis of Road Tunnels Considering Technical and Financial Uncertainties. Dissertation for the Doctoral Degree. Bochum: Ruhr-University (in German)

[43] Vogt P, Thewes M (2012). The calculation of life-cycle costs for road tunnels under the influence of uncertainties. In: Life-Cycle and Sustainability of Civil Infrastructure Systems: Proceedings of the 3rd International Symposium on Life-Cycle Civil Engineering. Vienna: CRC Press, 1036–1043

[44] World Economic Forum (2017). Sustainable Development Impact Summit 2017 Report – Global Agenda. New York

[45] Yang J B, Wang H H, Wang W C, Ma S M (2016). Using data envelopment analysis to support best-value contractor selection. Journal of Civil Engineering and Management, 22(2): 199–209

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

RIGHTS & PERMISSIONS

2020 The Author(s) 2020. This article is published with open access at link.springer.com and journal.hep.com.cn