Quality of Artificial Intelligence Driven Procurement Decision Making and Transactional Data Structure
Abstract
Purpose: Current data driven decision making development calls for the quality assurance based on quality data structure. The paper analyses transactional data structure used in public procurement in Slovakia and the effect of data structure enhancement on prediction performance as crucial part of artificial intelligence (AI) quality assurance standard. We examine the significance of data structure enhancement and attributes transformation for prediction modelling.
Methodology/Approach: The research is based on mutli-step model using stacked ensemble machine learning (ML) algorithm and simulating input space of 211 attributes transformed and aggregated according to different perspectives assessed by r2, mean absolute error (MAE) or mean square error (MSE).
Findings: The results show that different performance of variable categories to prediction power. The most significant predictors were in category related to sectoral product classifications and in category related to variables aggregated for supplier, what underline the significance of structured information of all suppliers and negotiation participants in public tenders.
Research Limitation/Implication: Methodology is based on big data with high complexity. Due to limited computing power, no subjects’ IDs were used as inputs. The complexity behind data and processes call for more complex simulations of all variables and their mutual interaction and interdependencies.
Originality/Value of paper: The paper contributes to data science in transactional data domain and assessed the significance of different variables categories with respect to their specific added value to prediction power.
References
Bodendorf, F., Hollweck, B. and Franke, J., 2022. Information asymmetry in business-to-business negotiations: A game theoretical approach to support purchasing decisions with suppliers. Group Decision and Negotiation, [e-journal] 31(4), pp.723-745. DOI: 10.1007/s10726-022-09780-3.
Bosmans, H., Zanca, F. and Gelaude, F., 2021. Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice. Physica Medica, [e-journal] 83, pp.257-263. DOI: 10.1016/j.ejmp.2021.04.006.
Centre for Data Ethics and Innovation (CDEI), 2021. The roadmap to an effective AI assurance ecosystem. [online] GOV.UK. Available at: <https://www.gov.uk/government/publications/the-roadmap-to-an-effective-ai-assurance-ecosystem/the-roadmap-to-an-effective-ai-assurance-ecosystem> [Accessed 03 January 2023].
Consortium of Quality Assurance of AI Systems (QAI), 2020. Guidelines for the Quality Assurance of AI Systems. 2020.02 edition. [pdf] Consortium of Quality Assurance for Artificial-Intelligence-based Products and Services. Available at: <https://www.qa4ai.jp/QA4AI.Guideline.202002.en.pdf> [Accessed 03 January 2023].
Elektronický kontraktačný systém (EKS), 2022. XEKS. [online] Available at: <http://www.eks.sk/> [Accessed 02 January 2023].
European Commission (EC), 2012. Common procurement vocabulary, Internal Market, Industry, Entrepreneurship and SMEs. [online] Berlin: Ramboll. Available at: <https://single-market-economy.ec.europa.eu/single-market/public-procurement/digital-procurement/common-procurement-vocabulary_en> [Accessed 02 January 2023].
European Commission (EC), 2017. Final Report: Review of the Functioning of the CPV Codes/System. [pdf] Bochum: Cosinex GmbH. Available at: <https://ec.europa.eu/docsroom/documents/27821/attachments/1/translations/en/renditions/pdf> [Accessed 01 January 2023].
Felderer, M. and Ramler, R., 2021. Quality assurance for AI-based systems: Overview and challenges (introduction to interactive session). In: D. Winkler, S. Biffl, D. Mendez, M. Wimmer and J. Bergsmann, J., eds. 2021. Software Quality: Future Perspectives on Software Engineering Quality. SWQD 2021. Lecture Notes in Business Information Processing. Cham: Springer. pp.33-42. DOI: 10.1007/978-3-030-65854-0_3.
Felderer, M., Russo, B. and Auer, F., 2019. On testing data-intensive software systems. In: S. Biffl, M. Eckhart, A. Lüder and E. Weippl, eds. 2019. Security and Quality in Cyber-Physical Systems Engineering. Cham: Springer. pp.129-148. DOI: 10.1007/978-3-030-25312-7_6.
Folmer, E., Oude Luttighuis, P. and Van Hillegersberg, J., 2011. Do semantic standards lack quality? A survey among 34 semantic standards. Electronic Markets, [e-journal] 21(2), pp.99-111. DOI: https://doi.org/10.1007/s12525-011-0058-y.
Giunipero, L.C., Bittner, S., Shanks, I. and Cho, M.H., 2019. Analyzing the sourcing literature: Over two decades of research. Journal of Purchasing and Supply Management, [e-journal] 25(5), 100521. DOI: 10.1016/j.pursup.2018.11.001.
Gruenen, J., Bode, C. and Hoehle, H., 2017. Predictive procurement insights: B2B business network contribution to predictive insights in the procurement process following a design science research approach. In: A. Maedche, J. vom Brocke and A. Hevner, eds. 2017. Designing the Digital Transformation. DESRIST 2017. Lecture Notes in Computer Science. Cham: Springer. pp.267-281. DOI: 10.1007/978-3-319-59144-5_16.
ISO/IEC, 2008. ISO/IEC 25012:2008 software engineering – software product quality requirements and evaluation (square) – data quality model. Technical report. Geneva: ISO.
Krcmar, H., 2015. Informationsmanagement. 6th ed. Wiesbaden: Springer.
Maisel, L. and Cokins, G., 2014. Predictive Business Analytics: Forward Looking Capabilities to Improve Business Performance. Hoboken: Wiley.
Ohm, P., 2014. Changing the rules: General principles for data use and analysis. In: J. Lane, V. Stodden, S. Bender and H. Nissenbaum, eds. 2014. Privacy, Big Data, and the Public Good: Frameworks for Engagement. Cambridge: Cambridge University Press. pp.96-111. DOI: 10.1017/cbo9781107590205.006.
Omar, I.A., Jayaraman, R., Debe, M.S., Salah, K., Yaqoob, I. and Omar, M., 2021. Automating Procurement Contracts in the Healthcare Supply Chain Using Blockchain Smart Contracts. IEEE Access, 9, pp.37397-37409. DOI: 10.1109/ACCESS.2021.3062471.
Van Hoek, R., Larsen, J.G. and Lacity, M., 2022. Robotic process automation in Maersk procurement–applicability of action principles and research opportunities. International Journal of Physical Distribution & Logistics Management, [e-journal] 52(3), pp.285-298. DOI: 10.1108/IJPDLM-09-2021-0399.
Viale, L. and Zouari, D., 2020. Impact of digitalization on procurement: the case of robotic process automation. Supply Chain Forum: An International Journal, [e-journal] 21(3), pp.185-195. DOI: 10.1080/16258312.2020.1776089.
Zhang, J.M., Harman, M., Ma, L. and Liu, Y., 2020. Machine learning testing: survey landscapes and horizons. IEEE Transactions on Software Engineering, [e-journal] 48(1), pp.1-36. DOI: 10.1109/TSE.2019.2962027.
Authors
Copyright (c) 2023 Radoslav Delina, Marek Macik
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