Each of the core functionalities or basic strengths of these digital technologies, as well as their rate of development, will determine a higher or lower likelihood of successful implementation in the area of procurement, which is a highly information/data-sensitive area of public policy and administration. Therefore, it seems unavoidable to first look at the need to create an enabling data architecture as a priority (and pre-condition) to the deployment of any digital technologies.
6. An enabling data architecture as a priority
The importance of the availability of good quality data in the context of digital technologies cannot be over-emphasised (see eg OECD, 2019b). This is also clear to the European Commission, as it has also included the need to improve the availability of good quality data as a strategic priority. Indeed, the Commission stressed that “Better and more accessible data on procurement should be made available as it opens a wide range of opportunities to assess better the performance of procurement policies, optimise the interaction between public procurement systems and shape future strategic decisions” (COM(2017) 572 fin at 10-11).
However, despite the launch of a set of initiatives that seek to improve the existing procurement data architecture, there are still significant difficulties in the generation of data [for discussion and further references, see A Sanchez-Graells, “Data-driven procurement governance: two well-known elephant tales” (2019) 24(4) Communications Law 157-170; idem, “Some public procurement challenges in supporting and delivering smart urban mobility: procurement data, discretion and expertise”, in M Finck, M Lamping, V Moscon & H Richter (eds), Smart Urban Mobility – Law, Regulation, and Policy, MPI Studies on Intellectual Property and Competition Law (Springer 2020) forthcoming; and idem, “EU Public Procurement Policy and the Fourth Industrial Revolution: Pushing and Pulling as One?”, Working Paper for the YEL Annual Conference 2019 ‘EU Law in the era of the Fourth Industrial Revolution’].
To be sure, there are impending advances in the availability of quality procurement data as a result of the increased uptake of the Open Contracting Data Standards (OCDS) developed by the Open Contracting Partnership (OCP); the new rules on eForms; the development of eGovernment Application Programming Interfaces (APIs); the 2019 Open Data Directive; the principles of business to government data sharing (B2G data sharing); etc. However, it seems to me that the European Commission needs to exercise clearer leadership in the development of an EU-wide procurement data architecture. There is, in particular, one measure that could be easily adopted and would make a big difference.
The 2019 Open Data Directive (Directive 2019/1024/EU, ODD) establishes a special regime for high-value datasets, which need to be available free of charge (subject to some exceptions); machine readable; provided via APIs; and provided as a bulk download, where relevant (Art 14(1) ODD). Those high-value datasets are yet to be identified by the European Commission through implementing acts aimed at specifying datasets within a list of thematic categories included in Annex I, which includes the following datasets: geospatial; Earth observation and environment; meteorological; statistics; companies and company ownership; and mobility. In my view, most relevant procurement data can clearly fit within the category of statistical information.
More importantly, the directive specifies that the ‘identification of specific high-value datasets … shall be based on the assessment of their potential to: (a) generate significant socioeconomic or environmental benefits and innovative services; (b) benefit a high number of users, in particular SMEs; (c) assist in generating revenues; and (d) be combined with other datasets’ (Art 14(2) ODD). Given the high-potential of procurement data to unlock (a), (b) and (d), as well as, potentially, generate savings analogous to (c), the inclusion of datasets of procurement information in the future list of high-value datasets for the purposes of the Open Data Directive seems like an obvious choice.
Of course, there will be issues to iron out, as not all procurement information is equally susceptible of generating those advantages and there is the unavoidable need to ensure an appropriate balance between the publication of the data and the protection of legitimate (commercial) interests, as recognised by the Directive itself (Art 2(d)(iii) ODD) [for extended discussion, see here]. However, this would be a good step in the direction of ensuring the creation of a forward-looking data architecture.
At any rate, this is not really a radical idea. At least half of the EU is already publishing some public procurement open data, and many Eastern Partnership countries publish procurement data in OCDS (eg Moldova, Ukraine, Georgia). The suggestion here would bring more order into this bottom-up development and would help Member States understand what is expected, where to get help from, etc, as well as ensure the desirable level of uniformity, interoperability and coordination in the publication of the relevant procurement data.
Beyond that, in my view, more needs to be done to also generate backward-looking databases that enable the public sector to design and implement adequate sustainability policies, eg in relation to the repair and re-use of existing assets.
Only when the adequate data architecture is in place, will it be possible to deploy advanced digital technologies. Therefore, this should be given the highest priority by policy-makers.
7. Potential AI uses for sustainable public procurement
If/when sufficient data is available, there will be scope for the deployment of several specific implementations of artificial intelligence. It is possible to imagine the following potential uses:
Sustainability-oriented (big) data analytics: this should be relatively easy to achieve and it would simply be the deployment of big data analytics to monitor the extent to which procurement expenditure is pursuing or achieving specified sustainability goals. This could support the design and implementation of sustainability-oriented procurement policies and, where appropriate, it could generate public disclosure of that information in order to foster civic engagement and to feedback into political processes.
Development of sustainability screens/indexes: this would be a slight variation of the former and could facilitate the generation of synthetic data visualisations that reduced the burden of understanding the data analytics.
Machine Learning-supported data analysis with sustainability goals: this could aim to train algorithms to establish eg the effectiveness of sustainability-oriented procurement policies and interventions, with the aim of streamlining existing policies and to update them at a pace and level of precision that would be difficult to achieve by other means.
Sustainability-oriented procurement planning: this would entail the deployment of algorithms aimed at predictive analytics that could improve procurement planning, in particular to maximise the sustainability impact of future procurements.
Moreover, where clear rules/policies are specified, there will be scope for:
Compliance automation: it is possible to structure procurement processes and authorisations in such a way that compliance with pre-specified requirements is ensured (within the eProcurement system). This facilitates ex ante interventions that could minimise the risk of and the need for ex post contractual modifications or tender cancellations.
Recommender/expert systems: it would be possible to use machine learning to assist in the design and implementation of procurement processes in a way that supported the public buyer, in an instance of cognitive computing that could accelerate the gains that would otherwise require more significant investments in professionalisation and specialisation of the workforce.
Chatbot-enabled guidance: similarly to the two applications above, the use of procurement intelligence could underpin chatbot-enabled systems that supported the public buyers.
A further open question is whether AI could ever autonomously generate new sustainability policies. I dare not engage in such exercise in futurology…
8. Limited use of blockchain/DLTs for sustainable public procurement