Is the ESPD the enemy of procurement automation in the EU (quick thoughts)

Source: https://americanhistory.si.edu/collections/search/object/nmah_856663.

I have started to watch the three-session series on Intelligent Automation in US Federal Procurement hosted by the GW Law Government Procurement Law Program over the last few weeks (worth watching!), as part of my research for a paper on AI and corruption in procurement. The first session in the series focuses in large part on the intelligent automation of information gathering for the purposes of what in the EU context are the processes of exclusion and qualitative selection of economic providers. And this got me thinking about how it would (or not) be possible to replicate some of the projects in an EU jurisdiction (or even at EU-wide level).

And, once again, the issue of the lack of data on which to train algorithms, as well as the lack of representative/comprehensive databases from which to automatically extract information came up. But somehow it seems like the ESPD and the underlying regulatory approach may be making things more difficult.

In the EU, automating mandatory exclusion (not necessarily to have AI adopt decisions, but to have it prepare reports capable of supporting independent decision-making by contracting authorities) would primarily be a matter of checking against databases of prior criminal convictions, which is not only difficult to do due to the absence of structured databases themselves, but also due to the diversity of legal regimes and the languages involved, as well as the pervasive problem of beneficial ownership and (dis)continuity in corporate personality.

Similarly, for discretionary exclusion, automation would primarily be based on retrieving information concerning grounds not easily or routinely captured in existing databases (eg conflicts of interest), as well as limited by increasingly constraining CJEU case law demanding case-by-case assessments by the contracting authority in ways that diminish the advantages of automating eg red flags based on decisions taken by a different contracting authority (or centralised authority).

Finally, automating qualitative selection would be almost impossible, as it is currently mostly based on the self-certification implicit in the ESPD. Here, the 2014 Public Procurement Directives tried to achieve administrative simplification not through the once only principle (which would be useful in creating databases supporting automatisation of some parts of the project, but on which a 2017 project does not seem to have provided many advances), but rather through the ‘tell us only if successful’ (or suspected) principle. This naturally diminishes the amount of information the public buyer (and the broader public sector) holds, with repeat tenderers being completely invisible for the purposes of automation so long as they are not awarded contracts.

All of this leads me to think that there is a big blind spot in the current EU approach to open procurement data as the solution/enabler of automatisation in the context of EU public procurement practice. In fact, most of the crucial (back office) functions — and especially those relating to probity and quality screenings relating to tenderers — will not be susceptible of automation until (or rather unless) different databases are created and advanced mechanisms of interconnection of national databases are created at EU level. And creating those databases will be difficult (or simply not happen in practice) for as long as the ESPD is in place, unless a parallel system of registration (based on the once only principle) is developed for the purposes of registering onto and using eProcurement platforms (which seems to also raise some issues).

So, all in all, it would seem that more than ever we need to concentrate on the baby step of creating a suitable data architecture if we want to reap the benefits of AI (and robotic process automation in particular) any time soon. As other jurisdictions are starting to move (or crawl, to keep with the metaphor), we should not be wasting our time.

AI & sustainable procurement: the public sector should first learn what it already owns

ⓒ Christophe Benoit (Flickr).

[This post was first published at the University of Bristol Law School Blog on 14 October 2019].

While carrying out research on the impact of digital technologies for public procurement governance, I have realised that the deployment of artificial intelligence to promote sustainability through public procurement holds some promise. There are many ways in which machine learning can contribute to enhance procurement sustainability.

For example, new analytics applied to open transport data can significantly improve procurement planning to support more sustainable urban mobility strategies, as well as the emergence of new models for the procurement of mobility as a service (MaaS). Machine learning can also be used to improve the logistics of public sector supply chains, as well as unlock new models of public ownership of eg cars. It can also support public buyers in identifying the green or sustainable public procurement criteria that will deliver the biggest improvements measured against any chosen key performance indicator, such as CO2 footprint, as well as support the development of robust methodologies for life-cycle costing.

However, it is also evident that artificial intelligence can only be effectively deployed where the public sector has an adequate data architecture. While advances in electronic procurement and digital contract registers are capable of generating that data architecture for the future, there is a significant problem concerning the digitalisation of information on the outcomes of past procurement exercises and the current stock of assets owned and used by the public sector. In this blog, I want to raise awareness about this gap in public sector information and to advocate for the public sector to invest in learning what it already owns as a potential major contribution to sustainability in procurement, in particular given the catalyst effect this could have for a more circular procurement economy.

Backward-looking data as a necessary evidence base

It is notorious that the public sector’s management of procurement-related information is lacking. It is difficult enough to have access to information on ‘live’ tender procedures. Accessing information on contract execution and any contractual modifications has been nigh impossible until the very recent implementation of the increased transparency requirements imposed by the EU’s 2014 Public Procurement Package. Moreover, even where that information can be identified, there are significant constraints on the disclosure of competition-sensitive information or business secrets, which can also restrict access. This can be compounded in the case of procurement of assets subject to outsourced maintenance contracts, or in assets procured under mechanisms that do not transfer property to the public sector.

Accessing information on the outcomes of past procurement exercises is thus a major challenge. Where the information is recorded, it is siloed and compartmentalised. And, in any case, this is not public information and it is oftentimes only held by the private firms that supplied the goods or provided the services—with information on public works more likely to be, at least partially, under public sector control. This raises complex issues of business to government (B2G) data sharing, which is only a nascent area of practice and where the guidance provided by the European Commission in 2018 leaves many questions unanswered.

I will not argue here that all that information should be automatically and unrestrictedly publicly disclosed, as that would require some careful considerations of the implications of such disclosures. However, I submit that the public sector should invest in tracing back information on procurement outcomes for all its existing stock of assets (either owned, or used under other contractual forms)—or, at least, in the main categories of buildings and real estate, transport systems and IT and communications hardware. Such database should then be made available to data scientists tasked with seeking all possible ways of optimising the value of that information for the design of sustainable procurement strategies.

In other words, in my opinion, if the public sector is to take procurement sustainability seriously, it should invest in creating a single, centralised database of the durable assets it owns as the necessary evidence base on which to seek to build more sustainable procurement policies. And it should then put that evidence base to good use.

More circular procurement economy based on existing stocks

In my view, some of the main advantages of creating such a database in the short-, medium- and long-term would be as follows.

In the short term, having comprehensive data on existing public sector assets would allow for the deployment of different machine learning solutions to seek, for example, to identify redundant or obsolete assets that could be reassigned or disposed of, or to reassess the efficiency of the existing investments eg in terms of levels of use and potential for increased sharing of assets, or in terms of the energy (in)efficiency derived from their use. It would also allow for a better understanding of potential additional improvements in eg maintenance strategies, as services could be designed having the entirety of the relevant stock into consideration.

In the medium term, this would also provide better insights on the whole life cycle of the assets used by the public sector, including the possibility of deploying machine learning to plan for timely maintenance and replacement, as well as to improve life cycle costing methodologies based on public-sector specific conditions. It would also facilitate the creation of a ‘public sector second-hand market’, where entities with lower levels of performance requirements could acquire assets no longer fit for their original purpose, eg computers previously used in more advanced tasks that still have sufficient capacity could be repurposed for routine administrative tasks. It would also allow for the planning and design of recycling facilities in ways that minimised the carbon footprint of the disposal.

In the long run, in particular post-disposal, the existence of the database of assets could unlock a more circular procurement economy, as the materials of disposed assets could be reused for the building of other assets. In that regard, there seem to be some quick wins to be had in the construction sector, but having access to more and better information would probably also serve as a catalyst for similar approaches in other sectors.

Conclusion

Building a database on existing public sector-used assets as the outcome of earlier procurement exercises is not an easy or cheap task. However, in my view, it would have transformative potential and could generate sustainability gains not only aimed at reducing the carbon footprint of future public expenditure but, more importantly, at correcting or somehow compensating for the current environmental impacts of the way the public sector operates. This could make a major difference in accelerating emissions reductions and should consequently be a matter of sufficient priority for the public sector to engage in this exercise. In my view, it should be a matter of high priority.