By Prof Albert Sanchez-Graells, Professor of Economic Law (University of Bristol Law School).
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.
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.
* A Sanchez-Graells, ‘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 (Berlin, Springer, 2020) forthcoming. Available on SSRN: http://ssrn.com/abstract=3452045.
** A Sanchez-Graells, ‘Data-driven procurement governance: two well-known elephant tales’ (2019) Communications Law, forthcoming. Available on SSRN: https://ssrn.com/abstract=3440552.
*** A Sanchez-Graells, ‘Transparency and competition in public procurement: A comparative view on a difficult balance’, in K-M Halonen, R Caranta & A Sanchez-Graells (eds), Transparency in EU Procurements: Disclosure within public procurement and during contract execution, vol 9 EPL Series (Edward Elgar 2019) 33-56. Available on SSRN: https://ssrn.com/abstract=3193635.