Digital procurement, PPDS and multi-speed datafication -- some thoughts on the March 2023 PPDS Communication

The 2020 European strategy for data ear-marked public procurement as a high priority area for the development of common European data spaces for public administrations. The 2020 data strategy stressed that

Public procurement data are essential to improve transparency and accountability of public spending, fighting corruption and improving spending quality. Public procurement data is spread over several systems in the Member States, made available in different formats and is not easily possible to use for policy purposes in real-time. In many cases, the data quality needs to be improved.

To address those issues, the European Commission was planning to ‘Elaborate a data initiative for public procurement data covering both the EU dimension (EU datasets, such as TED) and the national ones’ by the end of 2020, which would be ‘complemented by a procurement data governance framework’ by mid 2021.

With a 2+ year delay, details for the creation of the public procurement data space (PPDS) were disclosed by the European Commission on 16 March 2023 in the PPDS Communication. The procurement data governance framework is now planned to be developed in the second half of 2023.

In this blog post, I offer some thoughts on the PPDS, its functional goals, likely effects, and the quickly closing window of opportunity for Member States to support its feasibility through an ambitious implementation of the new procurement eForms at domestic level (on which see earlier thoughts here).

1. The PPDS Communication and its goals

The PPDS Communication sets some lofty ambitions aligned with those of the closely-related process of procurement digitalisation, which the European Commission in its 2017 Making Procurement Work In and For Europe Communication already saw as not only an opportunity ‘to streamline and simplify the procurement process’, but also ‘to rethink fundamentally the way public procurement, and relevant parts of public administrations, are organised … [to seize] a unique chance to reshape the relevant systems and achieve a digital transformation’ (at 11-12).

Following the same rhetoric of transformation, the PPDS Communication now stresses that ‘Integrated data combined with the use of state-of the-art and emerging analytics technologies will not only transform public procurement, but also give new and valuable insights to public buyers, policy-makers, businesses and interested citizens alike‘ (at 2). It goes further to suggest that ‘given the high number of ecosystems concerned by public procurement and the amount of data to be analysed, the impact of AI in this field has a potential that we can only see a glimpse of so far‘ (at 2).

The PPDS Communication claims that this data space ‘will revolutionise the access to and use of public procurement data:

  • It will create a platform at EU level to access for the first time public procurement data scattered so far at EU, national and regional level.

  • It will considerably improve data quality, availability and completeness, through close cooperation between the Commission and Member States and the introduction of the new eForms, which will allow public buyers to provide information in a more structured way.

  • This wealth of data will be combined with an analytics toolset including advanced technologies such as Artificial Intelligence (AI), for example in the form of Machine Learning (ML) and Natural Language Processing (NLP).’

A first comment or observation is that this rhetoric of transformation and revolution not only tends to create excessive expectations on what can realistically be delivered by the PPDS, but can also further fuel the ‘policy irresistibility’ of procurement digitalisation and thus eg generate excessive experimentation or investment into the deployment of digital technologies on the basis of such expectations around data access through PPDS (for discussion, see here). Policy-makers would do well to hold off on any investments and pilot projects seeking to exploit the data presumptively pooled in the PPDS until after its implementation. A closer look at the PPDS and the significant roadblocks towards its full implementation will shed further light on this issue.

2. What is the PPDS?

Put simply, the PPDS is a project to create a single data platform to bring into one place ‘all procurement data’ from across the EU—ie both data on above threshold contracts subjected to mandatory EU-wide publication through TED (via eForms from October 2023), and data on below threshold contracts, which publication may be required by the domestic laws of the Member States, or entirely voluntary for contracting authorities.

Given that above threshold procurement data is already (in the process of being) captured at EU level, the PPDS is very much about data on procurement not covered by the EU rules—which represents 80% of all public procurement contracts. As the PPDS Communication stresses

To unlock the full potential of public procurement, access to data and the ability to analyse it are essential. However, data from only 20% of all call for tenders as submitted by public buyers is available and searchable for analysis in one place [ie TED]. The remaining 80% are spread, in different formats, at national or regional level and difficult or impossible to re-use for policy, transparency and better spending purposes. In order (sic) words, public procurement is rich in data, but poor in making it work for taxpayers, policy makers and public buyers.

The PPDS thus intends to develop a ‘technical fix’ to gain a view on the below-threshold reality of procurement across the EU, by ‘pulling and pooling’ data from existing (and to be developed) domestic public contract registers and transparency portals. The PPDS is thus a mechanism for the aggregation of procurement data currently not available in (harmonised) machine-readable and structured formats (or at all).

As the PPDS Communication makes clear, it consists of four layers:
(1) A user interface layer (ie a website and/or app) underpinned by
(2) an analytics layer, which in turn is underpinned by (3) an integration layer that brings together and minimally quality-assures the (4) data layer sourced from TED, Member State public contract registers (including those at sub-national level), and data from other sources (eg data on beneficial ownership).

The two top layers condense all potential advantages of the PPDS, with the analytics layer seeking to develop a ‘toolset including emerging technologies (AI, ML and NLP)‘ to extract data insights for a multiplicity of purposes (see below 3), and the top user interface seeking to facilitate differential data access for different types of users and stakeholders (see below 4). The two bottom layers, and in particular the data layer, are the ones doing all the heavy lifting. Unavoidably, without data, the PPDS risks being little more than an empty shell. As always, ‘no data, no fun’ (see below 5).

Importantly, the top three layers are centralised and the European Commission has responsibility (and funding) for developing them, while the bottom data layer is decentralised, with each Member State retaining responsibility for digitalising its public procurement systems and connecting its data sources to the PPDS. Member States are also expected to bear their own costs, although there is EU funding available through different mechanisms. This allocation of responsibilities follows the limited competence of the EU in this area of inter-administrative cooperation, which unfortunately heightens the risks of the PPDS becoming little more than an empty shell, unless Member States really take the implementation of eForms and the collaborative approach to the construction of the PPDS seriously (see below 6).

The PPDS Communication foresees a progressive implementation of the PPDS, with the goal of having ‘the basic architecture and analytics toolkit in place and procurement data published at EU level available in the system by mid-2023. By the end of 2024, all participating national publication portals would be connected, historic data published at EU level integrated and the analytics toolkit expanded. As of 2025, the system could establish links with additional external data sources’ (at 2). It will most likely be delayed, but that is not very important in the long run—especially as the already accrued delays are the ones that pose a significant limitation on the adequate rollout of the PPDS (see below 6).

3. PPDS’ expected functionality

The PPDS Communication sets expectations around the functionality that could be extracted from the PPDS by different agents and stakeholders.

For public buyers, in addition to reducing the burden of complying with different types of (EU-mandated) reporting, the PPDS Communication expects that ‘insights gained from the PPDS will make it much easier for public buyers to

  • team up and buy in bulk to obtain better prices and higher quality;

  • generate more bids per call for tenders by making calls more attractive for bidders, especially for SMEs and start-ups;

  • fight collusion and corruption, as well as other criminal acts, by detecting suspicious patterns;

  • benchmark themselves more accurately against their peers and exchange knowledge, for instance with the aim of procuring more green, social and innovative products and services;

  • through the further digitalisation and emerging technologies that it brings about, automate tasks, bringing about considerable operational savings’ (at 2).

This largely maps onto my analysis of likely applications of digital technologies for procurement management, assuming the data is there (see here).

The PPDS Communication also expects that policy-makers will ‘gain a wealth of insights that will enable them to predict future trends‘; that economic operators, and SMEs in particular, ‘will have an easy-to-use portal that gives them access to a much greater number of open call for tenders with better data quality‘, and that ‘Citizens, civil society, taxpayers and other interested stakeholders will have access to much more public procurement data than before, thereby improving transparency and accountability of public spending‘ (at 2).

Of all the expected benefits or functionalities, the most important ones are those attributed to public buyers and, in particular, the possibility of developing ‘category management’ insights (eg potential savings or benchmarking), systems of red flags in relation to corruption and collusion risks, and the automation of some tasks. However, unlocking most of these functionalities is not dependent on the PPDS, but rather on the existence of procurement data at the ‘right’ level.

For example, category management or benchmarking may be more relevant or adequate (as well as more feasible) at national than at supra-national level, and the development of systems of red flags can also take place at below-EU level, as can automation. Importantly, the development of such functionalities using pan-EU data, or data concerning more than one Member State, could bias the tools in a way that makes them less suited, or unsuitable, for deployment at national level (eg if the AI is trained on data concerning solely jurisdictions other than the one where it would be deployed).

In that regard, the expected functionalities arising from PPDS require some further thought and it can well be that, depending on implementation (in particular in relation to multi-speed datafication, as below 5), Member States are better off solely using domestic data than that coming from the PPDS. This is to say that PPDS is not a solid reality and that its enabling character will fluctuate with its implementation.

4. Differential procurement data access through PPDS

As mentioned above, the PPDS Communication stresses that ‘Citizens, civil society, taxpayers and other interested stakeholders will have access to much more public procurement data than before, thereby improving transparency and accountability of public spending’ (at 2). However, this does not mean that the PPDS will be (entirely) open data.

The Communication itself makes clear that ‘Different user categories (e.g. Member States, public buyers, businesses, citizens, NGOs, journalists and researchers) will have different access rights, distinguishing between public and non-public data and between participating Member States that share their data with the PPDS (PPDS members, …) and those that need more time to prepare’ (at 8). Relatedly, ‘PPDS members will have access to data which is available within the PPDS. However, even those Member States that are not yet ready to participate in the PPDS stand to benefit from implementing the principles below, due to their value for operational efficiency and preparing for a more evidence-based policy’ (at 9). This raises two issues.

First, and rightly, the Communication makes clear that the PPDS moves away from a model of ‘fully open’ or ‘open by default’ procurement data, and that access to the PPDS will require differential permissioning. This is the correct approach. Regardless of the future procurement data governance framework, it is clear that the emerging thicket of EU data governance rules ‘requires the careful management of a system of multi-tiered access to different types of information at different times, by different stakeholders and under different conditions’ (see here). This will however raise significant issues for the implementation of the PPDS, as it will generate some constraints or disincentives for an ambitions implementation of eForms at national level (see below 6).

Second, and less clearly, the PPDS Communication evidences that not all Member States will automatically have equal access to PPDS data. The design seems to be such that Member States that do not feed data into PPDS will not have access to it. While this could be conceived as an incentive for all Member States to join PPDS, this outcome is by no means guaranteed. As above (3), it is not clear that Member States will be better off—in terms of their ability to extract data insights or to deploy digital technologies—by having access to pan-EU data. The main benefit resulting from pan-EU data only accrues collectively and, primarily, by means of facilitating oversight and enforcement by the European Commission. From that perspective, the incentives for PPDS participation for any given Member State may be quite warped or internally contradictory.

Moreover, given that plugging into PPDS is not cost-free, a Member State that developed a data architecture not immediately compatible with PPDS may well wonder whether it made sense to shoulder the additional costs and risks. From that perspective, it can only be hoped that the existence of EU funding and technical support will be maximised by the European Commission to offload that burden from the (reluctant) Member States. However, even then, full PPDS participation by all Member States will still not dispel the risk of multi-speed datafication.

5. No data, no fun — and multi-speed datafication

Related to the risk that some EU Member States will become PPDS members and others not, there is a risk (or rather, a reality) that not all PPDS members will equally contribute data—thus creating multi-speed datafication, even within the Member States that opt in to the PPDS.

First, the PPDS Communication makes it clear that ‘Member States will remain in control over which data they wish to share with the PPDS (beyond the data that must be published on TED under the Public Procurement Directives)‘ (at 7), It further specifies that ‘With the eForms, it will be possible for the first time to provide data in notices that should not be published, or not immediately. This is important to give assurance to public buyers that certain data is not made publicly available or not before a certain point in time (e.g. prices)’ (at 7, fn 17).

This means that each Member State will only have to plug whichever data it captures and decides to share into PPDS. It seems plain to see that this will result in different approaches to data capture, multiple levels of granularity, and varying approaches to restricting access to the date in the different Member States, especially bearing in mind that ‘eForms are not an “off the shelf” product that can be implemented only by IT developers. Instead, before developers start working, procurement policy decision-makers have to make a wide range of policy decisions on how eForms should be implemented’ in the different Member States (see eForms Implementation Handbook, at 9).

Second, the PPDS Communication is clear (in a footnote) that ‘One of the conditions for a successful establishment of the PPDS is that Member States put in place automatic data capture mechanisms, in a first step transmitting data from their national portals and contract registers’ (at 4, fn 10). This implies that Member States may need to move away from manually inputted information and that those seeking to create new mechanisms for automatic procurement data capture can take an incremental approach, which is very much baked into the PPDS design. This relates, for example, to the distinction between pre- and post-award procurement data, with pre-award data subjected to higher demands under EU law. It also relates to above and below threshold data, as only above threshold data is subjected to mandatory eForms compliance.

In the end, the extent to which a (willing) Member State will contribute data to the PPDS depends on its decisions on eForms implementation, which should be well underway given the October 2023 deadline for mandatory use (for above threshold contracts). Crucially, Member States contributing more data may feel let down when no comparable data is contributed to PPDS by other Member States, which can well operate as a disincentive to contribute any further data, rather than as an incentive for the others to match up that data.

6. Ambitious eForms implementation as the PPDS’ Achilles heel

As the analysis above has shown, the viability of the PPDS and its fitness for purpose (especially for EU-level oversight and enforcement purposes) crucially depends on the Member States deciding to take an ambitious approach to the implementation of eForms, not solely by maximising their flexibility for voluntary uses (as discussed here) but, crucially, by extending their mandatory use (under national law) to all below threshold procurement. It is now also clear that there is a need for as much homogeneity as possible in the implementation of eForms in order to guarantee that the information plugged into PPDS is comparable—which is an aspect of data quality that the PPDS Communication does not seem to have at all considered).

It seems that, due to competing timings, this poses a bit of a problem for the rollout of the PPDS. While eForms need to be fully implemented domestically by October 2023, the PPDS Communication suggests that the connection of national portals will be a matter for 2024, as the first part of the project will concern the top two layers and data connection will follow (or, at best, be developed in parallel). Somehow, it feels like the PPDS is being built without a strong enough foundation. It would be a shame (to put it mildly) if Member States having completed a transition to eForms by October 2023 were dissuaded from a second transition into a more ambitious eForms implementation in 2024 for the purposes of the PPDS.

Given that the most likely approach to eForms implementation is rather minimalistic, it can well be that the PPDS results in not much more than an empty shell with fancy digital analytics limited to very superficial uses. In that regard, the two-year delay in progressing the PPDS has created a very narrow (and quickly dwindling) window of opportunity for Member States to engage with an ambitions process of eForms implementation

7. Final thoughts

It seems to me that limited and slow progress will be attained under the PPDS in coming years. Given the undoubted value of harnessing procurement data, I sense that Member States will progress domestically, but primarily in specific settings such as that of their central purchasing bodies (see here). However, whether they will be onboarded into PPDS as enthusiastic members seems less likely.

The scenario seems to resemble limited voluntary cooperation in other areas (eg interoperability; for discussion see here). It may well be that the logic of EU competence allocation required this tentative step as a first move towards a more robust and proactive approach by the Commission in a few years, on grounds that the goal of creating the European data space could not be achieved through this less interventionist approach.

However, given the speed at which digital transformation could take place (and is taking place in some parts of the EU), and the rhetoric of transformation and revolution that keeps being used in this policy area, I can’t but feel let down by the approach in the PPDS Communication, which started with the decision to build the eForms on the existing regulatory framework, rather than more boldly seeking a reform of the EU procurement rules to facilitate their digital fitness.

Registration open: TECH FIXES FOR PROCUREMENT PROBLEMS?

As previously announced, on 15 December, I will have the chance to discuss my ongoing research on procurement digitalisation with a stellar panel: Eliza Niewiadomska (EBRD), Jessica Tillipman (GW Law), and Sope Williams (Stellenbosch).

The webinar will provide an opportunity to take a hard look at the promise of tech fixes for procurement problems, focusing on key issues such as:

  • The ‘true’ potential of digital technologies in procurement.

  • The challenges arising from putting key enablers in place, such as an adequate big data architecture and access to digital skills in short supply.

  • The challenges arising from current regulatory frameworks and constraints not applicable to the private sector.

  • New challenges posed by data governance and cybersecurity risks.

The webinar will be held on December 15, 2022 at 9:00 am EST / 2:00 pm GMT / 3:00 pm CET-SAST. Full details and registration at: https://blogs.gwu.edu/law-govpro/tech-fixes-for-procurement-problems/.

Save the date: 15 Dec, Tech fixes for procurement problems?

If you are interested in procurement digitalisation, please save the date for an online workshop on ‘Tech fixes for procurement problems?’ on 15 December 2022, 2pm GMT. I will have the chance to discuss my ongoing research (scroll down for a few samples) with a stellar panel: Eliza Niewiadomska (EBRD), Jessica Tillipman (GW Law), and Sope Williams (Stellenbosch). We will also have plenty time for a conversation with participants. Do not let other commitments get on the way of joining the discussion!

More details and registration coming soon. For any questions, please email me: a.sanchez-graells@bristol.ac.uk.

Digital procurement governance: drawing a feasibility boundary

In the current context of generalised quick adoption of digital technologies across the public sector and strategic steers to accelerate the digitalisation of public procurement, decision-makers can be captured by techno hype and the ‘policy irresistibility’ that can ensue from it (as discussed in detail here, as well as here).

To moderate those pressures and guide experimentation towards the successful deployment of digital solutions, decision-makers must reassess the realistic potential of those technologies in the specific context of procurement governance. They must also consider which enabling factors must be put in place to harness the potential of the digital technologies—which primarily relate to an enabling big data architecture (see here). Combined, the data requirements and the contextualised potential of the technologies will help decision-makers draw a feasibility boundary for digital procurement governance, which should inform their decisions.

In a new draft chapter (num 7) for my book project, I draw such a technology-informed feasibility boundary for digital procurement governance. This post provides a summary of my main findings, on which I will welcome any comments: a.sanchez-graells@bristol.ac.uk. The full draft chapter is free to download: A Sanchez-Graells, ‘Revisiting the promise: A feasibility boundary for digital procurement governance’ to be included in A Sanchez-Graells, Digital Technologies and Public Procurement. Gatekeeping and experimentation in digital public governance (OUP, forthcoming). Available at SSRN: https://ssrn.com/abstract=4232973.

Data as the main constraint

It will hardly be surprising to stress again that high quality big data is a pre-requisite for the development and deployment of digital technologies. All digital technologies of potential adoption in procurement governance are data-dependent. Therefore, without adequate data, there is no prospect of successful adoption of the technologies. The difficulties in generating an enabling procurement data architecture are detailed here.

Moreover, new data rules only regulate the capture of data for the future. This means that it will take time for big data to accumulate. Accessing historical data would be a way of building up (big) data and speeding up the development of digital solutions. Moreover, in some contexts, such as in relation with very infrequent types of procurement, or in relation to decisions concerning previous investments and acquisitions, historical data will be particularly relevant (eg to deploy green policies seeking to extend the use life of current assets through programmes of enhanced maintenance or refurbishment; see here). However, there are significant challenges linked to the creation of backward-looking digital databases, not only relating to the cost of digitisation of the information, but also to technical difficulties in ensuring the representativity and adequate labelling of pre-existing information.

An additional issue to consider is that a number of governance-relevant insights can only be extracted from a combination of procurement and other types of data. This can include sources of data on potential conflict of interest (eg family relations, or financial circumstances of individuals involved in decision-making), information on corporate activities and offerings, including detailed information on products, services and means of production (eg in relation with licensing or testing schemes), or information on levels of utilisation of public contracts and satisfaction with the outcomes by those meant to benefit from their implementation (eg users of a public service, or ‘internal’ users within the public administration).

To the extent that the outside sources of information are not digitised, or not in a way that is (easily) compatible or linkable with procurement information, some data-based procurement governance solutions will remain undeliverable. Some developments in digital procurement governance will thus be determined by progress in other policy areas. While there are initiatives to promote the availability of data in those settings (eg the EU’s Data Governance Act, the Guidelines on private sector data sharing, or the Open Data Directive), the voluntariness of many of those mechanisms raises important questions on the likely availability of data required to develop digital solutions.

Overall, there is no guarantee that the data required for the development of some (advanced) digital solutions will be available. A careful analysis of data requirements must thus be a point of concentration for any decision-maker from the very early stages of considering digitalisation projects.

Revised potential of selected digital technologies

Once (or rather, if) that major data hurdle is cleared, the possibilities realistically brought by the functionality of digital technologies need to be embedded in the procurement governance context, which results in the following feasibility boundary for the adoption of those technologies.

Robotic Process Automation (RPA)

RPA can reduce the administrative costs of managing pre-existing digitised and highly structured information in the context of entirely standardised and repetitive phases of the procurement process. RPA can reduce the time invested in gathering and cross-checking information and can thus serve as a basic element of decision-making support. However, RPA cannot increase the volume and type of information being considered (other than in cases where some available information was not being taken into consideration due to eg administrative capacity constraints), and it can hardly be successfully deployed in relation to open-ended or potentially contradictory information points. RPA will also not change or improve the processes themselves (unless they are redesigned with a view to deploying RPA).

This generates a clear feasibility boundary for RPA deployment, which will generally have as its purpose the optimisation of the time available to the procurement workforce to engage in information analysis rather than information sourcing and basic checks. While this can clearly bring operational advantages, it will hardly transform procurement governance.

Machine Learning (ML)

Developing ML solutions will pose major challenges, not only in relation to the underlying data architecture (as above), but also in relation to specific regulatory and governance requirements specific to public procurement. Where the operational management of procurement does not diverge from the equivalent function in the (less regulated) private sector, it will be possible to see the adoption or adaptation of similar ML solutions (eg in relation to category spend management). However, where there are regulatory constraints on the conduct of procurement, the development of ML solutions will be challenging.

For example, the need to ensure the openness and technical neutrality of procurement procedures will limit the possibilities of developing recommender systems other than in pre-procured closed lists or environments based on framework agreements or dynamic purchasing systems underpinned by electronic catalogues. Similarly, the intended use of the recommender system may raise significant legal issues concerning eg the exercise of discretion, which can limit their deployment to areas of information exchange or to merely suggestion-based tasks that could hardly replace current processes and procedures. Given the limited utility (or acceptability) of collective filtering recommender solutions (which is the predominant type in consumer-facing private sector uses, such as Netflix or Amazon), there are also constraints on the generality of content-based recommender systems for procurement applications, both at tenderer and at product/service level. This raises a further feasibility issue, as the functional need to develop a multiplicity of different recommenders not only reopens the issue of data sufficiency and adequacy, but also raises questions of (economic and technical) viability. Recommender systems would mostly only be susceptible of feasible adoption in highly centralised procurement settings. This could create a push for further procurement centralisation that is not neutral from a governance perspective, and that can certainly generate significant competition issues of a similar nature, but perhaps a different order of magnitude, than procurement centralisation in a less digitally advanced setting. This should be carefully considered, as the knock-on effects of the implementation of some ML solutions may only emerge down the line.

Similarly, the development and deployment of chatbots is constrained by specific regulatory issues, such as the need to deploy closed domain chatbots (as opposed to open domain chatbots, ie chatbots connected to the Internet, such as virtual assistants built into smartphones), so that the information they draw from can be controlled and quality assured in line with duties of good administration and other legal requirements concerning the provision of information within tender procedures. Chatbots are suited to types of high-volume information-based queries only. They would have limited applicability in relation to the specific characteristics of any given procurement procedure, as preparing the specific information to be used by the chatbot would be a challenge—with the added functionality of the chatbot being marginal. Chatbots could facilitate access to pre-existing and curated simple information, but their functionality would quickly hit a ceiling as the complexity of the information progressed. Chatbots would only be able to perform at a higher level if they were plugged to a knowledge base created as an expert system. But then, again, in that case their added functionality would be marginal. Ultimately, the practical space for the development of chatbots is limited to low added value information access tasks. Again, while this can clearly bring operational advantages, it will hardly transform procurement governance.

ML could facilitate the development and deployment of ‘advanced’ automated screens, or red flags, which could identify patterns of suspicious behaviour to then be assessed against the applicable rules (eg administrative and criminal law in case of corruption, or competition law, potentially including criminal law, in case of bid rigging) or policies (eg in relation to policy requirements to comply with specific targets in relation to a broad variety of goals). The trade off in this type of implementation is between the potential (accuracy) of the algorithmic screening and legal requirements on the explainability of decision-making (as discussed in detail here). Where the screens were not used solely for policy analysis, but acting on the red flag carried legal consequences (eg fines, or even criminal sanctions), the suitability of specific types of ML solutions (eg unsupervised learning solutions tantamount to a ‘black box’) would be doubtful, challenging, or altogether excluded. In any case, the development of ML screens capable of significantly improving over RPA-based automation of current screens is particularly dependent on the existence of adequate data, which is still proving an insurmountable hurdle in many an intended implementation (as above).

Distributed ledger technology (DLT) systems and smart contracts

Other procurement governance constraints limit the prospects of wholesale adoption of DLT (or blockchain) technologies, other than for relatively limited information management purposes. The public sector can hardly be expected to adopt DLT solutions that are not heavily permissioned, and that do not include significant safeguards to protect sensitive, commercially valuable, and other types of information that cannot be simply put in the public domain. This means that the public sector is only likely to implement highly centralised DLT solutions, with the public sector granting permissions to access and amend the relevant information. While this can still generate some (degrees of) tamper-evidence and permanence of the information management system, the net advantage is likely to be modest when compared to other types of secure information management systems. This can have an important bearing on decisions whether DLT solutions meet cost effectiveness or similar criteria of value for money controlling their piloting and deployment.

The value proposition of DLT solutions could increase if they enabled significant procurement automation through smart contracts. However, there are massive challenges in translating procurement procedures to a strict ‘if/when ... then’ programmable logic, smart contracts have limited capability that is not commensurate with the volumes and complexity of procurement information, and their development would only be justified in contexts where a given smart contract (ie specific programme) could be used in a high number of procurement procedures. This limits its scope of applicability to standardised and simple procurement exercises, which creates a functional overlap with some RPA solutions. Even in those settings, smart contracts would pose structural problems in terms of their irrevocability or automaticity. Moreover, they would be unable to generate off-chain effects, and this would not be easily sorted out even with the inclusion of internet of things (IoT) solutions or software oracles. This comes to largely restrict smart contracts to an information exchange mechanism, which does not significantly increase the value added by DLT plus smart contract solutions for procurement governance.

Conclusion

To conclude, there are significant and difficult to solve hurdles in generating an enabling data architecture, especially for digital technologies that require multiple sources of information or data points regarding several phases of the procurement process. Moreover, the realistic potential of most technologies primarily concerns the automation of tasks not involving data analysis of the exercise of procurement discretion, but rather relatively simple information cross-checks or exchanges. Linking back to the discussion in the earlier broader chapter (see here), the analysis above shows that a feasibility boundary emerges whereby the adoption of digital technologies for procurement governance can make contributions in relation to its information intensity, but not easily in relation to its information complexity, at least not in the short to medium term and not in the absence of a significant improvement of the required enabling data architecture. Perhaps in more direct terms, in the absence of a significant expansion in the collection and curation of data, digital technologies can allow procurement governance to do more of the same or to do it quicker, but it cannot enable better procurement driven by data insights, except in relatively narrow settings. Such settings are characterised by centralisation. Therefore, the deployment of digital technologies can be a further source of pressure towards procurement centralisation, which is not a neutral development in governance terms.

This feasibility boundary should be taken into account in considering potential use cases, as well as serve to moderate the expectations that come with the technologies and that can fuel ‘policy irresistibility’. Further, it should be stressed that those potential advantages do not come without their own additional complexities in terms of new governance risks (eg data and data systems integrity, cybersecurity, skills gaps) and requirements for their mitigation. These will be explored in the next stage of my research project.