=Paper=
{{Paper
|id=Vol-3214/WS8Paper4
|storemode=property
|title=Using the D-BEST Reference Model to Compare Italian and Polish Digital Innovation Hubs
|pdfUrl=https://ceur-ws.org/Vol-3214/WS8Paper4.pdf
|volume=Vol-3214
|authors=Walter Quadrini,Bartlomiej Gladysz,Sergio Terzi,Claudio Sassanelli
|dblpUrl=https://dblp.org/rec/conf/iesa/QuadriniGTS22
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==Using the D-BEST Reference Model to Compare Italian and Polish Digital Innovation Hubs==
https://ceur-ws.org/Vol-3214/WS8Paper4.pdf
Using the D-BEST Reference Model to Compare Italian and
Polish Digital Innovation Hubs
Walter Quadrini1, Bartlomiej Gladysz2, Sergio Terzi1 and Claudio Sassanelli1,3
1
Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Piazza Leonardo
da Vinci, 32, 20133 Milan, Italy
2
Warsaw University of Technology, Faculty of Mechanical and Industrial Engineering, Poland
3
Department of Mechanics, Mathematics and Management, Politecnico di Bari, Via Orabona 4, 70125, Bari,
Italy
Abstract
In recent years, the increasing importance of Digital Innovation Hubs (DIHs) in supporting
manufacturing Small and Medium Enterprises (SMEs) has been widely studied and several
works listing lessons learnt and success stories have been published. To further foster the
impact of these entities on the SMEs’ environment, The European Commission has recently
introduced the Smart Specialisation Platform, which contains a web service returning to its
users a geo-distributed list of DIHs, allowing the user also to cluster and visualise them
according to some pre-defined filters, such as the types of technologies employed. The data
provided by this platform has been downloaded and a secondary data analysis, based on the
websites of the DIHs has been carried on to frame the single DIHs according to the axes of
the D-BEST methodology. A comparative analysis with respect to the Italian and Poland
situation completes the study, to understand eventual differences and affinities among the
two countries.
Keywords 1
Digital innovation hub, service portfolio, digital transformation
1. Introduction
Since its introduction in 2011, “Industry 4.0” (I4.0) has been retained a “game changer” for the
manufacturing scenario [1], leveraging on its technological pillars [2] to provide quantifiable benefits
for the manufacturing firms which embraced its paradigm [3]. These benefits have been widely
studied in literature and have been generally inflected in several areas of the manufacturing business
and strategy: supply chain management, internal logistics, maintenance and decision-making are
maybe the most well-known applications of the I4.0 paradigm [4], but recent studies have also
demonstrated the implication of “4.0” practices with respect to some long-term objectives, such as the
accomplishment of sustainable practices [5] or the extension of the productive life of elder workers
[6]. As for the benefits, several studies have at the same time addressed barriers and issues of this
paradigm too, focusing, inter alia, on the ethical drawbacks. Among these, several studies in peculiar
trade journals have been focused on the ethical consequences of the particular technologies involved,
such as the so-called Cyber-Physical Systems (CPSs) [7], or the Artificial Intelligence, highly debated
because of its blameworthiness-related issues [8]. On the other hand, other works highlighted a
specific drawback, as the paradigm of I4.0 itself seems to be an inherent policy maker: the high skills’
level required by the technology integration [9] making indeed the paradigm adoption biased towards
big enterprises, leaving behind Small and Medium Enterprises (SMEs) [10] which are threatened by
Proceedings of the Workshop of I-ESA’22, March 23–24, 2022, Valencia, Spain
EMAIL: walter.quadrini@polimi.it (W. Quadrini); bartlomiej.gladysz@pw.edu.pl (B. Gladysz); sergio.terzi@polimi.it (S. Terzi);
claudio.sassanelli@polimi.it (C. Sassanelli).
ORCID: 0000-0003-0081-2255 (W. Quadrini); 0000-0003-0619-0194 (B. Gladysz); 0000-0003-0438-6185 (S. Terzi); 0000-0003-3603-
9735 (C. Sassanelli)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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their own nature in the acquisition of highly-specialised personnel, preventing them from getting the
aforementioned benefits [11]. In this context, given the great importance of manufacturing SMEs in
the European economic framework [12], the need of a digital transformation of these entities –
towards the I4.0 paradigm – emerges as well, and, in order to ease the access of this kind of
companies to the digital services [13], a European-driven public initiative aiming to connect
manufacturing SMEs to digital services providers has arisen, generating the “Digital Innovation
Hubs” (DIHs), entities aiming at supporting these manufacturing companies in their digital transition
[14]. DIHs can hence act as brokers between customer and suppliers for what concerns the digital
service, but their role could also be extended to wider areas of the digitalisation of manufacturing. For
example, the gathering of funding actions supporting revamping/refurbishment activities is one of the
activities where SMEs lack the administrative skills to properly apply for grants, and where the DIH
assistance could constitute a bonus [15], but a tailor-made business strategy for the digitalisation of a
manufacturing firm is a typical task performed by a DIH as well [16, 17].
To provide an organic offer to the end users, an initiative of the European Commission (EC) has
assessed the DIHs on the European territory according to their geographical position, as well as
according to the types of services offered to SMEs. This type of clustering is supposed to allow the
users to filter the DIHs available on the platform according to their needs, to select the closest one
offering the type of service the end user is interested in, and has been released under the Smart
Specialisation Platform (S3) [18].
To frame the same problem under a different theoretical lens, the list of the DIHs – both fully
operational and in preparation – has been re-assessed, not on the basis of User Generated Content
(UGC) but on a D-BEST analysis based on the website data.
The D-BEST analysis constitutes hence Section Error! Reference source not found.; Section 3
depicts the methodology and the results obtained analysing the DIHs of Italy and Poland; Section 4
closes the work providing some evidence from the study.
2. Methodology
2.1. The D-BEST model
The Data-based Business-Ecosystem-Skills-Technology (D-BEST) is a reference model to
categorize DIHs’ service portfolio [14, 19, 20], developed, validated and applied along the years in
several innovation action projects funded by the European Commission and related to both the cyber-
physical systems (CPS) domain (i.e., MIDIH, DIH4CPS, HUBCAP) and the artificial intelligence one
(AI REGIO and DIH4AI). Indeed, it is the result of several iterations of development along the years.
The original model was named “ETB”, proposed by [21] and grounded on three main macro-classes
of services (Ecosystem, Technology, and Business). The ETB was enriched with the Skills and Data
macro-classes, strictly needed to better answer to the digital needs triggered by the I4.0 domain. The
resulting model, named in a first moment ETBSD and then D-BEST, is composed of five macro-
classes of services (Data, Business, Ecosystem, Skills, Technology), and is broken down according to
a three-levels taxonomy defined to better detail and classify the type of activity [19].
2.2. Data extraction and evaluation
A standardised extraction has been performed on the S3 platform: from the embedded interface,
filters have been set for what concerns “Countries” (“Italy” and “Poland”) and “Evolutionary stage”
(“in progress” and “fully operational”, discarding “in preparation”). All the other voices have been
left unmarked, resulting in no additional filters.
The downloaded content has been hence clustered in four different datasets, according to the
DIHs’ origin (Italy or Poland) and evolutionary stage (fully operational or in preparation). A total of
82 DIHs (including duplicated entries and inactive items) has been gathered, and, for each of these
ones, the respective website has been systematically explored to find proofs of declared and
performed activities.
The factors considered in driving the exploration have been the accomplishments to the activity
types characterising the D-BEST methodology and every active and unique DIH has been flagged
with the activity it provides services about.
3. Results
For what concerns Italy, a total of 68 DIHs results on the S3 platform. This number results from
the merge of 53 fully operational ones and 15 in preparation. Among the 53 ones, 8 items have been
discarded as duplicates and among the 15 ones, 5 websites resulted not active yet.
With respect to the Polish DIHs, a total of 14 DIHs has been returned by the platform: one half of
them belongs to the fully operational ones, while the other half is not active yet. All the websites
resulted unique and active. Figure 1 depicts, for each country and evolutionary stage, the number of
DIHs offering a service related to the D-BEST activity types.
Fully Operational
In Italy
Fully Operational
in Poland
In Preparation
in Italy
In Preparation
in Poland
0 5 10 15 20 25 30 35 40
Data Business Ecosystem Skills Technology
Figure 1. Italian and Polish DIHs D-BEST clustering
4. Discussion and conclusions
As depicted in the figures above, Italian and Polish DIHs appears quite aligned despite the
territorial differences. Considering the specific activity types implied by D-BEST, Table 1 offers an
overview of the percentage of DIHs offering these services.
Table 1
Percentage of DIHs offering D-BEST activity types
Current Future
(“Fully operational” at Dec 2021) (including also “In progress” ones at Dec
2021)
Italy Poland Italy Poland
Data 9% 14% 15% 7%
Business 72% 100% 82% 100%
Ecosystem 69% 56% 69% 71%
Skills 78% 71% 75% 79%
Technology 49% 71% 56% 79%
A certain consistency can be noticed for some kind of services, (i.e., those related to Ecosystem,
Data and Skills). All Polish DIHs are, then, offering Business-related services. This could be linked to
the smaller pool of Polish DIHs, which doesn’t mirror the existence of some specialised DIHs like in
the Italian framework. Technology-related services present some differences too, where Polish DIHs
seem to be more active. A likely interpretation could lay in the geographic difference, which sees the
Central-Eastern Europe level of digitalisation below the EU average [22] bringing companies to
demand more technology-based services, but further studies on a wider dataset (e.g. including other
countries) could confirm or deny this assumption. There is also visible significant difference in direct
number of DIHs in Italy (45 fully operational and 10 in progress) and Poland (7 fully operational, 7 in
progress), but also in DIHs per capita (9.3*10-7 DIH/person in Italy, 3.6*10-7 DIH/person in Poland)
but appear quite aligned if considering the number of DIHs per GDP (2.6*10-5 DIH/mln€ in Italy,
2.4*10-5 DIH/mln€ in Poland). Analysing causes of such situation is not the purpose of this work, but
further research could provide drivers able to justify the aforementioned numbers, with the eventual
proof of concept given by a more heterogeneous dataset.
5. Acknowledgments
This work received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 872698.
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