Digital Innovation Hubs (DIHs) are ecosystems bolstering European companies to overtake innovation hindrances and drive Europe to become the world leading innovator in the industry digital revolution. Each of such organizations can provide a certain list of services, that can be classified and grouped in five macro-classes according to the Data-driven Business-Ecosystem-Skills-Technology (D-BEST) reference model, able to decode DIHs' service portfolio and to shape collaborative networks in the Industry 4.0 age. However, to support an easier codification of DIH support actions, which also directly entails the engagement of enterprises in the DIH ecosystems, a method able to analyze typical Customer Journeys (CJs) is needed. Therefore, this paper proposes the D-BEST based DIH CJ analysis method, able to configure DIHs' unique value proposition, mapping on the five macro-classes of services of the D-BEST the digital transformation processes of the two main categories of DIH customers (technology end-users and technology providers). The method analyses the service provision process of single DIHs, evidencing their strengths and weaknesses, and is also effective in suggesting possible collaborations and joint service provision in a network of multiple DIHs, being able to unveil the commonalities and complementarities among the different journeys.
Keywords: Digital innovation hub; DIH; customer journey; service portfolio; digital transformation; cyber-physical system
Technology is increasingly playing a key role in today's business, both product- and service-based.[
In this context, Digital Innovation Hubs (DIHs),[
However, to support an easier codification of the DIH support actions, which also directly entails the engagement of customer enterprises in the innovative DIH ecosystems, a method able to codify DIH Customer Journeys (CJ) is needed. Indeed, as usually done in the company context by service design,[
So far, CJ templates tailored for DIHs do not exist. Blueprinting is a general structure to design service provision. However, DIHs need of codified and systematized journeys, which they should refer to in the digitalization support process of SMEs, allowing them to better realize which is their own unique value proposition and how they could better engage their customers. Therefore, this paper proposes the D-BEST-based DIH CJ analysis method, able to configure DIHs' unique value proposition, mapping on the five macro-classes of services of the D-BEST the digital transformation processes of the two main categories of DIH customers, Technology end-Users (TU) and Technology Providers (TP). The research answers to the research question: How to configure DIHs' unique value proposition to bolster their support action towards SMEs throughout the digitalization path? The model is also able to detect the similarities among the different DIHs composing a given network of collaborating DIHs, unveiling attitudes and inclinations of each DIH towards specific macro-classes of services and CJ steps. This analysis open rooms for possible collaborations among the DIHs composing the network, based on the strengths and weaknesses detected for each of them.
The paper is structured as follows. The Research context: the D-BEST reference model for DIH service portfolio configuration and the customer journey method presents the research context, introducing the D-BEST reference model for configuring DIH service portfolios. The Research Methodology shows the research method adopted and The D-BEST based DIH Customer Journeys analysis method provides the results, proposing the D-BEST based DIH CJ analysis method and the analysis deriving by its application to the network of the DIH4CPS project. The Discussion discusses the results obtained and The Final concludes the paper also unveiling its limitations and the research opportunities triggered.
The D-BEST-based DIH CJ analysis method, as the D-BEST reference model on which it is grounded, is the result of the work of multiple projects of the third and fourth wave of I4MS.[
Service portfolio is defined as the identification of the set of services provided by a DIH. These services will be classified in the five macro-classes of services of the D-BEST model and will be allocated along the different steps of the CJ.[
In service design, two different states of a service are considered: the static potential state is reported in the blueprint while the kinetic state is represented by the CJ, that is the actual rendering of the service.[
So far, the CJ approach has been introduced in service ecosystems with the goal of increasing value creation for the customer and value capture for the provider [
For this reason, this paper proposes in the DIH domain a blueprinting model (i.e. the static templates composed by the main phases and blocking points of the digital path) in the CJ analysis method. The phases represent the actual steps that customers experience from the moment they identify a need, until the moment in which they implement the solution for it, collaborating with the DIH and benefitting of its service portfolio.[
The D-BEST based DIH CJ analysis method has been improved and validated in the DIH4CPS project to identify typical digital transformation processes for the two main categories of DIH customers (TU and TP).
This section is aimed at describing how the method has been applied to build the CJs of the DIH composing the DIH4CPS ecosystem. The input of this work is the result coming from the survey previously conducted in the DIH4CPS project, based on the D-BEST reference model [
The D-BEST based DIH CJ analysis method has been developed and refined through an iterative process in the POLIMI DIH. A first iteration to build the two CJs was conducted only by the main representative of the DIH. However, it came out that a second iteration was needed to figure out and provide a better vision of the entire digitalization path done by the customers interacting with the DIH. Thus, it was required to involve in a brainstorming session both the main managerial and operative users (Project Manager, Research Coordinator, Business Developer) of the DIH. The results of the pilot case are in part reported in Sassanelli, Gusmeroli and Terzi (2021).
Once the pilot case was completed, the templates and materials were ready to be shared and provided to the other DIHs belonging to the DIH4CPS network to build their CJs through the D-BEST based DIH CJ analysis method. 11 use cases, reported in Table 1, were conducted:
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Table 1. Cases having a DIH involved in the research.
DIH 1 ASOCIACION DE EMPRESAS TECNOLOGICAS INNOVALIA 2 Innomine digital innovation hub nonprofit kft 3 BIBA - bremer institut für produktion und logistik GmbH 4 PRODUTECH 5 Universitat politècnica de valència (UPV) 6 Luxembourg institute of science and technology (LIST) 7 Digital manufacturing innovation hub wales (DMIW) 8 PSNC/HPC4Poland DIH 9 CCI des vosges 10 Université lumière lyon 2 - ICT4Manufacturing DIH 11 ITI - instituto tecnológico de informática
The application of the method proposed in this paper starts from the result of the survey previously conducted in the DIH4CPS project to configure the service portfolio of the network. After their retrieval, the first activity has been the organization of a workshop with the representatives of each of the DIHs involved in the research to explain them the research objectives and the needed output, and to ask for a complete and detailed overview of the service portfolio. Indeed, the different DIHs service portfolios configurations, previously obtained with the survey, did not actually provided the instances proper of the single DIHs (see Appendix A1). Therefore, it was asked to the DIHs representatives to brainstorm with the main managerial and operative users of their DIHs (Project Manager, Research Coordinator, Business Developer) to specify and detail the results coming from the previous survey (composed by generic instances of the services composing the D-BEST model) into a set of services actually characterizing the single DIHs of the DIH4CPS network. For conducting this activity, a table was provided to each of them (see Appendix A1), presenting the related configuration of the DIH service portfolio previously obtained through the survey. In this table, structured in "Service macro-class", "type", "class of service" and "service instance", it was asked to the DIH representatives to fill the last column, named "DIH service instance", only where the service instance field was marked as provided. Of course, in this step, it was possible for each DIH representative to further brainstorm on the information previously provided during the survey and better define which services are actually provided by their DIH. Then, a second workshop with the same main representatives of the DIHs has been organized to:
- (
1 ) check the output provided by each of them, - (
2 ) explain the main phases of the CJ and the related blocking points (both for TU and TP), - (
3 ) present the functionalities of the Mural platform (App.mural.co), the online collaborative platform chosen to build the CJs, and provide to each of the DIHs representatives the two links to the platform with their dedicated pages where they could find the two templates of the CJ for TU and TP on which they were supposed to work (see Figure 1 and Figure 2 in Section 2), - (
4 ) ask them to allocate, through the Mural platform, the services composing the complete and detailed service portfolios previously defined, in the two templates presenting the 5-steps of the CJs (for TU and TP), also detailing per each step: - (a) the blocking points unlocked through the provision of the services allocated,
- (b) the granularity of the average time (days/weeks/months/years) foreseen (based on their experience) to move from a step to the following one.
- (
5 ) to detect the typical paths of the customers along the CJs through the use of arrays linking the different services. In this step, it will be also defined if any service usually triggers and activates another (or a set of) service(s).
Finally, a last workshop was set up to perform a Question and Answer (Q&A) session about the building of the CJs on the Mural platform and to ask each DIH representative to provide a full description of their CJs in text format, explaining why the single services are important to pursue the five steps of the CJs.
This section has the aim of introducing the D-BEST based DIH CJ analysis method and the results obtained through its application to the DIH4CPS project. In detail, part of the results of the pilot case conducted for the development of this method, the POLIMI DIH, have been presented in.[
The TU CJ is composed of five steps (Observation, Awareness, Experimentation, Experience, and Adoption), chaperoning the manufacturers towards a higher level of digital maturity.[
The identified blocking points for the TU are shown in Table 2. Taking into consideration all of them in each stage, the role of a DIH is to strengthen the offer of services that can help to reduce the distance between the companies that have started and those that come to a successful conclusion of digital transformation. In other words, a DIH must provide alternative solutions to boost the innovation funnel and reduce the Digital Transformation premature abandoning. In Figure 1, on the left the TU CJ template is shown, also reporting the blocking points characterizing each of its five steps. On the right, the set of services that could be composing the DIH service portfolio are reported, split by macro-classes.
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Table 2. Blocking points for TUs.
Phase Blocking point cluster Blocking point Description Observation (digitization requires a change in mentality, but many companies are limited by the lack of initiative to explore new technologies and in some cases to know about its possibilities in their fields) Mind-set "Doing well" syndrome The company feels safe in its comfort zone of a stable business routine when they can meet the market requirements No need for digitalization The company is not able to identify the benefits of technological transformation Risk aversion The company suffers aversion to the potential side effects of change Focus on core business Budget constrains for R&D expenditure A low or no budget is destined for activities that go beyond the core business Order to book priority Main activities receive the attention, but improvement activities are marginated in a secondary importance area Business strategy locked in The company follows only a specific and not flexible long-term strategy Peculiar market features Unknown communication channels The usual channels of communication are those to reach current customers and suppliers, but the existence of other channels to reach different information and customers is often not known Language and Referring to a target market often means not being familiar with the terminology not close to daily business content Awareness (the awareness phase can lead to opposite results than expected and some barriers can be found in it) Capital assets Time consuming The company may be uncertain about participating in a digital transformation supported by an innovative ecosystem because they do not have time to reach information and targeted concepts, which take time Lack of funds The company can be limited by the amount of money required to reach the technological development Limited human resources Employees are already fully engaged in the execution of the activities of the company, they cannot be easily relieved from current activities to devote themselves to the DT activities Lack of skills Staff often lack the digital skills necessary to sustain a real change Access to knowledge Information overwhelming The collection of experiences and information in general can lead to an information overload Information complexity The experiences and information collected can lead to the feeling of not having the required knowledge to implement the technology Knowledge-based information Generic information on new technologies and innovations seems too far removed from the business reality of SMEs Not applicable content The experience of others does not seem to be applicable to the context and needs of SMEs Intimidating content Digitization seems to be so complex which generates a sensation of intimidation for C levels Ecosystem building Poor ecosystem support The creation of a correct working group is not easy, since having the commitment to change is a challenge within and outside the SME boundaries High effort in engaging people The process of engagement can require a high effort Lack of working team partners It seems impossible to tackle the digital transformation only by one person that identified the DT opportunity Challenges not specific enough SMEs usually do not really know what challenges they should face, and therefore the right people to support them Experiment (PoC execution may not be sufficient for SMEs to open the door to the next step of the digital transformation as they struggle to overcome obstacles related with the experimentation of the technology) Capital assets Human capital priority management The limited availability of resources in the daily activities of SMEs, in particular of employees, the commitment of human capital can be reshaped according to the contingent business priorities in case the trial phase lasts longer than expected or returns result that they are below expectations Time consuming activities Lack of time to keep the implementation of DT. Lack of funds Lack of money to keep the implementation of DT. Technological support No "in house" IT skills Fear of not being equipped with the right skills Lack of competencies Fear of not being equipped with the right competences Low understanding Lack of skills to understand problems correctly and find the right remedies Press Too risky/too costly The pressure on a successful digitalization path, the pressure on capital expenditure and the associated potential cost of the inability to make the change work once brought into the SME environment block progress towards the experience phase Unclear expected results The attitude to ask for evidence of return on the investments spent for the experimentation activities Early ROI expectations Tailored/realized customised solutions in an experimental facility blocks the digitalization journey as well, since no results can be sought as long as the solution is tested inside the company Experience (once the solution has been tested in the SME environment, the main challenges to be faced are related to the management change of the organizational aspects of the company) Technological support Lack of skills The involvement of the infrastructure and the environment of the company for technological tests require the strengthening and/or requalification of human forces or the recruitment of newcomer personnel Packed solutions not available/unsuitable kits The new solution must be customized based on its specific characteristics and needs. Commercial solutions are often unsuitable DIH honest broker The lack of technological knowledge inside SMEs requests an honest broker, or DIH and ecosystem of innovation. Therefore, relationships of trust can scare and block the SME in its path towards the implementation of new digital solutions Organizational changes Lack of internal engagement Lack of involvement of the people and units most involved in the changes End-user customers blocking Lack of acceptance from end-customers Change readiness The company is not ready to embrace the innovations No innovation manager Lack of a head figure that leads the innovation Data management complexity The DT involves differences in the way the organization and operations are performed, including increased availability of data, whose collection, analysis and use might not be clear Loss of enthusiasm The lack of evidence can undermine the completion of the transformation KPIs evidence Impact/effort evaluation The SMEs must be supported in assessing the quality of the investment to be made Definition of new KPIs Identifying the performance metrics that can capture the impact not only at the process level, but at strategic level and market level Adoption (the final step is the decision to invest in the massive deployment of technology, to move from a pilot test involving a defined area of the company or the entire company (up to the value chain) Technological support Support in system integration Lack of knowledge to carry out the integration of the new solution within the current systems and operations of the company Lack of documentation for massive technological development Lack of a kit and guide to the final technical deployment Maintenance Loneliness after the end of the project Loneliness after the end of the digitization project prevents the SME from completing this path Lack of competencies in the continuous improvement Technological change must allow the company to return to perform in a sustainable way and the lack of skills for continuous improvement and adaptation, together with the lack of a reference figure as the innovation project manager oppose the completion of the technological adoption
Graph: Figure 1.TU Customer Journey Template (developed on MURAL platform).
For TPs, the CJ is a skill-demanding process model going through five main phases leading to the final product market launch (Ideation, Design and Engineering, MVP, Verification and Validation, Go to Market).
During Ideation the business idea is conceived (flanked by preliminary architecture of the solution to be implemented and by the key technical milestones and (functional and non-functional) requirements to be addressed in the following stages), through a creative process (through methods as Brainstorming, Creative thinking, Creative matrix, Wall of idea, etc.). Services offered to TP are workshops/webinars on design thinking, SWOT analysis, idea market positioning, hackathons. Once consolidated the business idea, the Design and Engineering phase starts with the design phase and the specifications for its technical development. Tools that could be useful in this phase are: Technical pills, Dockers, Kubernetes, visual analytics, UX, UI, an assessment about how to validate the solution or customer discovery (validation of the idea to see if the idea has a market). In this phase there could be some deviations from the original business idea since current software components cannot meet the requirements or new functionalities can be added without cost increases. A comprehensive Market Requirements Document (MRD) needs to be prepared in this phase (to articulate the new product plan including customers, buyers, goals, use-cases, requirements, and specification sizing), leading to a more streamlined Minimum Viable Product (MVP) definition, useful for the company to validate products value and growth hypotheses as fast as possible. MVP needs to be experimented to be confirmed or refuted. Tools such as FIWARE Lab, credit from Google/Amazon cloud, 3D Printers, sensors, etc., can be provided in this stage as well as any service to find economical support for subcontracting to realize the final MVP and elaborating the business part programme. Verification and validation are essential parts of the product development process.[
The blocking points that TP might have to face when going through a technology innovation journey are reported in Table 3. Instead, in Figure 2, on the left the TP CJ template is shown, also reporting the blocking points characterizing each of its five steps. On the right, the set of services that could be composing the DIH service portfolio are reported, split by macro-classes.In the following section, it is presented how the D-BEST reference model has been used to structure the method to analyse DIHs CJs. Indeed, in this task, the D-BEST services composing the DIHs' service portfolios are combined towards the implementation of the DIHs' Unique Value Propositions, building and defining flexible service workflows for DIH customers.
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Table 3. Blocking points for TPs.
Phase Blocking point cluster Blocking point Description Ideation (differences of opinion persist amongst economists and policymakers about the exact role of intellectual property (IP) in relation to innovation) Access Intellectual property (IP) protection The company can be inclined to use trade secrets rather than patents as a form of protecting their inventions to stay competitive. The main reasons given by SMEs for shying away from patenting their inventions include high costs and complexity of the patent system Limited access to end-users IP can lead to a limited access to end users at validation time, meaning that validation is only done locally Local validation only Due to IP limitations, the company is able only validate the technology in local environments Lack of awareness from customers Customers cannot give a feedback as the company is unable to reach them No interoperability and lack of knowledge of existing standards Lack of globalized platforms that generate a lack of interoperability Hardware Hardware validation Company not able to validate the hardware to be utilized in the development Problem in finding the right hardware Lack of knowledge of the right hardware to be utilized for a certain development Design & engineering (it is often too tough to manage all the differing ideas between designers and product components, functionalities, etc. Managers should agree on an appropriate ambition level for innovation and find common language to describe it.) Development team management Differing ideas Fail to agree on an appropriate ambition level for the innovation idea and fail to find a common language to describe it Who is going to design the idea The overall return on innovation investments depends on how managers analyse opportunities. The imperative is to identify and accelerate the most promising ideas and kill off the rest (some of which may be perfectly viable but don't represent the best use of resources) Customer & product Identification of customers' needs Difficulties in identifying the customer needs Find products' validation partners Some partners are not able to validate the design Minimum viable product (MVP) (MVP price may vary greatly, the cost roughly depends on the number of features in the product and its complexity. The trick is to identify what is actually required and what is only desirable. Besides, it also needs to be taken in account that MVP is core feature of an iterative testing process.) Cost & effort High testing costs The costs of testing the MVP are too high Market analysis requires effort To reduce the high testing costs, is possible to do a market analysis, although it requires effort and knowledge Customer awareness Customers see MVPs as final products Often there is confusion between the final product and the MVP. Verification & validation (it is vital to determine the right target audience for usability testing and gain maximum value from it. To decide who to involve in the focus group research, the key parameter is experience with similar technologies. There are three groups of target users: i) Experienced users, ii) users with similar experience, iii) inexperienced users) Testing method Find the right target group to test Fail to identify the focus group to perform the validation process Feedback Find good method to collect feedback The right methodology to collect feedback must be identified to successfully gather relevant information. In case of failing to do so, the feedback will not be valuable Ego The ego can block the possibility to further improve the product or service Go to market (GTM) (A GTM strategy is an action plan that specifies how a company will reach target customers and achieve competitive advantage. The purpose of a GTM strategy is to provide a blueprint for delivering a product/service to the end customer, taking in account such factors as pricing and distribution Final product Way from prototype to product Difficulties in defining the GTM strategy that defines the path from prototype to product Difficulties in entering the market Challenging entry to the market due to competition or definition of the right GTM strategy Business method Expensive sales network High costs in the sales network can turn back the GTM strategy Business perspective difficulties for technology developers Difficulties on considering the business perspective if the company is focused on the technological perspective Find the right partners It can be challenging to identify the right partners that can participate in the release of the product to the market. Managing the loop and distribute budget (ROI measurement) Challenges in the moment to identify how the budget should be distributed through the process
Graph: Figure 2.TP Customer Journey Template (developed on MURAL platform) (E:Ecosystem, T:Technology, B: Business, S: Skills, D: Data).
Based on the multiple analysis of the 12 DIHs composing the DIH4CPS network, this section is aimed to understand the nature of the single DIHs and to unveil both the common features and the degree of complementarity among them. Indeed, future collaborations among the DIHs are raised up, envisaged, and suggested. Through these collaborations, daily operations of the single DIHs in supporting the SMEs towards digital technologies adoption can be eased. Indeed, joint provision, development or matchmaking of a needed service among different DIHs, characterized by different inclinations towards specific macro-classes of services of the D-BEST model, can enhance the effectiveness of DIHs in the digitalization CJs.
First of all, looking at the service portfolio overviews provided in Appendix (A2 and A3), it can be confirmed that the classical ETB services represent most of the services provided by the DIHs. This can be observed in Table 4, where around the 28% of the services belong to Ecosystem, 19% to Business and 29% to Technology. However, Skills and Data services are not to be neglected and play a strategic role in the typical paths of the customers.
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Table 4. Updated service portfolio overviews of the DIHs composing the DIH4CPS network (E:Ecosystem, T:Technology, B:Business, S:Skills, D:Data).
Service portfolio E B S T D Polimi 8 3 3 2 3 Innovalia 9 5 6 11 7 Innomine 14 12 5 8 0 BIBA 9 8 5 15 9 PRODUTECH 23 13 8 20 5 UPV 10 7 8 14 5 LIST 14 7 4 17 9 DMIW 22 11 5 17 5 PSNC 3 2 2 3 1 CCI 17 12 8 17 9 LYON2 13 12 8 17 11 ITI 17 12 8 17 9 Total 159 104 70 158 73 Total % 27,5% 18,6% 12,8% 28,8% 12,3%
It is interesting to look at how these services are distributed (or delivered) along the different steps that compose the digitalisation journeys of the two customers, TU and TP. As shown in the Figure 3 and Figure 4 (respectively per TU and TP), the number and types of services employed in the CJs are different per each step and reflect how the DIHs support their customers along the digital transition. For instance, in the TU CJ, it is worth mentioning the relevance that Ecosystem services have for the first step and the second step. In the third and fourth step, the most predominant services are Technology and Data services, while Business services have relevance in every step after the first one. Regarding the TP CJ, the Business services are delivered in almost all the steps, while Ecosystem services mostly in the first and last steps. It is also notable that DIHs support TU and TP in a different way, since they employ different sets of services to support the digital transition of these two kinds of customers. However, Steps 1 and 3 of the two journeys unveil some similarities in terms of types and distribution of services provided.
Graph: Figure 3.Total number of services utilized by DIHs in each step of the TU CJ (E:Ecosystem, T:Technology, B: Business, S: Skills, D: Data).
Graph: Figure 4.Total number of services utilized by DIHs in each step of the TP CJ (E:Ecosystem, T:Technology, B: Business, S:Skills, D: Data).
In the following, first in sub-Technology Users CJ, the TU CJs of the 11 DIHs are analysed, considering the services offered, blocking points solved and the timeline in which the whole CJ takes place. In Technology Providers CJ, the same analysis is presented for the TP CJs and in TU and TP CJ: a comparison TP and TU CJs are compared. For both TP and TU analyses, the details (e.g. percentages of DIHs addressing blocking points in each step) have not been reported for space limitations. Later, in DIH groups, an overall perspective of the DIH4CPS network is taken. Here, the CJs are analyzed to understand which are the different ways in which DIHs use to offer their services and to group in relevant clusters the DIHs acting in a similar way with a specific type of customer. The clustering can help to detect possible overlaps, synergies and complementarity conditions between the DIHs composing the DIH4CPS network.
Looking at Table 5, starting from TUs' journeys, it is evident the relevant role that Ecosystem and Business services have in Step 1 (Observation). In addition, sometimes also some Skills and Technology services are provided to trigger the provision of new services in the following steps. Finally, it is important to note that Data services are usually not requested at this initial stage (although Ecosystem services might include data-related activities: e.g. data sharing awareness events, data exploitation webinars, etc.). This phase is the longest one: it usually takes some time to convince the customer to start the digitalisation journey. Indeed, a high percentage of DIHs center their efforts on the blocking points that cope with Mind-set type and Focus on core business (in particular Budget constrains for R&D expenditure).
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Table 5. Number of services and time lapse per each step of TU CJ (E:Ecosystem, T:Technology, B:Business, S: Skill, D: Data).
Step 1 - Observation Step 2 - Awareness Step 3 Experiment Step 4 Experience Step 5 Adoption E B S T D E B S T D E B S T D E B S T D E B S T D Total 52 12 8 9 0 39 31 26 19 4 15 20 13 40 23 14 13 12 30 23 10 21 47 5 Total % respect to the total of the macroclass 35% 12% 12% 6% 0% 26% 31% 38% 12% 6% 10% 20% 19% 26% 35% 9% 13% 17% 19% 35% 7% 21% 6% 5% 8% CJ Step 1-Observation 2-Awarenes 3-Experiment 4-Experience 5-Adoption Time horizon D W M Y D W M Y D W M Y D W M Y D W M Y Total 1 5 7 3 0 8 8 0 0 5 9 0 0 6 9 1 1 4 10 3
Switching to Step 2, Ecosystem services leave some space to the provision of more business skills and technology services. In this specific phase, Skills services are very important and few data services (i.e. collaborative decision support systems and data analytics) begin to be provided to enhance the awareness of specific technologies. The time lapse starts to decrease (being in the order of magnitude of weeks/months). Finally, the blocking points unlocked are mainly of the types of Capital assets and Access to knowledge. Dealing with Step 3 (Experiment), Technology and Data services are the most dominant. The time lapse is still of the order of weeks/months. Finally, the blocking points unlocked are mainly of the Technological support type, but also Capital assets and pressure on results are important. In Step 4 (Experience), still Technology and Data services are the most delivered ones but also skills (under the shape of training activities), business (consortia development) and ecosystem services are supporting this phase. The time lapse is always of the order of weeks/months (with one exception of year), and the blocking points unlocked are mainly of the Organizational changes type. In the last step, Step 5 (Adoption), Business services are very important to support the adoption of the technology. All the other services are also delivered in this phase but with a lower impact. The time lapse in this phase gets longer (becomes of the order of months/years). The blocking points unlocked in this phase are more of the Maintenance type (lack of skills and competences to carry out and continue to deliver the given solution), than Technological support.
Finally, at the bottom of Table 6, it is shown how many services, composing the portfolios of the DIHs belonging to the DIH4CPS network, are not actually occurring in the TU CJs. Among them, the majority is belonging to the Business and Technology macro-classes.
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Table 6. Number of services and time lapse per each step of TP CJ (E:Ecosystem, T:Technology, B:Business, S: Skill, D: Data).
Step 1 - Ideation Step 2 - Design & Engineering Step 3 - MVP Step 4 - Verification & Validation Step 5 - Go to Market 68 35 11 18 3 30 34 8 37 11 10 22 5 48 22 18 26 7 26 11 31 27 5 19 4 Total E B S T D E B S T D E B S T D E B S T D E B S T D Total % respect to the total of the macroclass 43% 34% 16% 11% 4% 19% 33% 11% 23% 15% 6% 21% 7% 30% 30% 11% 25% 10% 16% 15% 19% 26% 7% 12% 5% CJ Step 1-Ideation 2-Design & Engineering 3-MVP 4-Verification & Validation 5-Go to Market Time horizon D W M Y D W M Y D W M Y D W M Y D W M Y Total 1 5 7 3 0 8 8 0 0 5 9 0 0 6 9 1 1 4 10 3
Also for TPs, the most relevant services for the first step of the CJ (Ideation) are the Ecosystem and Business services, which are strategic (Table 7). Indeed, they represent most of the actions done by DIHs to support TPs. Sometimes also Skills (roadmaps definition based on the maturity of the company) and Technology (support in the conceptualization of solutions) services are provided in this phase. This phase usually lasts weeks or months and is functional to unlock mainly Limited access to end-users and Lack of awareness from customers.
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Table 7. TU CJ analysis: services utilized, time-lapse, blocking points.
Characteristic Step 1: Observation Step 2: Awareness Step 3: Experiment Step 4: Experience Step 5: Adoption Services utilized Ecosystem services Business, skills and technology services Technology and data services Mostly technology and data services, but also skills All services with emphasis on business Time Longest phase (months/years) Reduced time lapse (weeks/months) Maintained time lapse (weeks/months) Maintained time lapse (weeks/months) Increased time lapse (months/years) Blocking points (BP) addressed Mind-set and focus on core business (budget constrains for R&D expenditure) Capital assets and access to knowledge Mainly technological support type but also capital assets and pressure on results Mainly organizational changes Maintenance BP addressed (lack of skills and competences to carry out and continue to deliver the given solution) Services of the DIH portfolios not occurring in the TU CJs: The majority is belonging to the business and technology macro-classes
In Step 2 (Design & Engineering), there is a good balance between Ecosystem, Business and Technology services. From this phase onward, Skills services are less important since the path is aimed at the development of a technological solution. Also data services begin to be provided, paired with Technology services. The time lapse ranges from weeks to months and the most unlocked blocking points are more in the Customer and products domain (Identification of customer' needs and Find partners to validate the product) than Team Management. Concerning Step 3 (MVP), similarly to the TU CJ, Technology and Data services are dominant. The time lapse is still of the order of weeks/months. Finally, the main blocking points unlocked are the High testing costs. In Step 4 (Verification and Validation) Business and Technology services, flanked also by Data and Ecosystem ones, are the most delivered. Few cases of Skills empowerment services are also occurring in this phase. The time lapse is always of the order of weeks/months (with one exception of year) and the main blocking points unlocked is to Find the right target group for testing. In the last step, Step 5 (Go to Market), Business services are very important to support the adoption of the technology. All the other services are also delivered in this phase but with a lower impact. The time lapse in this phase gets longer (becomes of the order of months/years). The blocking points unlocked in this phase are more of the Business method type (Business process for tech people is hard to understand and find the right partners) than Final Product (Way from prototype to product).
Finally, another analysis has been conducted on how many services of the portfolios of the DIHs of DIH4CPS network are not actually occurring in the TU CJs (see bottom of Table 8). Among them, the majority is belonging to the Skills and Data (respectively 63% and 41%), but also Ecosystem and Business macro-classes are often not provided in these CJs.
Graph
Table 8. TP CJ analysis: services utilized, time-lapse, blocking points.
Step 1: Ideation Step 2: Design & engineering Step 3: MVP Step 4: Verification & validation Step 5: Go to market Services utilized Ecosystem and business services. In some cases, skills (roadmap definition) and technology (conceptualization) Balance between ecosystem, business and technology supported by data services (skills services lose importance) Technology and data services Business and technology services supported by data and ecosystem. In some cases skills services are utilized Business services are mainly delivered. All the other services are also delivered in this phase but with a lower impact Time Shorter than TU (weeks/months) Maintained time lapse (weeks/months) Maintained time lapse (weeks/months) Maintained time lapse (weeks/months) Increased time lapse (months/years) Blocking points (BP) addressed Limited access to end-users and lack of awareness from customers Customer and products domain (identification of customer' needs and find partners to validate the product) The BP of high testing costs is mainly addressed in this step The BP of finding the right target group for testing is addressed The main BP adressed are business method type (business process for tech people is hard to understand and find the right partners) than final product (way from prototype to product) Services of the DIH portfolios not occurring in the TU CJs: The majority is belonging to the skills (63%) and data (41%), but also ecosystem and business macro-classes are often not provided in these CJs
A comparison between the characteristics of TU's and TP's CJs has also been performed. Looking at Tables 6 and 8, it emerged that the number and types of services employed in the CJs are different per each step and reflect how the DIHs support each type of customer along the digital transition. In the case of TU's CJ, Ecosystem services are relevant for the first step and the second step. In the third and fourth step, the most predominant services are Technology and Data services, while Business services have relevance in every step after the first one. Instead, in the TP's CJ, the Business services are delivered in almost all the steps, while Ecosystem services mostly in the first and fifth steps. Thus, DIHs support TU and TP employing different sets of services for their digital transition. However, Step 1 (driven by Ecosystem services aimed at enlarging and empowering the network or new stakeholders procurement) and Step 3 (where the major effort is focused on the technological and data support of digital solutions) of the two journeys unveil some similarities in terms of types and distribution of services.
Graph: Figure 5.Total services offered by each DIH clustered in D-BEST model for TU CJ (E: Ecosystem, T: Technology, B: Business, S: Skill, D: Data).
Graph: Figure 6.Clusters of DIHs strengths based on TU CJ analysis (E:Ecosystem, T: Technology, B: Business, S:Skills, D:Data).
Graph: Figure 7.Total services offered by each DIH clustered in D-BEST model for TP CJ (E: Ecosystem, T: Technology, B: Business, S: Skill, D: Data).
Graph: Figure 8.Clusters of DIHs strengths based on TP CJ analysis (E:Ecosystem, T: Technology, B: Business, S:Skills, D:Data).
With the intention to better understand the CJ dynamics for both TP and TU cases, the total number of services offered by each DIH clustered with the D-BEST model were plotted (Figure 5). This, in combination with the CJ from each DIH, were utilized to identify similarities between the paths of the DIHs' customers.
Through the analysis of the TU CJ for Innovalia, ITI and PRODUTECH, it is possible to identify that these DIHs can support their customers in the whole CJ path, offering an equilibrated set of services in each cluster of the D-BEST model. From the CJ results, it is also possible to identify that the connection between the services through the CJ path follows a consistent flow, which represents a clear gain for the customer as it will boost the speed of development and adoption. On the other hand, INNOMINE and Polimi lack a defined path that connects the services offered to the customer in the last two steps of the TU CJ. It is clear from the results, that these DIHs are not technology- and data-driven, and that they focus their efforts on ecosystem, business, and skills support for their TU customers. Considering this, it can be identified that INNOMINE and Polimi can offer a high level of support to their customers in the first three steps of the CJ, but that could be supported in the last steps by some other DIHs in the ecosystem that have a stronger technological and data driven perspective. Is similar the case of Lyon2, but in it, even with a small number of services offered in the step 4, the DIH can offer a complete CJ path until this step. Nevertheless, it can also be supported in the last step of the CJ as it only has one data service that do not supports completely the customer in the adoption step. It was identified that BIBA is a DIH centred mainly in ecosystem, business, and technology services. Nevertheless, it offers complementary services in data and skills areas that represent an advantage for the customer as they complement the set of services that represent the strength of the DIH. DMIW shares a similar behaviour to BIBA, but it can be identified that it lacks a complete set of services in the data cluster of the D-BEST model. This means that the DIH can have some issues in the middle and last phases of the CJ due to the limited data services that can be offered. However, this opens the possibility to collaborate with DIHs that are strongly data driven such as ITI, LIST, UPV or PRODUTECH. In the same way, CCI can also take the same approach as it does not count with a set of data services. Similar to BIBA, UPV shows a strong defined path that customers can follow to complete the adoption of a technology. Nevertheless, UPV does not support their customers from a business perspective through the CJ path. For this reason, some DIHs such as POLIMI that are part of the network and are known for their strength in business, have the possibility to collaborate with UPV to create better service for their customers as this cluster of services is vital for the second and last phase of the CJ. LIST is an exceptional case with a CJ centred mainly in technology and data services. The lack of services from the ecosystem, business and skills clusters can represent a weakness in the first, second and fifth steps of the CJ path. For this reason, strong DIHs in the previously mentioned clusters such as CCI, POLIMI or PRODUTECH can support LIST to offer a consistent and smooth CJ.
Taking into consideration the previous analysis (wrapped-up in Table 9), some groups of DIHs (Figure 6) were identified for the TU CJ analysis:
Graph
Table 9. TU CJ: Strengths, weaknesses and collaboration opportunities among the groups of DIHs.
Group DIH Strengths Weaknesses Could be supported by 1 (solid CJ with seamless connections) Innovalia Solid set of services almost evenly distributed in the D-BEST model clusters and in the different steps of the CJ. This permits a continuous flow of activities of the customer through the CJ path — — CCI PRODUTECH PSNC Exceptional case: simple and limited service portfolio that offers a seamless flow even with limited services offered — — UPV Technology driven DIH Implementation of additional ecosystem and business services could complement the services already offered in each step of the CJ. PRODUTECH, CCI, BIBA, ITI 2 (solid CJ in the first 2 steps) Polimi Consistent but not complete CJ path Further implement services in the last steps of the customer journey with the intention to offer a better guidance for their customers PRODUTECH, ITI Lyon2 Strong ecosystem and business support for the first two steps of the CJ Lack of skills services. This represents a challenge for customers in the last two steps of the CJ and in the development of skills related with the technology CCI, PRODUTECH 3 (solid CJ but lack of skill services) BIBA Well founded CJs Lack of skill services. Which can create a challenge in the whole CJ path as skills are vital to adopt in a faster pace new technologies Inclusion of services that the DIH has in its portfolio but is not currently offering ITI Collaboration with CCI could create a better support for their customers through the CJ 4 (solid CJ but lack of skill and data services) Innomine, LIST, DMIW Strong CJ paths but limitations in terms of data and skills services Lack of skills and data centred services Possibility to collaborate with data-driven DIHs such as ITI or BIBA.
The same analysis was performed for the TP CJs. Initially, a plot of the services offered by each DIHs classified in the D-BEST clusters was made (Figure 7).
Following the same methodology previously made for the TU CJs, the analysis was made by closely examining the path that the TP customer follows in the CJ defined by each DIH. Starting from CCI, Innovalia and PRODUTECH, these CJ paths show a solid set of services almost evenly distributed in the D-BEST model clusters and in the different steps of the CJ. This permits a continuous flow of activities of the customer through the CJ path. In a similar way, BIBA and ITI have well founded CJs but with lack of skills services. In the case of BIBA, even with only one type of skill service offered, it has the capacity to support the customer through the whole path of the CJ. The implementation of additional skill services that this DIH has available but that is not currently offering could further support the process. Nevertheless, the single skill service offered also permits a seamless flow through the CJ path. In the case of ITI, a further collaboration with other partner of the network could create a better support for their customers through the CJ, as it could represent a challenge in the first three steps of the same. On the other hand, DMIW, Innomine and LIST show a lack of skills and data centred services. This can create a flaw in the third step of the CJ due to the lack of the pivot that data services represent in the adoption of new technologies. Data services act as catalysers for decision making processes and validations. As data, skills services are also vital for the third and the next steps of the CJ. These services support the customer to understand and design the new technology (first three steps of the CJ). This lack of services can also open the possibility to collaborate with data-driven DIHs such as ITI or BIBA. As in the TU CJ, UPV is also a technology-driven DIH in the TP case. In this case, the DIH can successfully guide their customers through the whole CJ path. Nevertheless, further collaboration or implementation of additional ecosystem and business services could be considered to complement the services already offered in each step of the CJ. PSNC is an exceptional case that shows a simple and limited service portfolio that offers a seamless flow through the CJ. With a low number of services of each cluster offered in each path, it complements the process to create a clear path for the customer. Lastly, it was found that POLIMI could further implement services in the last steps of the customer journey with the intention to offer a better guidance for their customers. The collaboration with other partners such as PRODUTECH or ITI could be one of the best alternatives to an internal development of services. In a similar way, Lyon2 is a data driven DIH, with null services offered in the skills cluster, nevertheless a strong ecosystem and business support for the first two steps of the CJ. This represents a challenge for customers in the last two steps of the CJ and in the development of skills related with the technology, but in the same way it represents a challenge, it also opens a new opportunity of collaboration with DIHs with strong services offering in the last two steps of the CJ and with strong skills services offer such as CCI and PRODUTECH.
One of the major conclusions of the previous analysis is that there is a lack of DIHs centred in skill services. Most of the DIHs offer a limited amount of them, which opens a new field of exploration for an improvement of the network. Taking into consideration the previous analysis (wrapped-up in Table 10), some groups of DIHs (Figure 8) were identified based on the TP CJ analysis:
Graph
Table 10. TP CJ: Strengths, weaknesses and collaboration opportunities among the groups of DIHs.
Group DIH Strengths Weaknesses Could be supported by 1 (solid CJ and solid flows) Innovalia Support their customers in the whole CJ path and through all the types of services. The connection between the services through the CJ path follows a consistent flow, which represents a clear gain for the customer as it will boost the speed of development and adoption — ITI PRODUTECH 2 (solid last 3 steps) LIST Centred mainly in technology and data services Lack of services from ecosystem, business and skills that could be a disadvantage in the first and fifth step of the CJ CCI, POLIMI or PRODUTECH UPV Strong defined path in the whole CJ Lacks of services from business dimension that could be a disadvantage for its customers PRODUTECH or polimi 3 (solid CJ except last phase) BIBA Centred mainly in ecosystem, business, and technology services but complements CJ with additional skills and data services Challenges in connecting the first steps of the CJ with the last one Could be supported by PRODUTECH, ITI and innovalia DMIW High level of support for the customer in every step except adoption Lack of data services that affects the middle and end phases of the CJ Could complement themselves with LIST or UPV 4 (solid 2-3 steps) Polimi High level of support in the first 3 steps with ecosystem, business and skills services Lack of a defined path that connects the services offered to the customer in the last two steps (total lack of data services for innomine case) Could complement themselves with LIST or UPV with data and technology services Innomine CCI Solid CJ path Lack of data services that could affect middle and end phases of the CJ Could complement themselves with LIST or UPV Lyon2 High level of support in the first 4 steps of the CJ Last step of the CJ Innovalia, ITI, PRODUTECH
The results obtained through this research confirm that different DIHs play different roles in supporting European companies along the digital transformation journey, addressing a specific combination of the four typical categories of functionalities characterizing this kind of innovation ecosystems according to the EC.
This difference in DIHs' behaviour can depend either on: a) their nature (i.e. their public or private organization and structure), leading them to ensure a fit with their current service and capabilities portfolio; or b) by choice or needs, to address the expectations of their stakeholders. The heterogeneity of such ecosystems fits with the main aim of the EC in fostering their development, attempting to expand the already existing network and to create an integrated platform for DIHs from different, especially digitally underdeveloped, sectors and regions. The envisioned result by the EC would be an extended pan-European ecosystem of DIHs. Each of them would have a different nature, would be located in different regions and would be focusing on diverse industries and digital technologies. The resulting pan-European DIH ecosystem would be able to activate innovation-driven collaboration and cooperation dynamics through the joint development, provision, and matchmaking of services among its partners. The successful achievement of such a result would avoid single DIHs to strive to concurrently fill all the four functionalities and focus more on the most characterizing one/s. Indeed, "DIHs' inner characteristics (e.g. founding members' profiles, mission, staff, technological specialization and credibility among local recipients) and the types of knowledge (e.g. technical, relational or territorial) that are shared, help to determine the sets of products, service providers, technologies and know-how to which SMEs have access".[
In this paper, the D-BEST based DIH CJ Analysis method has been proposed and tested in the DIH4CPS project.[
sj-pdf-1-enb-10.1177_18479790221124634.pdf
Supplemental Material - The D-BEST Based digital innovation hub customer journey analysis method: Configuring DIHs unique value proposition
Supplemental Material for The D-BEST Based digital innovation hub customer journey analysis method: Configuring DIHs unique value proposition by Claudio Sassanelli and Sergio Terzi in International Journal of Engineering Business Management
Claudio Sassanelli https://orcid.org/0000-0003-3603-9735
By Claudio Sassanelli and Sergio Terzi
Reported by Author; Author
