Intelligent and Sustainable Manufacturing-logo
Intelligent and Sustainable Manufacturing

Maturity Model for the Manufacturing Industry with Case Experiences

Intelligent and Sustainable Manufacturing-logo
Intelligent and Sustainable Manufacturing
Submit to Download PDF
Cite This Article
Contents

Article Open Access

Maturity Model for the Manufacturing Industry with Case Experiences

Leila M. Saari 1,* Jukka Kääriäinen 1 Markus Ylikerälä 2
Author Information
1
VTT Technical Research Centre of Finland Ltd., 90570 Oulu, Finland
2
VTT Technical Research Centre of Finland Ltd., 02044 Espoo, Finland
*
Authors to whom correspondence should be addressed.
Views:562
Downloads:65
Intelligent and Sustainable Manufacturing 2024, 1 (2), 10010;  https://doi.org/10.35534/ism.2024.10010

Received: 28 May 2024 Accepted: 14 August 2024 Published: 19 August 2024

Creative Commons

© 2024 The authors. This is an open access article under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

ABSTRACT: This manuscript describes the research path when extending a maturity model. The initial model—ManuMaturity—was for manufacturing companies aiming beyond Industry 4.0. The extended OSME model covers data sharing within a supply chain, an open innovation ecosystem and sustainable manufacturing. The OSME maturity model has five maturity levels: traditional factory, modern factory, agile factory, agile cognitive factory and agile cognitive industry and seven dimensions (such as infrastructure, data, customer, business model, employee, sustainability and processes). The tool was experimented with in manufacturing companies on two occasions: with a set of manufacturing companies and a group of companies. In both cases, feedback was gathered from the respondents. The article follows the maturity assessment development phases such as scope, design, populate, test, deploy and maintain, and reports the software implementation of the maturity tool. With the help of the developed maturity model and the tool, it was possible to make assessments in case companies, where the tool and its results were commented mostly positively. The tool can be applied in various ways. For example, a group of people can jointly submit their common understanding and have a thorough discussion or a group of company representatives submit their responses and the variation is discussed afterwards.
Keywords: Manufacturing industry; Maturity model; Sustainable manufacturing; Data sharing; Ecosystem; Supply chain

1. Introduction

In addition to potential new business possibilities, digital transformation (DT) also generates challenges for manufacturing companies. Aside from manufacturing skills, these companies must learn new capabilities while integrating physical objects, human actors, intelligent machines, production lines and processes across the organisation [1] and simultaneously considering product servitisation, business model innovations and supply chains [2]. Even without the challenges of the external world and the phenomenon that appears at an accelerating pace, the transformation is challenging. Manufacturing SMEs struggle with resource constraints and knowledge gaps that slow down their digitalisation efforts and investments. The main challenges and barriers to overcome are limited understanding, insufficient resources and gaps in deploying digital solutions in practice [3,4]. Although sustainability has three pillars (economic, environmental and social), the majority of the activities considered in the manufacturing industry still pay attention purely to the economic goals [5]. The ongoing climate change has put the environmental aspects and circular economy (CE) in focus. Companies are pursuing a CE by implementing new R-cycles such as reuse, repair and remanufacture, aiming for even zero defects [6]. Companies have also discovered that collaboration is a new way of working. To enable this, data management rules and platforms for securing private data and sharing federated data with trusted partners are required [7,8,9]. Data sharing between business-to-business partners is a key enabler for sustainable manufacturing [10]. The twin transition combines digitalisation and green transition and creates a green, digital and resilient economy as digital technologies enable the circular strategies of the manufacturing industry [11]. The positive impacts on the sustainability of manufacturing can be achieved by (i) increasing data sharing and the use of artificial intelligence (AI), (ii) using digital products and services, instead of physical products, as much as possible (digital twins, simulations, etc.), (iii) exploiting information and communication technology (ICT) solutions for energy and material consumption optimisation, and (iv) developing (and applying) sustainability tools and methods [5,12]. The key elements of sustainable manufacturing are non-polluting processes, closed loops for energy and resources, and safety for employees, communities, and consumers on top of the profitability goals [13,14]. Occupational safety and human well-being bring in the concept of Operator 4.0 [15]. In recent years, several maturity models (MM) have been published by both consultancies and academies for various topics, such as digitalisation and Industry 4.0. Even the EU created a digital maturity assessment tool to evaluate the progress of digitalisation before and after interaction with European Digital Innovation Hubs [16]. Manufacturing companies need three skills to proceed with DT: awareness, informed decision-making and rapid implementation [17]. A good MM (with a tool) can support building awareness. Our ManuMaturity tool [18] was developed to accelerate the Finnish manufacturing industry towards Industry 4.0 [2] or even beyond [19] in 2019. In 2020, Industry 4.0 was not the major goal for the manufacturing industry. It must consider openness and data sharing within the supply chain, employees and sustainability. This manuscript reports on how the tool was extended and experimented together with a Finnish manufacturing ecosystem. The extended MM tool was nominated as the OSME maturity tool, according to the project (Open Smart Manufacturing Ecosystem 1) where the extension took place.

2. Related Research

2.1. Maturity Models MMs have a long history, with academies, alliances, and consultancies regularly publishing their work. These institutions have also developed and published many models that have been applied to various topics [20]. For DT only, dozens of MMs are available from both practitioners and academies [21,22]. There are also MMs for business processes [23,24] and information security [25]. MMs dedicated to manufacturing companies or Industry 4.0 [2] and beyond [19] are discussed in the next chapter. 2.2. Maturity Models for Manufacturing Companies Wendler systematically studied the maturity of MM research with more than 200 articles and found that 46% of the articles focused on model development, 35% presented a model application, and only 14% focused on model validation. Thus, there seems to be a gap in evaluating and validating the developed MMs [26]. According to another study, when 15 published MMs of the smart manufacturing or journey towards Industry 4.0 were analysed, three research gaps were identified: (i) differences in the starting conditions between SMEs and original equipment manufacturers (OEMs) regarding Industry 4.0 or smart manufacturing, (ii) disconnection between the MM and the self-assessment tools and (iii) support (especially tailored for SMEs) to propose the next steps after maturity assessment is missing [27]. Another systematic literature review on MMs against Industry 4.0 elements (human-centred design, resiliency and sustainability) also evaluated dimensions: technology, employees, data, organisation and processes, strategy and management, products and services, corporate environment, customer, and corporate culture [28]. In another study, 15 dimensions—organisational strategy, smart factory, smart operation, smart product, vertical integration, horizontal integration, employees, leadership, customers, culture, governance, technology, data-driven governance, IT infrastructure and information systems—were identified from 16 MMs [29]. Sustainability is an emerging topic, and the manufacturing industry shall proceed with the twin transition, i.e., enhance their environmental and economic sustainability via implementing new digital solutions and R-cycles, such as reuse, repair and recycle [30]. An MM for CE proposes maturity levels, such as (i) linearity, (ii) industrial CE piloting, (iii) systemic materials management, (iv) CE thinking and (v) circularity, and maps them to the manufacturing value chain [31,32]. A systematic literature review [33] explored 16 CE MM, among which only one focused on manufacturing companies [34], another was dedicated to remanufacturing [35], and a third was on the material flow in the supply chain. In addition to CE MMs, several tools are available. For example, the Ellen MacArthur Foundation introduced a CE measurement tool, Circulytics, in 2020. It supports a company’s transition towards the CE, regardless of industry, complexity and size [36]. Further, innovation management needs to be considered. Companies are gradually ready to work together when they realise that the industry’s challenges require a wide range of skills and technologies that a small company alone cannot provide. Collaboration and co-creation are easier to start with trusted partners who have already worked with a project, community, or ecosystem. There are also MMs for ecosystems, innovation ecosystems [37,38], innovation ecosystem strategies [39], software ecosystems [40] and software start-up ecosystems [41], to mention a few. The continuous flow of new MMs for manufacturing or Industry 4.0 seems not to diminish, although new viewpoints, such as green suppliers [42], appear. MM can even be exploited for proactive skill development in the context of DT [43] and data value management capabilities [44]. In addition, industry-specific models are needed, as a systematic literature review found 19 Industry 4.0 MMs that did not fit into the oil and gas upstream industry [45]. To boost the development and digitalisation of the manufacturing industry, academies, industrial alliances and consultancies have provided various tools and models. In 2019, researchers reported on 10 academic Industry 4.0 MMs and 10 consultancies [46]. Several other maturity tools have been developed for Industry 4.0 and the manufacturing industry [47,48]. Before creating an MM for digital twins in the battery cell industry, Schabany et al. [49] analysed 767 papers and found four features relevant to any MM. Table 1 carries several examples of academic MMs having either clear dimensions and maturity levels or other highlighted features suitable for manufacturing companies. Alliances (e.g., ADMA, BDO, EDB Singapore and VDMA) and consultancies (e.g., Deloitte Consulting, Frost & Sullivan, IXON and PWC) have also created MMs for the manufacturing industry or Industry 4.0. [18].
Table 1. Academic MMs related to manufacturing industry or Industry x.0 (in alphabetic order) updated from [18].

3. Research Design

Many MMs have been developed and are widely applied in various industrial contexts. However, their construction and testing must be rigorous and result in practical solutions for industrial use. To support this, approaches have been reported to support model development [75,76,77]. In our research, we have followed the maturity model development approach defined by de Bruin et al. [75]. This model was already selected for use in the first phase of the research [18]. The widely used model offers easy-to-understand steps while enabling systematic maturity model development. It also allows incremental improvements to be made over time to the model and tool (Figure 1).
Figure 1. Maturity model development phases.
The development and testing of the extended MM and tool were done in two iterations. The first iteration covered the definition, implementation and testing of our original model and tool (called ManuMaturity). The model and tool were intended to help manufacturing companies proceed in digitalisation, reach Industry 4.0 or even go beyond it [78]. The ManuMaturity model and tool were tested in an industrial setting [18]. The study revealed the concepts of sustainability [13,14], employee [15] and data sharing [7,8,9,10]. They eed to be included in the model and tool. This triggered the development of the next iteration of the model and tool. This article reports the research carried out in the 2nd iteration. Table 2 illustrates the actions performed, techniques used and output for each development phase, extending the ManuMaturity model and tool to cover sustainability, innovation ecosystem and data-sharing components identified as important additional components of the model. In the development of the extended MM and the tool (nominated as OSME maturity tool), we applied the maturity model development approach defined by de Bruin et al. [62], which led to the following research question:
How can the maturity model development process be followed when extending a maturity model with features like data sharing, open innovation ecosystem and sustainability?
The purpose of the model and tool is to support “as-is analysis” to provide a diagnostics tool to describe the company’s current state (descriptive) and, later on, enable a comparative analysis with different sizes or domains of manufacturing companies when sufficient data has been collected. Indeed, it has been indicated that MMs are in their first phase, which is descriptive, then prescriptive and finally evolve towards a comparative purpose [56]. The model development process was assisted by both practitioners and academia. Practitioners comprise the manufacturing and IT companies in the Open Smart Manufacturing Ecosystem (OSME) project 1. Academics are represented by the selected research scientists of one research institute. The new scope of the model and tool affected meant that there was a need to include experts in data sharing, innovation ecosystems, sustainability and employee viewpoints in model development. The model and tool are primarily intended for company executives and management to understand the company’s current state and to identify possible paths to improvement in the future (targeted audience). The model and tool should enable self-assessment and third-party–supported analysis (method of application). The respondents of the model application are representatives of different employee groups related to the company’s manufacturing process covering the model’s new scope such as manufacturing managers, procurement, quality, information management, and sustainability. The model architecture was the same as in the first iteration [18], including sectors, dimensions, question areas and five maturity levels. The logic of how the assessment happens was similar to the ManuMaturity model and tool, i.e., question areas were formulated as questions with response alternatives reflecting each maturity level. The respondent chooses the most descriptive response from the set of five alternatives.
Table 2. Research approach as the matrix of MM development method model [75] as columns and procedures [79] as rows.
The new scope heavily impacted the model details; therefore, the population phase required much work and calendar time. The scope and design phase of the maturity model process was more straightforward since the second iteration was based on the ManuMaturity model and tool, and there were just a few changes in the overall design. The population phase focuses on the questions of what to measure and how. The literature review results were utilised to iteratively generate an updated model [75] considering sustainability, innovation ecosystem and data sharing–related maturity model literature. The OSME MM contained dimensions, question areas (questions) and response alternatives organised according to the MM principles. The updated model with dimensions, questions and response alternatives was presented and discussed thoroughly in expert interviews with both research scientists (academia) and practitioners. Five senior scientists–high-level experts with wide industrial experience representing manufacturing (1 person), data sharing (2 persons), innovation ecosystems (1 person) and sustainability (1 person)–were interviewed and workshopped with. All experts could also comment on the whole model from the manufacturing industry’s point of view, not just on their expert areas. Furthermore, two IT companies were interviewed about the model. They represented especially human/employee and data sharing aspects. Interviews were semi-structured; new or modified questions and response alternatives were presented in the interview and feedback and improvement proposals were gathered during the interviews. This process resulted in an extended version of the original model, the OSME MM. The model’s development reported that each question of the question area and response alternative can be traced back to its source, either literature or interviews. While formulating the response alternatives, the development steps of the manufacturing industry [3] were kept in mind. The practical measurement was implemented as a web-based tool guiding the respondent through background and actual maturity model questions. As a result, the respondent gets a report that indicates the respondent company’s maturity level (dimensions and question areas) compared to the dimension averages of all respondents in the tool. Therefore, the respondent can compare their situation with the average of the other companies on a large scale. After the model was populated with questions and their response alternatives, the model and tool were tested. The OSME MM and tool were tested in case studies in four manufacturing companies (see Section 3.1 for details). Companies were invited to respond to the OSME maturity tool. Follow-up meetings were arranged for each company. Two researchers facilitated all meetings. Meetings were recorded for research purposes. In these follow-up meetings, the company’s results were presented and discussed. Furthermore, the respondents commented on tool structure, functionality and ease of use, as well as the perceived completeness of the model, with dimensions, questions and response alternatives. In the deployment phase, the lifecycle plan for maintaining the tool was developed and the OSME maturity tool was integrated into the web portal, which introduces several tools for twin transition (in Finnish at TwinTransition.fi). This contributes to the tool’s availability and continuous maintenance of the tool and the model. Bruin et al. [75] stated that it is likely that the initial application of the model will be with the stakeholders with whom the model has been developed and tested. Therefore, the tool was applied on a larger scale with one participating manufacturing corporation (5 subsidiaries) (see Section 3.2 for details). The research followed the same protocol as in the previous case. In this way, we gained insight into a larger group of respondents (24 respondents). This is the first and critical step when building acceptance. Until the model and tool have been deployed to entities independent of development and testing activities, generalisability will remain an open issue [75]. This further testing and feedback collection will be one of the future research activities. 3.1. Testing the OSME Maturity Model with Heterogeneous Companies The OSME maturity model was piloted with five Finnish manufacturing companies from the OSME 1 project (Table 3). The responses were gathered from February to March 2023. A total of 12 responses were received from 5 companies. Four were original equipment manufacturers (OEM) and one was a small and medium-sized company (SME). The results and feedback sessions with four out of five companies took place from March to May 2023. Table 4 indicates the level of interest in the companies.
Table 3. Twelve responses were received from five Finnish manufacturing companies.
Table 4. The results and feedback session took place between March and May 2023.
3.2. Deployment with a Group of More Homogeneous Companies Company C—a corporation with 5 subsidiaries—expressed their willingness to repeat the maturity analysis within their group of companies on a larger scale. During the second round, we received 24 responses from Company C (5 subsidiaries) in June 2023 (Table 5). The results were delivered in August, but the feedback session was delayed until 17 October. Four managers attended the feedback session, all representing senior manager roles. It was agreed that a similar analysis and session would be arranged in autumn 2024 to see how the situation has improved in companies. Company C is an SME in the industry domain, which is “Manufacture of fabricated metal products, except machinery and equipment,” as indicated in Table 3.
Table 5. Twenty-four responses were received from the case company group (corporation) having four subsidiaries.

4. Results

The results are expressed in the maturity tool development phases: model development, tool implementation or instrumentation, test and deployment [75]. Thus, the next subtitles carry the model development phase in brackets. 4.1. Extension of ManuMaturity (Model Development) The maturity model presented in this paper was built on the ManuMaturity tool [18,78]. The ManuMaturity model was designed with seven dimensions in three sectors (Figure 2). The (grey) dimensions of sustainability and employee contributed to the responsibility sector. Further, the dimensions of infrastructure and data together discovered the viewpoints of the (blue) technology sector. Finally, the (red) business sector had two dimensions: business model and customer. The process dimension interacted with all three sectors and was drawn around the other six dimensions [18].
Figure 2. The sectors and dimensions of ManuMaturity.
The ManuMaturity model had five maturity levels: (i) traditional factory, (ii) modern factory, (iii) agile factory, (iv) agile cognitive factory and (v) agile cognitive industry [18]. Our initial hypothesis was to add new dimensions covering the “ecosystemic way of working” and federated data sharing. When looking into the existing maturity model, we found that the ecosystemic way of working was already embedded into the maturity levels. The federated data shading was implemented with one additional question (supply chain data management) in the data dimension and another (openness for collaboration) in the business model dimension. In addition to openness to collaboration, the business model dimension was enhanced with new questions, such as resiliency and foresight. The old dimension of the employee was enriched with several relevant question areas that contributed to the human-centric Industry 5.0 approach and renamed as employee and culture. The new questions relate to monitoring work(ing hours), teamwork, attitude to global challenges, well-being and learning and development programmes. One employee-related question (occupational safety) was replaced in the processes dimension, together with quality. Figure 3 displays the additions and modifications made for the OSME MM and Table 6 the actual questions for each dimension.
Figure 3. The dimensions and question areas of the OSME maturity model.
Table 6. Dimensions and questions of the OSME MM.
4.2. Implementation of the OSME Maturity Model (Tool Implementation) The OSME MM was deployed as a web application, i.e., full-stack software implementation. Figure 4. Maturity tool architecture shows the web architecture with the backend and frontend. The frontend was implemented as a web browser user interface (UI) with React JavaScript library 2, Material UI component library 3, i18next internationalisation framework 4 and Apollo Client 5. The backend was implemented with Spring Framework 6 modules and the Netflix DGS Framework 7. The backend utilises PostgreSQL 8 as a database. Communication between the frontend and the backend was deployed with GraphQL 9. While localisation was done for Finnish and English languages, the maturity tool supports internationalisation, i.e., enabling adding more regions or languages. For identity and access management, including registration with customised maturity tool themes (i.e., background figure and stylesheet) and properties (i.e., additional registration input), Keycloak was utilised.
Figure 4. Maturity tool architecture.
When conducting the assessment, the respondent goes through several phases. Background information is asked to enable the use of the maturity tool. This is needed for data gathering and publishing the results after analysing the data so that the respondent cannot be identified. In the user registration phase, identification information is asked, i.e., username, password, first and last names, email address, telephone number, organisation, postal code and country. After logging in, in the background phase, additional information not required for the registration is asked for, i.e.,the respondent’s position and represented function, headcount, turnover and domain of respondent’s company. The ‘statistical classification of economic activities’ in the European Community, abbreviated as NACE rev. 2, 10 is utilised for the drop-down selection of the domain. The maturity model is presented internally as JavaScript Object Notation (JSON), but to enable feasible editing of the model, the maturity model JSON can be generated from the document with a fixed structure produced with a word processor so that different parts required for forming the views of the UI can be identified. A separate document with the same structure but a different language is utilised for each desired localisation. Figure 5 shows a view of the questionnaire phase, i.e., the questions and response options (to choose from) of the customer interface dimension. The response options indicate a numeric value to enable the calculation of averages. The view consists of (i) a stepper that displays progress through a sequence, (ii) buttons for backward and forward navigation between views of the sequence, (iii) snack bars (also known as toasts) for brief notifications about unlocking the next view of the sequence, (iv) the name of the model, (v) dimension of the model as a list with questions as radio buttons and (vi) counter for required responses.
Figure 5. Questions and prewritten response options for the customer interface dimension.
In the results phase, the result levels of maturity dimension or questions are visualised in three ways. The radar chart in Figure 6, displays dimension averages calculated from the fresh responses (my current state, orange) and all given responses (all, blue). The bar chart for each question in Figure 7 and the textual report for each question in Figure 8 enable detailed analysis. In case the respondent has completed the target setting phase addition, the results are visualised with the response of the respondent (smiley) compared to the average level with the value of all responses (numeric value) as well as the desired target level (cup) Figure 9. The respondent can also export the visualisations.
Figure 6. Sample radar chart on the dimensions tab in the results phase.
Figure 7. Sample bar graph on each question in the questions tab of the results phase.
Figure 8. Screenshot from the customer interface in the textual status report of the results phase.
Figure 9. Visualisation of target setting phase of the maturity tool.
4.3. Heterogeneous Company Results and Feedback Discussions (Testing) Testing of the maturity tool took place from February to March 2023, with a total of 12 responses from five companies, four of which were OEMs (see Figure 10 for further details). Each company received its results on a radar graph displaying its dimension averages and the dimension averages of all respondents. The average of a dimension is the average of responses given for a question related to that dimension. In addition, the bar graphs for each question were shared with the companies. Figure 11 displays the overall results presented to the companies, counted and drawn in Excel. All OSME companies (twelve responses, N = 12) are represented in the blue line and OEMs (N = 7) with red. The customer dimension is the only one that exceeds level 2, agile factory [3], within the average of OEMs. Generally, OEMs are slightly above all respondents. The patterns are quite similar, as OEMs represent 7/12 of all responses. The biggest gap lies in the data where the responses of SMEs pull down the average of all respondents. The bar graph in Figure 12 displays the averages and average deviations for each question in the data and infrastructure dimensions. The average deviation is lowest in the question, “How are systems, networks and programs protected from digital attacks?”. This means that the variation in the responses was minor. The highest average deviation is in the question “How is production data collected and shared?”, all OSME responses 0.75 and OEMs 0.57. The production data reaches level 2 for OEMs and the deviation is low, while the level is lower for all responses and the deviation is higher. The lowest maturity levels in the data dimension are in questions related to (i) supply chain data, (ii) data analytics, (iii) exploitation of data and (iv) agile production. The average response (OSME average) for these questions remains below 1.5. The even maturity result between OEMs and all responses of the infrastructure dimension (three rightmost questions in the radar graph, Figure 11) is also visible on the bar chart (Figure 12). Some companies requested the target-setting option, but only two persons finally completed the target-setting. Further, the registration phase was considered complex. Another way to block unwanted (robot) responses is to find them before the verified registration can be skipped.
Figure 10. Results as a radar graph visualise the averages of the dimensions in heterogeneous test case.
Figure 11. Results as a bar graph over the questions of data and infrastructure dimensions with the average deviation.
Semi-structured interviews were organized during the feedback sessions with the companies, where the functionality of the model and the tool in the pilots were discussed. The semi-structured interview questions were divided into two perspectives. One focused on the maturity model and questions and the other on the use and usability of the tool. The general comment was that the model seemed to be comprehensive. However, regarding the structure of the model, there was a reflection that some of the themes of the questions intersected in the dimensions, meaning that some questions could also fall under another dimension. Furthermore, some dimensions have many questions and some have a few. The levels of the response options sparked a discussion. It was suggested that the lowest level should be marked as “1”. This was because it is more difficult for a person to think of the lowest level as “0”. According to companies, transitions between different levels are not always evenly spaced. Thus, for example, the step from levels 1 to 2 can be bigger than from levels 2 to 3. It was also commented that finding your own level among the different options was sometimes challenging. On the other hand, it was observed that there was no ambiguity in most of the questions. There were some comments on the contents of the individual questions, for example, regarding their clarity which will be considered when maintaining the model and tool. In addition, it was stated, for example, related to hybrid work, that well-being and tools for teamwork are important when there is not so much F2F interaction anymore; therefore, it was good that the employees and culture dimension had many questions. It was mentioned that many questions about the employee and culture dimension can be quite subjective in how they are interpreted. Company D especially considered the modifications and additions compared to the previous version of the model and tool, i.e., ManuMaturity:
“I believe that this (model) is quite good in terms of coverage. Discussion and iteration in the OSME project have now brought additions to this model. When I look at it now, safety and quality are extremely important to us, so they are good additions. In the bigger picture, cooperation at the ecosystem level to make it open is important and must be measured. Resilience has also been an important issue during the last couple of years. Examining the entire supply chain is a good and logical addition when supply chains compete with each other. There is now much emphasis on the employee and culture dimension and they are of course good things, but they can also be subjective things, how the current state of these things is interpreted.” [Vice President of Company D]
In all the interviews, it was stated that the tool was fairly easy to use but that it is good to remember that the responses reflect the respondent’s understanding of the situation in their companies.
“No problems in responding, we have experienced much more difficult tools.” [Production director of Company B]
“The answering was straightforward, but of course, this is my subjective assessment of the situation.” [Continuous Improvement Manager & Lean Leader of Company A]
After getting responses, navigation between questions of different dimensions and results pages sparked debate and criticism. This functionality was not seen as flexible as it should be.
“... when you have responded once, moving between questions and responses was difficult. Maybe you could add a shortcut menu from which you can access results, goals or questions.” [Production director of Company B]
The response time varied from 1/2 h to 1 h. Goal setting was a new feature of the tool requested in the previous round (Saari et al., 2022) [18]. However, only two persons (out of 2 companies) completed the goal setting. For example, it was commented that the goal-setting section of the tool was not noticed after looking at the results. In addition, it was commented that the target setting was not sufficiently specified. Goal setting is a different kind of activity from identifying the current state. You should see the current state and the defined target state, and based on that, you should think about practical actions and what should be done. Thus, the tool could be more of a support tool for the company’s development projects.
“First you get the results in the tool and after that—maybe the next time you use the tool—you start making a goal setting. The goal setting is a different kind of mental process for a person than the evaluation of the current state. When the goal setting comes directly after the results tab (sequence), the user will not necessarily click any Next-button there ... the goal setting is also not sufficiently specified. It would perhaps be easier to see the current and target states, and based on that you could think about (and describe) practical actions. The tool could be a support tool for development projects ... such as what development projects there should be and how to prioritize these projects.” [Production director of Company B]
Related to the reports, it was noticed that not all users found all reports. They were difficult to find. This is a shortcoming in the usability of the tool. On the other hand, one respondent had gone through all the graphs and even noticed a mistake in his responses based on them and changed his response. Regarding the usability of the results, it was stated that they could be used for different purposes. The result of this tool can be used as a basis for discussion and development in the company because it gives a reference point where the company is, identifies gaps, concretises different issues and thus helps to find development targets. It was also stated that companies had a broader understanding of the use of digitalisation, especially if a team responded and discussed the results together. Therefore, the company’s internal discussion and getting a big picture was emphasised. This was already noticed when piloting the ManuMaturity tool as one good way to apply it [18].
“It would be good to implement this assessment within our whole team.” [Business Data Analyst of Company A]
“It is better that this kind of [assessment] is done with a group, because it brings discussion, iteration and different points of view, which brings added value to the assessment when there is also an open atmosphere of trust in the team.” [Vice president of Company D]
One respondent had experience with MMs and their utilisation (e.g., EFQM 11). He considered them a good approach when applied correctly.
“Maturity-type assessment solutions are good ... we can discuss where we are and where we should be and adjust activities accordingly.” [Chief Operations and Supply Chain Officer of Company B]
“Very good tool to concretise issues and situation in a company.” [Production director of Company B]
Company C proposed they could use the tool more intensively in their company (group type of company). This meant they wanted to have a broader application of the tool to cover the stakeholders of different subsidiaries and then discuss the results together. This was done in the deployment phase of the model and tool and is explained in the next paragraph. 4.4. Homogeneous Company Group Results and Feedback (Deployment Phase) The deployment phase was experienced with a group of more homogenous companies, i.e., within one group of companies during the summer 2023 (more details in Section 3.2). The group contained four subsidiaries. Figure 12 displays the averages of each dimension for the four subsidiaries, the group as a whole (N = 24) and the reference group, i.e., OSME companies (N = 32).
Figure 12. Radar chart of the averages for the company group experiment.
This time, the radar chart is quite busy, so the averages for each dimension are presented in Table 7. The number of new responses also decreased the averages of all OSME responses in every dimension. Subsidiary 1’s peak in customer dimension and drop in business model dimension were discussed in the feedback session.
Table 7. Dimension averages of the company group experiment.
In autumn 2023, subsidiaries of Company C responded to the tool. They gathered into a joint session where the results of the OSME maturity analysis and feedback for the model and tool were discussed. Regarding the model and tool, it was stated that the model brings out areas where the company could improve its operations. When applied in the context of a group of subsidiaries, this tool also enables you to discuss, find, and share “best practices” between different subsidiaries. At the end of the session, the method by which the results will be utilised in practice was discussed. Company C (with 4 subsidiaries) agreed that each subsidiary first considers the development targets (e.g., in the steering group) and identifies 1–2 development targets. Then, they bring these ideas to the group-wide discussion to agree on what to focus on at the group level. It was also discussed that this same analysis could be repeated after about a year, testing again with the tool to see where the whole group was heading and how to steer the improvement actions. This allows us to utilise the model and tool for “before-after” scenarios within a set of subsidiaries.

5. Discussion

Research on MMs and tools began in 2016, when a generic—suitable for all industries—digitalisation maturity tool was published [80]. The tool in question was used in various case studies, mainly to support the digitalisation development of SME companies [4,81]. The ManuMaturity tool supported the digitalisation needs of the manufacturing industry [18,78]. The tool in question was piloted in the spring 2022 when the need arose to expand this tool also to cover the perspectives of information sharing, ecosystem way of working, human perspective, and sustainability [18]. Some development model support the development of MMs and tools [75,76,77]. In our research, we used the model presented by Bruin et al. [75], because of good experiences in the first iteration [18]. Further, the same model has been utilised in other studies, for example [40,82]. The discussion hereafter is structured based on the model development phases: (i) model development, (ii) testing and piloting, (iii) deployment and (iv) maintenance [70]. Therefore, we start with the development phase. Schabany et al. [49] identified four features relevant to any MM. The first one is goal congruence or consistency, i.e., the dimensions shall reflect the goal of the MM. Our OSME MM fully fulfills this objective, as the model’s foundation and prewritten response options lie on the stair model developed for the manufacturing industry aiming for Industry 4.0 or even beyond (Figure 13) [3].
Figure 13. The development steps of the manufacturing industry [3].
The second principle, uniqueness, is also followed, as a topic is represented by only one dimension. Thus, the ecosystemic operation did not reserve a new dimension in the OSME MM, as it is embedded in the response options at the highest level (agile cognitive industry). Further, the third one—definiteness—is reached, as all seven dimensions are uniquely defined. Also, the fifth one—suggestiveness—is reached, as the highest level is beyond. In our OSME MM, the response options at the highest level are beyond the obvious. Being suggestive also caused feelings of inadequacy and frustration in the respondents, as the highest levels seemed so far away. Further, the linear scale between the levels was considered unfair, as the leap between higher steps is huge. An exponential scale was even proposed. The scale with exponent two could be feasible, starting with 20 = 1 and ending with 24 = 16. The problem with the linear scale raises the question of averages: can the averages be counted if the numeric values are not solid enough? As in several MMs, the averages are counted for dimensions; this is a common problem. Instead of averages, perhaps the distribution of responses at each level could be discussed. In Table 1, several academic MMs are described. Nick et al. [63] did not follow the suggestiveness request, as each dimension has five unique intervention points. This is quite a practical outcome, as it was quite challenging to follow the stair model when formulating the response options for dimensions such as sustainability and employee and culture. A few MMs included the weighting of dimensions [57] or the selection of irrelevant questions. The model with new elements, such as 11 new questions and one modified dimension (employees and culture) (Figure 3), was tested with a heterogeneous group of manufacturing companies, six of which were OEMs. The results for OEMs were slightly higher than those for other companies, but due to the insufficient number of responses, patterns for OEMs or SMEs could not be established.A new feature in our tool was the option of setting the target level after the assessment had been done. The target-setting (as-is and to-be) option was mentioned in a few MMs [56,66,70]. No MMs in Table 1 described the software implementation of their model or tool; thus, our manuscript is a more complete description of the development process. A few models apply the radar graph [71,73] and count averages for the dimensions. Two MMs applied digital methods as a web tool [59] or an Excel solution [50]. The OSME MM was implemented as an open and free web application 12, where registration with a functional email address is needed to avoid irrelevant input to the response database. The deployed software architecture is described, and a few screenshots of the UIs are displayed in this manuscript. Our tool immediately provides three views (radar over dimensions in Figure 6, question bars in Figure 7 and textual status report in Figure 8) for the respondent, but the analysis over a group of responses has to be conducted manually, exploiting Excel functionalities. The tool is easily reconfigurable with a new language if the dimensions, questions, response options and UI texts are translated into a new language. The tool can provide a new language instance from the translated document. Further, the deployed tool was experimented with within one group of companies and 24 responses were gathered. In the MMs listed in Table 1, only a few reported experiments with companies [52,53,57,62,67,72]. Indeed, careful development of models and tools is needed, as well as their testing in industrial environments, to ensure their applicability in practice. This is important so that the content of the model and the operation of the tool can be modified based on the user experience gathered.This would require more research and concurrent development together with industrial representatives. The maintenance phase is not fully managed, although technical support is agreed upon by the end of 2024. Unfortunately, the certificates and potential software version updates cannot be managed within a limited project. However, maintenance (both model and tool) is a crucial activity [75]. Therefore, a new way (funding) has to be found to enable this tool’s full and continuous functionality. For this reason, the development of the business model for the tool is also relevant. Although VTT is a non-profit organisation, no unprojected work is allowed. Without promoting the tool, the response database will not have enough data. Sufficient data would enable reports and articles on the findings. VTT has created a landing page for various methods and tools to support the twin transition of the manufacturing industry 13. Only one MM reported support after the assessment by providing a roadmap [58]. Also Mittal et al. [27] recognised this gap. Consultancies and associations naturally provide more support, e.g., workshops, but not without a fee or membership. An academic MM’s business model and life cycle management remains an issue. According to the feedback sessions, the experimentation of the OSME maturity tool was interesting and eye-opening for the companies. They planned to bring the results to internal discussions in their company. Since the maturity model is quite extensive, in some cases, it is useful for the company to gather several people together to respond to the tool as a team. In this case, the personnel can discuss different options and come up with ideas for development needs. This identification and piloting of different usage scenarios of the maturity tool has been found useful [18,83]. One group of companies also aimed to repeat the assessment next year. This means the possibility for a before-after comparison. Because of the low number of responses, general patterns for manufacturing OEMs or SMEs could not be created. These patterns could have been applied when accepting or rejecting new ecosystem member candidates.

6. Conclusions and Further Work

This study presents the research path of the ManuMaturity model and the tool was extended to cover rising topics such as sustainability [5], data sharing and employees in addition to the Industry 4.0 [2] goals. The extended OSME model and tool were developed and tested with a Finnish manufacturing ecosystem. This research is part of a longer research path, which aims to promote the digitalisation of manufacturing companies and towards data sharing within the supply chain, open innovation ecosystem and sustainable manufacturing. With the help of the developed maturity model and the tool, it was possible to make assessments in case companies, where the tool and its results were commented mostly positively. However, for instance, the tool considered the registration phase complicated. According to our experience, the tool can be applied in various ways and individual people can respond to the tool and receive immediate comparisons to other respondents. Also, a group of people respond to the tool together and identify the topics for further development simultaneously. In this article, we present the development of the maturity model and tool and their testing in an industrial environment. The development and testing of the maturity model and the tool have been done following the process model to develop maturity models [75]. The process model helped structure the development and testing of the maturity model.In addition, it covered important considerations for model maintenance and further development, which are crucial for applying the tool in the industry. However, the definition of the maintenance phase is still under construction, as the business model of the tool remains open. The latest version of the tool is available on the landing page created for the twin transition tools 13. The validation was done twice, first with a group of companies representing both OEMs and their suppliers. The second experiment was conducted within a group of companies, i.e., the validation was unusually comprehensive [26]. The limitation of the research is that the testing of the model and tool has been limited to a few companies. Thus, in the future, they should be tested more widely in other companies. In addition, it would be interesting to conduct longitudinal research on the benefits of using the model and tool. This is an interesting research topic for the future, although it requires well-planned long-term cooperation with companies. All these functions also increase the number of responses in the tool’s database, which opens up the possibility to analyse the collected data, for example, concerning a certain demographic factor such as company size, turnover or subdomain.

Acknowledgments

We wish to thank the companies of the Open Smart Manufacturing Ecosystem 1 (OSME) project funded by Business Finland for contributing to this research and providing valuable feedback when developing the OSME maturity tool. Thanks also to VTT Technical Research Centre of Finland Ltd. for allowing us to perform this study.

Author Contributions

Individual contributions of the authors are following: Conceptualization, L.M.S. and J.K.; Methodology, J.K.; Software, M.Y., L.M.S. and J.K.; Formal Analysis, L.M.S. and J.K.; Investigation, L.M.S.; Resources, J.K.; Data Curation, L.M.S. and J.K.; Writing—Original Draft Preparation, L.M.S. and J.K.; Writing—Review & Editing, L.M.S. and J.K.; Visualization, L.M.S. and M.Y.; Supervision, J.K.; Project Administration, J.K.; Funding Acquisition, J.K.

Ethics Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Funding

This research was funded by Business Finland within the project Open Smart Manufacturing Ecosystem.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability Statement

The minutes of the feedback discussions and the audio recordings are confidential. Due to its proprietary nature, supporting data cannot be made openly available. The maturity tool responses cannot be shared openly to maintain the privacy of companies.

Footnotes

  1. https://cris.vtt.fi/en/projects/open-smart-manufacturing-ecosystem; https://www.mexfinland.org/osme/.
  2. https://github.com/facebook/react.
  3. https://mui.com/.
  4. https://github.com/i18next/i18next.
  5. https://github.com/apollographql/apollo-client.
  6. https://github.com/spring-projects/spring-framework.
  7. https://github.com/netflix/dgs-framework.
  8. https://www.postgresql.org/.
  9. https://graphql.org/.
  10. https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/ks-ra-07-015.
  11. https://efqm.org/the-efqm-model/.
  12. https://twintransition.fi.
  13. https://maturity.vtt.fi/.

References

1.
Ganzarain J, Errasti N. Three stage maturity model in SME’s towards industry 4.0. J. Ind. Eng. Manag. 2016, 9, 1119–1128. [Google Scholar]
2.
Machado C, Davim PJ. Industry 4.0 Challenges, Trends, and Solutions in Management and Engineering; CRC: Boca Raton, FL, USA, 2020.
3.
Heilala J, Helaakoski H, Kuivanen R, Kääriäinen J, Saari L. A Review of Digitalisation in the Finnish Manufacturing SME Companies; VTT Technical Research Centre of Finland: Espoo, Finland, 2020.
4.
Kääriäinen J, Pussinen P, Saari L, Kuusisto O. Applying the positioning phase of the digital transformation model in practice for SMEs : Toward systematic development of digitalization. Int. J. Inf. Syst. Proj. Manag. 2020, 8, 24–43. [Google Scholar]
5.
Davim PJ. Sustainable and Intelligent Manufacturing: Perceptions in line with 2030 Agenda of Sustainable Development.  Bioresources 2024, 19, 4–5. [Google Scholar]
6.
Korhonen J, Honkasalo A, Seppälä J. Circular Economy: The Concept and its Limitations. Ecol. Econ. 2018, 143, 37–46. [Google Scholar]
7.
Deshmukh RA, Jayakody D, Schneider A, Damjanovic-behrendt V. Data spine: A federated interoperability enabler for heterogeneous iot platform ecosystems.  Sensors 2021, 21, 4010. [Google Scholar]
8.
Scerri S, Tuikka T, Lopez de Vallejo L. Towards a European Data Sharing Space Enabling Data Exchange and Unlocking AI Potential; Big Data Value Association: Brussels, Belgium, 2019.
9.
Tuikka T, Curry E. An Organisational Maturity Model for Data Spaces: A Data Sharing Wheel Approach. In Data Spaces; Springer: Cham, Switzerland, 2022.
10.
Jurmu M, Niskanen I, Kinnula A, Kääriäinen J, Ylikerälä M, Räsänen P, et al. Exploring the role of federated data spaces in implementing twin transition within manufacturing ecosystems.  Sensors 2023, 23, 4315. [Google Scholar]
11.
Kristoffersen E, Blomsma F, Mikalef P, Li J. The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies. J. Bus. Res. 2020, 120, 241–261. [Google Scholar]
12.
Orko I, Heikkilä L, Häikiö J, Järvinen S, Karhu M, Klein J, et al. Towards a Data-Driven Circular Economy: Stakeholder Interviews; VTT Technical Research Centre of Finland: Espoo, Finland, 2022.
13.
Davim PJ. Sustainable Manufacturing; John Wiley & Sons: Hoboken, NJ, USA, 2010.
14.
Peralta ME, Soltero V. Sustainable manufacturing: Needs for future quality development. In Sustainable Manufacturing; Elsevier: Amsterdam, The Netherlands, 2021; pp. 1–28.
15.
Romero D, Stahre J, Taisch M. The Operator 4.0: Towards socially sustainable factories of the future.  Comput. Ind. Eng. 2020, 139, 106128. [Google Scholar]
16.
European Commission. Digital Maturity Assessment for EDIH Customers; European Commission: Seville, Spain, 2022.
17.
Albukhitan S. Developing Digital Transformation Strategy for Manufacturing. Procedia Comput. Sci. 2020, 170, 664–671. [Google Scholar]
18.
Saari LM, Heilala J, Kääriäinen J. Building the maturity model for a sustainable collaborative manufacturing industry. In Proceedings of the XXXIII ISPIM Innovation Conference “Innovating in a Digital World”; LUT Scientific and Expertise Publications: Research Reports, Copenhagen, Denmark, 5–8 June 2022.
19.
Machado CF, Davim PJ. Industry 5.0.; Springer International Publishing: Cham, Switzerland, 2023.
20.
Paulk MC, Curtis B, Chrissis MB, Weber CV. Capability maturity model.  IEEE Softw. 1993, 10, 18–27. [Google Scholar]
21.
Teichert R. Digital transformation maturity: A systematic review of literature. Acta Univ. Agric. Et Silvic. Mendel. Brun. 2019, 67, 1673–1687. [Google Scholar]
22.
Warner KSR, Wäger M. Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Plan. 2019, 52, 326–349. [Google Scholar]
23.
Goos G, Hartmanis J, Van J, Board L, Hutchison D, Kanade T, et al. An Overview of the Business Process Maturity Model (BPMM). In Lecture Notes in LNCS 4537—Advances in Web and Network Technologies, and Information Management; Chang KC, Wang W, Chen L, et al., Eds.; Springer: Huangshan, China, 2007; pp. 384–395.
24.
Tarhan A, Turetken O, Reijers HA. Business process maturity models: A systematic literature review. Inf. Softw. Technol. 2016, 75, 122–134. [Google Scholar]
25.
Saleh M. Information Security Maturity Model. Int. J. Comput. Sci. Secur. 2011, 5, 316–337. [Google Scholar]
26.
Wendler R. The maturity of maturity model research: A systematic mapping study. Inf. Softw. Technol. 2012, 54, 1317–1339. [Google Scholar]
27.
Mittal S, Khan MA, Romero D, Wuest T. A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs).  J. Manuf. Syst. 2018, 49, 194–214. [Google Scholar]
28.
Hein-Pensel F, Winkler H, Brückner A, Wölke M, Jabs I, Mayan IJ, et al. Maturity assessment for Industry 5.0: A review of existing maturity models. J. Manuf. Syst. 2023, 66, 200–210. [Google Scholar]
29.
Elhusseiny HM, Crispim J.  A Review of Industry 4.0 Maturity Models: Theoretical Comparison in The Smart Manufacturing Sector. Procedia Comput. Sci. 2024, 232, 1869–1878. [Google Scholar]
30.
Reike D, Vermeulen WJV, Witjes S. The circular economy: New or Refurbished as CE 3.0?—Exploring Controversies in the Conceptualization of the Circular Economy through a Focus on History and Resource Value Retention Options. Resour. Conserv. Recycl. 2018, 135, 246–264. [Google Scholar]
31.
Saari L, Järnefelt V, Valkokari K, Martins JT, Acerbi F. Towards sustainable manufacturing through collaborative circular economy strategies. In Smart and Sustainable Collaborative Networks 4.0. Proceedings of the 22nd IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2021, Saint-Étienne, France, 22–24 November 2021; Camarinha-Matos LM, Boucher X, Afsarmanesh H, Eds.; Springer: Cham, Switzerland, 2021; pp. 362–373.
32.
Saari L, Valkokari K, Martins JT, Acerbi F. Circular economy matrix guiding manufacturing industry companies towards circularity-a multiple case study perspective. Circ. Econ. Sustain. 2024, 1–26, doi:10.1007/s43615-024-00385-3.
33.
Kreutzer D, Müller-Abdelrazeq SL, Isenhardt I. Circular Economy Maturity Models: A Systematic Literature Review. Int. J. Econ. Manag. Eng. 2023, 17, 666–678. [Google Scholar]
34.
Acerbi F, Järnefelt V, Martins JT, Saari L, Valkokari K, Taisch M. Developing a qualitative maturity scale for circularity in manufacturing. In Advances in Production and Management Systems 2021, Nantes (France). IFIP Advances in Information and Communication Technology; Springer: Cham, Switzerland, 2021.
35.
Golinska-Dawson P, Werner-Lewandowska K, Kosacka-Olejnik M. Responsible resource management in remanufacturing—Framework for qualitative assessment in small and medium-sized enterprises. Resources 2021, 10, 19. [Google Scholar]
36.
Ellen Mac Arthur Foundation. Circulytics. 2022. Available online: https://ellenmacarthurfoundation.org/resources/circulytics/insights (accessed on 1 November 2022).
37.
Rozalska-Lilo M. Innovation Ecosystem Maturity. In Creators Medium. 2019. Available online: https://medium.com/creatorspad/innovation-ecosystem-maturity-3775812b3d3e (accessed on 27 January 2022).
38.
Leigh S, Smith K. Fostering Innovation in the Public Sector: An Organizational Innovation Ecosystem Maturity Model. Master’s Thesis, University of Alberta, Edmonton, AB, Canada, 2018.
39.
Visscher K, Hahn K, Konrad K. Innovation ecosystem strategies of industrial firms: A multilayered approach to alignment and strategic positioning. Creat. Innov. Manag. 2021, 30, 619–631. [Google Scholar]
40.
Jansen S.  A focus area maturity model for software ecosystem governance. Inf. Softw. Technol. 2020, 118, 106219. [Google Scholar]
41.
Cukier D, Kon F. A maturity model for software startup ecosystems.  J. Innov. Entrep. 2018, 7, 14. [Google Scholar]
42.
Miemczyk J, Johnsen T, Bernardin E. Developing a green supplier maturity model: Concepts, application and limits. In Proceedings of the 18th Annual IPSERA Conference; HAL: Oestrich-Winkel, Germany, 2009.
43.
Ostmeier E, Strobel M. Building skills in the context of digital transformation: How industry digital maturity drives proactive skill development. J. Bus. Res. 2022, 139, 718–730. [Google Scholar]
44.
Brennan R, Attard J, Helfert M. Management of data value chains, a value monitoring capability maturity model. In ICEIS 2018—Proceedings of the 20th International Conference on Enterprise Information Systems; SciTePress: Setúbal, Portugal, 2018; pp. 573–584.
45.
Onyeme C, Liyanage K. A systematic review of Industry 4.0 maturity models: Applicability in the O&G upstream industry.  World J. Eng. 2022, 20, 1160–1173. [Google Scholar]
46.
Felch V, Asdecker B, Sucky E. Maturity Models in the Age of Industry 4.0-Do the Available Models Correspond to the Needs of Business Practice? In Proceedings of the 52nd Hawaii International Conference on System Sciences, Grand Wailea, Hawaii, 8–11 January 2019; pp. 5165–5174.
47.
Liebrecht C, Kandler M, Lang M, Schaumann S, Stricker N, Wuest T, et al. Decision support for the implementation of Industry 4.0 methods: Toolbox, Assessment and Implementation Sequences for Industry 4.0.  J. Manuf. Syst. 2021, 58, 412–430. [Google Scholar]
48.
Rauch E, Unterhofer M, Rojas RA, Gualtieri L, Woschank M, Matt DT. A maturity level-based assessment tool to enhance the implementation of industry 4.0 in small and medium-sized enterprises. Sustainability 2020, 12, 3559. [Google Scholar]
49.
Schabany D, Hülsmann TH, Schmetz A. Development of a Maturity Assessment Model for Digital Twins in Battery Cell Industry.  Procedia CIRP 2023, 120, 946–951. [Google Scholar]
50.
Bretz L, Klinkner F, Kandler M, Shun Y, Lanza G. The ECO Maturity Model—A human-centered Industry 4.0 maturity model.  Procedia CIRP 2022, 106, 90–95. [Google Scholar]
51.
Elhusseiny HM, Crispim J. A Review of Industry 4.0 Maturity Models: Adoption of SMEs in the Manufacturing and Logistics Sectors. Procedia Comput. Sci. 2023, 219, 236–243. [Google Scholar]
52.
Colli M, Berger U, Bockholt M, Madsen O, Møller C, Wæhrens BV. A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era. Annu. Rev. Control 2019, 48, 165–177. [Google Scholar]
53.
Colli M, Madsen O, Berger U, Møller C, Wæhrens BV, Bockholt M. Contextualizing the outcome of a maturity assessment for Industry 4.0. Ifac-Pap. 2018, 51, 1347–1352. [Google Scholar]
54.
Ferreira DV, de Gusmão APH, de Almeida JA. A multicriteria model for assessing maturity in industry 4.0 context. J. Ind. Inf. Integr. 2024, 38, 100579. [Google Scholar]
55.
Frank AG, Dalenogare LS, Ayala NF. Industry 4.0 technologies: Implementation patterns in manufacturing companies. Int. J. Prod. Econ. 2019, 210, 15–26. [Google Scholar]
56.
De Carolis A, Macchi M, Negri E, Terzi S. A Maturity Model for Assessing the Digital Readiness of Manufacturing Companies. In Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing; Lödding H, Riedel R, Thoben K-D, von Vieminski G, Kiritsis D, Eds.; Springer International Publishing: Hamburg, Germany, 2017; pp. 13–20.
57.
Çınar ZM, Zeeshan Q, Korhan O. A framework for industry 4.0 readiness and maturity of smart manufacturing enterprises: A case study.  Sustainability 2021, 13, 6659. [Google Scholar]
58.
Issa A, Hatiboglu B, Bildstein A, Bauernhansl T. Industrie 4.0 roadmap: Framework for digital transformation based on the concepts of capability maturity and alignment.  Procedia CIRP 2018, 72, 973–978. [Google Scholar]
59.
Kieroth A, Brunner M, Bachmann N, Jodlbauer H, Kurz W. Investigation on the acceptance of an Industry 4.0 maturity model and improvement possibilities. Procedia Comput. Sci. 2022, 200, 428–437. [Google Scholar]
60.
Klötzer C, Pflaum A. Toward the Development of a Maturity Model for Digitalization within the Manufacturing Industry’s Supply Chain. In Proceedings of the 50th Hawaii International Conference on System Sciences, Hilton Waikoloa Village, Hawaii, 4–7 January 2017; pp. 4210–4219, doi:10.24251/HICSS.2017.509.
61.
Lucato WC, Pacchini APT, Facchini F, Mummolo G. Model to evaluate the Industry 4.0 readiness degree in Industrial Companies. IFAC-PapersOnLine 2019, 52, 1808–1813. [Google Scholar]
62.
Ngai EWT, Chau DCK, Poon JKL, To CKM. Energy and utility management maturity model for sustainable manufacturing process.  Int. J. Prod. Econ. 2013, 146, 453–464. [Google Scholar]
63.
Nick G, Kovács T, Ko A, Kádár B. Industry 4.0 readiness in manufacturing: Company Compass 2.0, a renewed framework and solution for Industry 4.0 maturity assessment. Procedia Manuf. 2020, 54, 39–44. [Google Scholar]
64.
Pienkowski M. Comprehensive Lean Manufacturing Maturity Model. In Proceedings of the 34th IBIMA Conference, Madrid, Spain, 13–14 November 2019.
65.
Pulkkinen A, Anttila J-P, Leino S-P. Assessing the maturity and benefits of digital extended enterprise. In Proceedings of the 29th International Conference on Flexible Automation and Intelligent Manufacturing FAIM2019, Limerick, Ireland, 24–28 June 2019; pp. 1417–1426.
66.
Rafael LD, Jaione GE, Cristina L, Ibon SL. An Industry 4.0 maturity model for machine tool companies.  Technol. Forecast. Soc. Chang. 2020, 159, 120203. [Google Scholar]
67.
Saari L, Kuivanen R, Poikkimäki J. DigiMove Analysis for Manufacturing SMEs to Identify Their Current Status and next Digitalisation Steps. In Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2021; SciTePress: Valetta, Malta, 2021; pp. 59–66.
68.
Schuh G, Anderl R, Gausemeier J, Ten Hompel M, Wahlster W. Industrie 4.0 Maturity Index. In Managing the Digital Transformation of Companies;. acatech STUDY, Herbert Utz Verlag: Munich, Germany, 2017; ISSN 2192-6174.
69.
Senna PP, Barros AC, Bonnin Roca J, Azevedo A. Development of a digital maturity model for Industry 4.0 based on the technology-organization-environment framework. Comput. Ind. Eng. 2023, 185, 109645. [Google Scholar]
70.
Semeraro C, Alyousuf N, Kedir NI, Lail EA. A maturity model for evaluating the impact of Industry 4.0 technologies and principles in SMEs. Manuf. Lett. 2023, 37, 61–65. [Google Scholar]
71.
Schumacher A, Nemeth T, Sihn W. Roadmapping towards industrial digitalization based on an Industry 4.0 maturity model for manufacturing enterprises. Procedia CIRP 2019, 79, 409–414. [Google Scholar]
72.
Simetinger F, Basl J. A pilot study: An assessment of manufacturing SMEs using a new Industry 4.0 Maturity Model for Manufacturing Small- and Middle-sized Enterprises (I4MMSME). Procedia Comput. Sci. 2022, 200, 1068–1077. [Google Scholar]
73.
Steinlechner M, Schumacher A, Fuchs B, Reichsthaler L, Schlund S. A maturity model to assess digital employee competencies in industrial enterprises.  Procedia CIRP 2021, 104, 1185–1190. [Google Scholar]
74.
Weber C, Königsberger J, Kassner L, Mitschang B. M2DDM—A Maturity Model for Data-Driven Manufacturing.  Procedia CIRP 2017, 63, 173–178. [Google Scholar]
75.
de Bruin T, Freeze R, Kulkarni U, Rosemann M. Understanding the Main Phases of Developing a Maturity Assessment Model. 2005. Available online: https://eprints.qut.edu.au/25152/ (accessed on 15 May 2018).
76.
Becker J, Knackstedt R, Pöppelbuß J. Developing Maturity Models for IT Management. Bus. Inf. Syst. Eng. 2009, 1, 213–222. [Google Scholar]
77.
Poeppelbuss J, Roeglinger M. What makes a useful maturity model? A framework of general design principles for maturity models and its demonstration in business process management. In ESIC 2011 Proceedings; AIS elibrary: Helsinki, Finland, 9–11 June 2011. https://aisel.aisnet.org/ecis2011/28/
78.
Saari L, Kuusisto O, Häikiö J. ManuMaturity—The Maturity Tool for Manufacturing Companies to Reach beyond Industry 4.0.; VTT Technical Research Centre of Finland: Espoo, Finland, 2021.
79.
Neff AA, Hamel F, Herz TP, Uebernickel F, Brenner W, vom Brocke J. Developing a maturity model for service systems in heavy equipment manufacturing enterprises. Inf. Manag. 2014, 51, 895–911. [Google Scholar]
80.
Paasi J. Towards a New Era in Manufacturing Final Report of VTT’ s for Industry Spearhead Programme; VTT Technical Research Centre of Finland: Tampere, Finland, 2017.
81.
Kuusisto O, Kääriäinen J, Hänninen K, Saarela M. Towards a Micro-Enterprise–Focused Digital Maturity Framework. Int. J. Innov. Digit. Econ. 2020, 12, 72–85. [Google Scholar]
82.
Santos RC, Martinho JL. An Industry 4.0 maturity model proposal. J. Manuf. Technol. Manag. 2020, 31, 1023–1043. [Google Scholar]
83.
Kääriäinen J, Saari L, Tihinen M, Perätalo S. Supporting the digital transformation of SMEs—trained digital evangelists facilitating the positioning phase. Int. J. Inf. Syst. Proj. Manag. 2023, 11, 5–27. [Google Scholar]
TOP