Digital Maturity Assessment Model for the Organizational and Process Dimensions
Abstract
:1. Introduction
- Identification of assessed areas of digital maturity in models described in the literature in the last decade.
- Development of a framework for a digital maturity assessment model, considering two levels of analysis.
- Proposing a five-step scale for evaluating the level of digital maturity for defined areas of digital maturity assessments.
- Formulating the stages of the procedure for using the maturity assessment model to improve the digital transformation in organizations.
- Developing a diagnostic tool to audit the current state.
- Implementation of the proposed solution in the selected company.
2. Methodology
- QR1: What ranges of an organization’s digital maturity are analyzed in the models for evaluating the level of digital transformation described in the literature in the last decade?
- QR2: Which areas of digital transformation concern the level of the organization, and which can be implemented and should be assessed at the level of the process/spheres of activity?
- QR3: How can one assess the levels of digital transformation in the organizational dimension and process dimensions?
- QR4: How can one conduct a maturity assessment process for active digital transformation in an organization?
- QR5: Can the company independently carry out an assessment based on the developed digital maturity model?
TITLE-ABS-KEY: “digital transformation assessment” OR “Industry 4.0 maturity assessment” OR “digital maturity assessment” OR “digital maturity model” |
3. Results—Literature Review
3.1. Guidelines for Creating a Maturity Model
- Maturity levels—models are typically built on five levels, with 1 being the lowest; in some models, level 0 is also considered.
- Dimensions—maturity is usually assessed in 4 to 16 dimensions.
- Assessment mode—self-assessment or by an external auditor.
- Representation method—numerical representation, commonly visualized using radar charts.
3.2. Digital Transformation Maturity Models
- They are built on the same or similar principles as CMMI (Capability Maturity Model Integration).
- The analyzed parameters are always areas for evaluation and graded levels.
- Assessment is made in different areas/dimensions (both expressions are used interchangeably); for some models, the areas are further extended to sub-areas.
- Assessment levels are arranged logically from the lowest to the highest level. Each level has its name and characteristics for the requirements and properties to meet a given level within a given area.
- For some models, company readiness is expressed quantitatively as a readiness index.
- Many DMMs lack the required methodological rigor, as they are more practical than academic in nature.
- Most DMMs evaluate areas that have not been empirically verified, which raises questions about their relevance and fit to the organization’s needs.
- Many DMMs are based on the assumption of linear evolution occurring in the digital transformation process and ignore industry and organizational specifics, which many authors criticize.
- Technology and Operations (n = 17);
- People and Products (n = 16);
- Strategy (n = 15) and Governance (n = 14).
4. Results—Model Development
- Dimension—refers to the level of the organization affected by digital transformation. The model distinguishes two dimensions: (1) an organization dimension and (2) a process dimension. The organizational evaluation applies to the entire enterprise. The assessment in the process dimension refers to a specific operational division, which may be, for example, production or logistics.
- Area—refers to the scope of activity that is the subject of the assessment; the model also assumes the introduction of sub-areas that may specify the content of the evaluation.
- Level—the level of implementation of the assumptions regarding fulfilling the requirements applicable to digital readiness.
- Data management.
- Cyber security.
- Performance management.
- Processes management.
- Supporting employees’ activities through digital solutions.
- Employee behavior.
- Development of employees’ competencies.
- AMLS—sub-area/area maturity level.
- DML—dimension maturity level.
- Xk—positively verified question for a given sub-area/area.
- n—number of questions in the set for a given sub-area/area.
- N—number of sub-area/areas in dimension assessed.
5. Results—Implementation in a Selected Company
5.1. The Use of DMM-OP to Assess Digital Transformation in a Manufacturing Company
5.2. Model Verification and Validation
- Unambiguity of terms used in research tools and their complexity level.
- Completeness and validity of the areas adopted in the model for assessment.
- Understanding of the procedure for the assessment team.
- Correctness of the adopted maturity assessment levels for individual areas.
- VQ1: Does DMM-OP meet the requirements of managers regarding the tool assessing the current level of digital transformation in their organization?
- VQ2: Does DMM-OP meet the requirements of managers regarding the further framework for digital transformation being formulated for their organizations and possible directions of change?
6. Discussion
- Level 1 focuses on the basic potential of the enterprise and its readiness to join the digital transformation.
- Level 2 checks the level of standardization and analytical potential of the company.
- Level 3 focuses on internal integration regarding processes, data, and employee readiness.
- Level 4 takes digitization processes outside the enterprise and checks the level of digitization in cooperation with business partners.
- Level 5 is focused on using the best practices related to digital transformation, supported by the latest technological developments.
7. Conclusions
Supplementary Materials
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Matt, D.T.; Pedrini, G.; Bonfanti, A.; Orzes, G. Industrial Digitalization. A Systematic Literature Review and Research Agenda. Eur. Manag. J. 2022, 41, 47–78. [Google Scholar] [CrossRef]
- Legner, C.; Eymann, T.; Hess, T.; Matt, C.; Böhmann, T.; Drews, P.; Mädche, A.; Urbach, N.; Ahlemann, F. Digitalization: Opportunity and Challenge for the Business and Information Systems Engineering Community. Bus. Inf. Syst. Eng. 2017, 59, 301–308. [Google Scholar] [CrossRef]
- Alsufyani, N.; Gill, A.Q. Digitalisation Performance Assessment: A Systematic Review. Technol. Soc. 2022, 68, 101894. [Google Scholar] [CrossRef]
- Brunetti, F.; Matt, D.T.; Bonfanti, A.; De Longhi, A.; Pedrini, G.; Orzes, G. Digital Transformation Challenges: Strategies Emerging from a Multi-Stakeholder Approach. TQM J. 2020, 32, 697–724. [Google Scholar] [CrossRef]
- Hein-Pensel, F.; Winkler, H.; Brückner, A.; Wölke, M.; Jabs, I.; Mayan, I.J.; Kirschenbaum, A.; Friedrich, J.; Zinke-Wehlmann, C. Maturity Assessment for Industry 5.0: A Review of Existing Maturity Models. J. Manuf. Syst. 2023, 66, 200–210. [Google Scholar] [CrossRef]
- Meng, F.; Wang, W. Research on the Mechanism of Digitalization to the Improvement of Manufacturing Enterprises Performance Based on Mediating Effect. In Proceedings of the 2020 6th International Conference on Information Management (ICIM), London, UK, 27–29 March 2020; pp. 122–126. [Google Scholar]
- Thordsen, T.; Murawski, M.; Bick, M. How to Measure Digitalization? A Critical Evaluation of Digital Maturity Models. In Responsible Design, Implementation and Use of Information and Communication Technology. I3E 2020; Lecture Notes in Computer Science; Hattingh, M., Matthee, M., Smuts, H., Pappas, I., Dwivedi, Y., Mäntymäki, M., Eds.; Springer: Berlin/Heidelberg, Germany, 2020; Volume 12066, pp. 358–369. [Google Scholar]
- Remane, G.; Hanelt, A.; Wiesboeck, F.; Kolbe, L. Digital Maturity in Traditional Industries—An Exploratory Analysis. In Proceedings of the 25th European Conference on Information Systems (ECIS), Guimarães, Portugal, 5–10 June 2017. [Google Scholar]
- Tubis, A.A.; Grzybowska, K.; Król, B. Supply Chain in the Digital Age: A Scientometric–Thematic Literature Review. Sustainability 2023, 15, 11391. [Google Scholar] [CrossRef]
- Parhi, S.; Joshi, K.; Gunasekaran, A.; Sethuraman, K. Reflecting on an Empirical Study of the Digitalization Initiatives for Sustainability on Logistics: The Concept of Sustainable Logistics 4.0. Clean. Logist. Supply Chain 2022, 4, 100058. [Google Scholar] [CrossRef]
- Lee, S.-Y. Sustainable Supply Chain Management, Digital-Based Supply Chain Integration, and Firm Performance: A Cross-Country Empirical Comparison between South Korea and Vietnam. Sustainability 2021, 13, 7315. [Google Scholar] [CrossRef]
- Schilling, L.; Seuring, S. Sustainable Value Creation through Information Technology-Enabled Supply Chains in Emerging Markets. Int. J. Logist. Manag. 2022, 33, 1001–1016. [Google Scholar] [CrossRef]
- Di Vaio, A.; Latif, B.; Gunarathne, N.; Gupta, M.; D’Adamo, I. Digitalization and Artificial Knowledge for Accountability in SCM: A Systematic Literature Review. J. Enterp. Inf. Manag. 2023. ahead-of-print. [Google Scholar] [CrossRef]
- Perera, S.; Jin, X.; Das, P.; Gunasekara, K.; Samaratunga, M. A Strategic Framework for Digital Maturity of Design and Construction through a Systematic Review and Application. J. Ind. Inf. Integr. 2023, 31, 100413. [Google Scholar] [CrossRef]
- Schumacher, A.; Erol, S.; Sihn, W. A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia CIRP 2016, 52, 161–166. [Google Scholar] [CrossRef]
- Antony, J.; Sony, M.; McDermott, O. Conceptualizing Industry 4.0 Readiness Model Dimensions: An Exploratory Sequential Mixed-Method Study. TQM J. 2023, 35, 577–596. [Google Scholar] [CrossRef]
- Haryanti, T.; Rakhmawati, N.A.; Subriadi, A.P. The Extended Digital Maturity Model. Big Data Cogn. Comput. 2023, 7, 17. [Google Scholar] [CrossRef]
- Ehie, I.; Ferreira, L.M.D.F. Conceptual Development of Supply Chain Digitalization Framework. IFAC-PapersOnLine 2019, 52, 2338–2342. [Google Scholar] [CrossRef]
- Ho, W.R.; Tsolakis, N.; Dawes, T.; Dora, M.; Kumar, M. A Digital Strategy Development Framework for Supply Chains. IEEE Trans. Eng. Manag. 2022, 70, 2493–2506. [Google Scholar] [CrossRef]
- Klötzer, C.; Pflaum, A. Toward the Development of a Maturity Model for Digitalization within the Manufacturing Industry’s Supply Chain. In Proceedings of the Hawaii International Conference on System Sciences, Hilton Waikoloa Village, HI, USA, 4–7 January 2017. [Google Scholar]
- Hellweg, F.; Lechtenberg, S.; Hellingrath, B.; Thomé, A.M.T. Literature Review on Maturity Models for Digital Supply Chains. Braz. J. Oper. Prod. Manag. 2021, 18, 1–12. [Google Scholar] [CrossRef]
- Zoubek, M.; Simon, M. Evaluation of the Level and Readiness of Internal Logistics for Industry 4.0 in Industrial Companies. Appl. Sci. 2021, 11, 6130. [Google Scholar] [CrossRef]
- Oleśków-Szłapka, J.; Stachowiak, A. The Framework of Logistics 4.0 Maturity Model. In Intelligent Systems in Production Engineering and Maintenance. ISPEM 2018. Advances in Intelligent Systems and Computing; Burduk, A., Chlebus, E., Nowakowski, T., Tubis, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2019; Volume 835, pp. 771–781. [Google Scholar]
- Facchini, F.; Oleśków-Szłapka, J.; Ranieri, L.; Urbinati, A. A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research. Sustainability 2019, 12, 86. [Google Scholar] [CrossRef]
- Rojko, A. Industry 4.0 Concept: Background and Overview. Int. J. Interact. Mob. Technol. (iJIM) 2017, 11, 77. [Google Scholar] [CrossRef]
- Carrijo, P.; Alturas, B.; Pedrosa, I. Similarities and Differences Between Digital Transformation Maturity Models: A Literature Review. In Ntelligent Systems in Digital Transformation; Lecture Notes in Networks and Systems; Kahraman, C., Haktanır, E., Eds.; Springer: Berlin/Heidelberg, Germany, 2023; Volume 549, pp. 33–52. [Google Scholar]
- Van Looy, A. Business Process Maturity. A Comparative Study on a Sample of Business Process Maturity Models; vom Brocke, J., Liechtenstein, V., Eds.; Springer: Berlin/Heidelberg, Germany, 2014; ISBN 9783319160702. [Google Scholar]
- Angreani, L.S.; Vijaya, A.; Wicaksono, H. Systematic Literature Review of Industry 4.0 Maturity Model for Manufacturing and Logistics Sectors. Procedia Manuf. 2020, 52, 337–343. [Google Scholar] [CrossRef]
- Proença, D.; Borbinha, J. Maturity Models for Information Systems—A State of the Art. Procedia Comput. Sci. 2016, 100, 1042–1049. [Google Scholar] [CrossRef]
- Kosieradzka, A.; Smagowicz, J. Concept for a Pilot Study to Verify a Maturity Model in the Area of Public Crisis Management. Zesz. Nauk. Politech. Poznańskiej Organ. I Zarządzanie 2018, 77, 127–143. (In Polish) [Google Scholar] [CrossRef]
- Chrapko, M. CMMI. Improving Processes in an Organisation; Polish Scientific Publishing House: Warsaw, Poland, 2010. (In Polish) [Google Scholar]
- Gray, J.; Rumpe, B. Models for the Digital Transformation. Softw. Syst. Model. 2017, 16, 307–308. [Google Scholar] [CrossRef]
- Almeida, F.; Duarte Santos, J.; Augusto Monteiro, J. The Challenges and Opportunities in the Digitalization of Companies in a Post-COVID-19 World. IEEE Eng. Manag. Rev. 2020, 48, 97–103. [Google Scholar] [CrossRef]
- Amankwah-Amoah, J.; Khan, Z.; Wood, G.; Knight, G. COVID-19 and Digitalization: The Great Acceleration. J. Bus. Res. 2021, 136, 602–611. [Google Scholar] [CrossRef] [PubMed]
- How COVID-19 Has Pushed Companies over the Technology Tipping Point—And Transformed Business Forever. 2020. Available online: https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/how-covid-19-has-pushed-companies-over-the-technology-tipping-point-and-transformed-business-forever#/ (accessed on 15 June 2023).
- Wiesner, S.; Gaiardelli, P.; Gritti, N.; Oberti, G. Maturity Models for Digitalization in Manufacturing—Applicability for SMEs. In Advances in Production Management Systems. Smart Manufacturing for Industry 4.0. APMS 2018. IFIP Advances in Information and Communication Technology; Moon, I., Lee, G., Park, J., Kiritsis, D., von Cieminski, G., Eds.; Springer: Berlin/Heidelberg, Germany, 2018; Volume 536, pp. 81–88. [Google Scholar]
- Oleśków-Szłapka, J.; Wojciechowski, H.; Domański, R.; Pawłowski, G. Logistics 4.0 Maturity Levels Assessed Based on GDM (Grey Decision Model) and Artificial Intelligence in Logistics 4.0 -Trends and Future Perspective. Procedia Manuf. 2019, 39, 1734–1742. [Google Scholar] [CrossRef]
- Werner-Lewandowska, K.; Kosacka-Olejnik, M. Logistics 4.0 Maturity in Service Industry: Empirical Research Results. Procedia Manuf. 2019, 38, 1058–1065. [Google Scholar] [CrossRef]
- Zoubek, M.; Šimon, M. Methodology for Evaluating the Readiness of Internal Logistics Processes for Industry 4.0. IOP Conf. Ser. Mater. Sci. Eng. 2020, 947, 012005. [Google Scholar] [CrossRef]
- 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] [CrossRef]
- Tonelli, F.; Demartini, M.; Loleo, A.; Testa, C. A Novel Methodology for Manufacturing Firms Value Modeling and Mapping to Improve Operational Performance in the Industry 4.0 Era. Procedia CIRP 2016, 57, 122–127. [Google Scholar] [CrossRef]
- Jochem, R.; Geers, D.; Heinze, P. Maturity Measurement of Knowledge-intensive Business Processes. TQM J. 2011, 23, 377–387. [Google Scholar] [CrossRef]
- Comuzzi, M.; Patel, A. How Organisations Leverage Big Data: A Maturity Model. Ind. Manag. Data Syst. 2016, 116, 1468–1492. [Google Scholar] [CrossRef]
- Schumacher, A. Development of a Maturity Model for Assessing the Industry 4.0 Maturity of Industrial Enterprises. Ph.D. Thesis, Vienna University of Technology, Wien, Austria, 2015. [Google Scholar]
- da Silva, I.A.; Barbalho, S.C.M.; Adam, T.; Heine, I.; Schmitt, R. Industry 4.0 Maturity Models: A Bibliometric Study of Scientific Articles from 2001 to 2018. In Proceedings of the 26th EurOMA Conference Operations Adding Value to Society, Helsinki, Finland, 17–19 June 2019; pp. 1829–1838. [Google Scholar]
- Bettoni, A.; Matteri, D.; Montini, E.; Gładysz, B.; Carpanzano, E. An AI Adoption Model for SMEs: A Conceptual Framework. IFAC-PapersOnLine 2021, 54, 702–708. [Google Scholar] [CrossRef]
- Yams, N.B.; Richardson, V.; Shubina, G.E.; Albrecht, S.; Gillblad, D. Integrated AI and Innovation Management: The Beginning of a Beautiful Friendship. Technol. Innov. Manag. Rev. 2020, 10, 5–18. [Google Scholar] [CrossRef]
- Gökalp, E.; Şener, U.; Eren, P.E. Development of an Assessment Model for Industry 4.0: Industry 4.0-MM. In Software Process Improvement and Capability Determination. SPICE 2017. Communications in Computer and Information Science; Mas, A., Mesquida, A., O’Connor, R., Rout, T., Dorling, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2017; Volume 770, pp. 128–142. [Google Scholar]
- Sikhwal, R.K.; Childs, P.R.N. Towards Mass Individualisation: Setting the Scope and Industrial Implication. Des. Sci. 2021, 7, e16. [Google Scholar] [CrossRef]
- Heim, H. Digital Fashion Revolutions: Supply Chain Transparency, Digitalization and the Non-Disclosure Paradox. Fash. Pract. 2022, 14, 329–351. [Google Scholar] [CrossRef]
Characteristic | Description |
---|---|
Object of evaluation | The objects that are the subject of the maturity assessment, this may include technology, systems, people, project management, etc. |
Dimensions | Defined areas of the organization’s capabilities, which characterize the various elements of the maturity of the object to be assessed. These dimensions should be unambiguous and enable a comprehensive assessment to be made. |
Levels | The maturity state of the evaluated object; it usually includes five levels (Level 1—initial; Level 2—managed; Level 3—defined; Level 4—quantitatively managed; Level 5—in optimization). |
Maturity principles | There are two types of maturity models: (1) a continuous model, which evaluates based on the average level achieved in various dimensions, and (2) a staged model, in which all the elements of a given level must be achieved for the organization to move to a higher level of maturity. |
Assessment | Qualitative (interviews) or quantitative (questionnaires with Likert scales). |
Hein-Pensel et al. [5] | Schumacher et al. [44] | Angreani et al. [28] | Hellweg et al. [21] |
---|---|---|---|
Technology | Strategy | Strategy | Business |
Employees | Leadership | Leadership | Organization |
Data | Customers | Customers | Process & Method |
Organization and Processes | Products | Products | Technological |
Strategy and Management | Operations | Operations | |
Products and Services | Culture | Culture | |
Corporate Environment | People | People | |
Customer | Governance | Governance | |
Corporate Culture | Technology | Technology |
Levels | Description |
---|---|
Level 1—Basic | There is a low level of digitization in the area. IC technologies support some activities, but no centrally controlled system solutions exist. Individual innovative projects are implemented, but at the local level, without affecting other business areas. Digital transformation does not have the highest priority in the changes implemented in the organization. Employees need to feel the personal benefits of starting to use IC tools to support their work. Only relevant data are stored by the organization, but their collection is not continuous and is not always supported by IT solutions. The collected data are not the basis for systematic analyses to improve processes. |
Level 2—Discovery | The company is starting a process of changes aimed at digital transformation. It has a digital transformation plan and defined milestones. IT tools and standards supporting systematic data collection and analysis are being implemented. Employees are open to changes and digital innovations supporting operational processes. ICT tools and mobile devices support their work. The process of developing their digital competencies begins. The company implements continuous improvement and change management strategies. The processes in the company are standardized, and the fundamental processes have been digitized. The measurement of process efficiency based on procedures, indicators, and goals is also introduced. The enterprise provides business partners with critical data in digital form. |
Level 3—Developed | The company is focused on growth through its digital transformation. Critical data from various sources are integrated at the enterprise level, centrally collected, systematically analyzed using IT tools, and used to optimize processes within the enterprise. The data collection process is supported by sensors monitoring the process and machine operation, and cloud computing is used for data storage and distribution. Analytical tools support decision-making processes. Employees perceive digital technologies as a value supporting the implementation of processes and actively share information in interdisciplinary teams. |
Level 4—Integrated | The company has reached a high level of digital transformation. Modern technological solutions are used in the field of process automation as well as data analysis and management. These solutions are based on integrated IT platforms that support all processes carried out in the company. At the same time, information integration also applies to business partners, which means mutual sharing of data in real time and a coherent planning process based on data analysis from both partners’ systems. Autonomous devices are included in operational processes. Employees are focused on active human–machine cooperation and can manage the risks associated with digitization. |
Level 5—Leadership | The enterprise has reached the peak of digital transformation. The latest technological solutions in process automation and data analysis are used. Autonomous solutions support operational processes, the Internet of Things concept, and artificial intelligence. Employees have the required digital competencies, which are constantly developed and updated as part of training. The company continuously analyzes and evaluates current trends in digital transformation and implements best practices in its operations. At the same time, it is itself the initiator of many innovative solutions that promote and support digital transformation in the served market. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tubis, A.A. Digital Maturity Assessment Model for the Organizational and Process Dimensions. Sustainability 2023, 15, 15122. https://doi.org/10.3390/su152015122
Tubis AA. Digital Maturity Assessment Model for the Organizational and Process Dimensions. Sustainability. 2023; 15(20):15122. https://doi.org/10.3390/su152015122
Chicago/Turabian StyleTubis, Agnieszka A. 2023. "Digital Maturity Assessment Model for the Organizational and Process Dimensions" Sustainability 15, no. 20: 15122. https://doi.org/10.3390/su152015122