Research Article

Technology prioritization framework to adapt maintenance legacy systems for Industry 4.0 requirement: an interoperability approach

André Luiz Alcântara Castilho Venâncio; Eduardo de Freitas Rocha Loures; Fernando Deschamps; Alvaro dos Santos Justus; Alysson Felipe Lumikoski; Guilherme Louro Brezinski

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Paper aims: Aiming to avoid an inefficient digital transformation, the present work proposes a framework that will provide companies with a strategy to implement technologies to legacy systems of maintenance.

Originality: Such a framework was produced through a series of strategic analyses using multicriteria decision-making (MCDM) methods.

Research method: These analyses are composed of three steps. First, reviewing the literature of industry 4.0 and interoperability, combining the RAMI4.0 architecture and Framework for Enterprise Interoperability (FEI). Second, by exploring technics of maturity assessments, addressing systems attributes and requirements. Third, reviewing the literature of Total Productive Maintenance (TPM) and recent maintenance technologies applications.

Main findings: The results confirm that such a framework can support the adequacy of legacy systems that are part of digital transformation projects.

Implications for theory and practice: To test the proposed framework, a multinational industrial entity belonging to the automotive sector was selected for a case study.


Industry 4.0, Industrial maintenance, Multicriteria Decision-Making (MCDM) methods, Interoperability, Digital transformation


Acatech. (2017). Industrie 4.0 Maturity Index. Munich: Acatech – National Academy of Science and Engineering.

Ahuja, I. P. S., & Khamba, J. S. (2008). Total productive maintenance: literature review and directions. International Journal of Quality & Reliability Management, 25(7), 709-756.

Alcácer, V., & Cruz-Machado, V. (2019). Scanning the Industry 4.0: a literature review on technologies for manufacturing systems. Engineering Science and Technology an International Journal, 22(3), 899-919.

Athawale, V. M., Chatterjee, P., & Chakraborty, S. (2012). Decision making for facility location selection using PROMETHEE II method. International Journal of Industrial and Systems Engineering, 11(1/2), 16-30.

Baidya, R., & Ghosh, S. K. (2015). Model for a predictive maintenance system effectiveness using the analytical hierarchy process as analytical tool. IFAC-PapersOnLine, 48(3), 1463-1468.

Banihabib, M. E., Hashemi-Madani, F. S., & Forghani, A. (2017). Comparison of compensatory and non-compensatory multi criteria decision making models in water resources strategic management. Water Resources Management, 31(12), 3745-3759.

Batlajery, B. V., Khadka, R., Saeidi, A. M., Jansen, S., & Hage, J. (2014). Industrial perception of legacy software system and their modernization (Technical Report Series). Utrecht: Department of Information and Computing Sciences, Utrecht University.

Battirola Filho, J. C. B., Piechnicki, F., Loures, E. D. F. R., & Santos, E. A. P. (2017). Process-Aware FMEA framework for failure analysis in maintenance. Journal of Manufacturing Technology Management, 28(6), 822-848.

Behera, P. K., & Sahoo, B. S. (2016). Leverage of multiple predictive maintenance technologies in root cause failure analysis of critical machineries. Procedia Engineering, 144, 351-359.

Borangiu, T., Morariu, O., Răileanu, S., Trentesaux, D., Leitão, P., & Barata, J. (2020). Digital transformation of manufacturing. Industry of the future with cyber-physical production systems. Romanian Journal of Information Science and Technology, 23(1), 3-37.

Botta, A., Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and internet of things: a survey. Future Generation Computer Systems, 56, 684-700.

Brans, J. P., & Mareschal, B. (2005). Promethee Methods. In J. Figueira, S. Greco, & M. Ehrogott (Eds.), Multiple Criteria Decision Analysis: State of the Art Surveys (International Series in Operations Research & Management Science, Vol. 78). New York: Springer.

Brooke, C., & Ramage, M. (2001). Organisational scenarios and legacy systems. International Journal of Information Management, 21(5), 365-384.

Cañas, H., Mula, J., Díaz-Madroñero, M., & Campuzano-Bolarín, F. (2021). Implementing Industry 4.0 principles. Computers & Industrial Engineering, 158, 107379.

Capgemini Consulting. (2014). Industry 4.0: The Capgemini Consulting view: sharpening the picture beyond the hype. Paris: Capgemini.

Carvalho, N., Chaim, O., Cazarini, E., & Gerolamo, M. (2018). Manufacturing in the fourth industrial revolution: a positive prospect in Sustainable Manufacturing. Procedia Manufacturing, 21, 671-678.

Chen, D., & Daclin, N. (2006). Framework for Enterprise Interoperability. In H. Panetto & N. Boudjlida (Eds.), Proceedings of the Workshops and the Doctorial Symposium of the Second IFAC/IFIP I-ESA International Conference (pp. 77-88). London, UK: ISTE.

Chen, D., Dassisti, M., & Elvesæter, B. (2007). Enterprise Interoperability Framework and knowledge corpus. In Interoperability research for networked enterprises applications and software (pp. 1-44). Bordeaux: CNRS, IMS-Bordeaux.

Chen, Y. (2017). Integrated and intelligent manufacturing: perspectives and enablers. Engineering, 3(5), 588-595.

Cisco. (2015). The digital manufacturer resolving the service dilemma. San Jose: Cisco.

Cleland-Huang, J. (2007). Quality requirements and their role in successful products jane. In A. Sutcliffe & P. Jalote (Eds.), 15th IEEE International Requirements Engineering Conference (pp. 361). Los Alamitos, CA: IEEE Computer Science.

Cupek, R., Drewniak, M., Ziebinski, A., & Fojcik, M. (2019). “Digital Twins” for highly customized electronic devices-case study on a rework operation. IEEE Access: Practical Innovations, Open Solutions, 7, 164127-164143.

Darko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E. (2020). Artificial intelligence in the AEC industry: scientometric analysis and visualization of research activities. Automation in Construction, 112(January), 103081.

Deac, V., Cârstea, G., Bâgu, C., & Pârvu, F. (2010). The modern approach to industrial maintenance management. Informatica Economica Journal, 14(2), 133-144.

Deloitte. (2015). Industry 4.0: Challenges and solutions for the digital transformation and use of exponential technologies. London: Deloitte.

Dhillon, B. S. (2002). Engineering maintenance: a modern approach. Boca Raton: CRC Press.

Efthymiou, K., Papakostas, N., Mourtzis, D., & Chryssolouris, G. (2012). On a predictive maintenance platform for production systems. Procedia CIRP, 3(1), 221-226.

Elbok, G., & Berrado, A. (2020). Project prioritization for portfolio selection using MCDA. In Proceedings of the International Conference on Industrial Engineering and Operations Management (pp. 2317–2326). Michigan, USA: IEOM Society International.

Erasmus, J., Vanderfeesten, I., Traganos, K., Keulen, R., & Grefen, P. (2020). The HORSE project: the application of business process management for flexibility in smart manufacturing. Applied Sciences, 10(12), 4145.

Forman, E., & Peniwati, K. (1998). Aggregating individual judgments and priorities with the Analytic Hierarchy Process. European Journal of Operational Research, 108(1), 165-169.

Furch, J., Turo, T., Krobot, Z., & Stastny, J. (2018). Using Telemetry for Maintenance of Special Military Vehicles. In: J. Mazal (Eds.), Modelling and Simulation for Autonomous Systems (Lecture Notes in Computer Science, Vol. 10756).

Gallegos-Baeza, D., Caro, A., Rodríguez, A., & Velásquez, I. (2021). Aligning business strategy and information technologies in local governments using enterprise architectures. Information Development. In press.

Jantunen, E., Emmanouilidis, C., Arnaiz, A., & Gilabert, E. (2011). e-Maintenance: trends, challenges and opportunities for modern industry. IFAC Proceedings Volumes, 44(1), 453-458.

Justus, A. D. S., Ramos, L. F. P., & Loures, E. F. R. (2018). A capability assessment model of industry 4.0 technologies for viability analysis of poc (proof of concept) in an automotive company. Advances in Transdisciplinary Engineering, 7, 936-945.

Karim, R., Westerberg, J., Galar, D., & Kumar, U. (2016). Maintenance analytics: the new know in maintenance. IFAC-PapersOnLine, 49(28), 214-219.

Kodali, R., Mishra, R. P., & Anand, G. (2009). Justification of world-class maintenance systems using analytic hierarchy constant sum method. Journal of Quality in Maintenance Engineering, 15(1), 47-77.

Kozma, D., Varga, P., & Larrinaga, F. (2021). System of systems lifecycle management—a new concept based on process engineering methodologies. Applied Sciences, 11(8), 3386.

Kumar, A., Shankar, R., & Thakur, L. S. (2018). A big data driven sustainable manufacturing framework for condition-based maintenance prediction. Journal of Computational Science, 27, 428-439.

Lamine, E., Guédria, W., Rius Soler, A., Ayza Graells, J., Fontanili, F., Janer-García, L., & Pingaud, H. (2017). An inventory of interoperability in healthcare ecosystems: Characterization and challenges. In B. Archimède & B. Vallespir (Eds.), Enterprise Interoperability: INTEROP-PGSO Vision (Vol. 1, pp. 167-198): Hoboken, NJ: Wiley/ISTE

Laney, D. (2001). Evidence of two effects in the size segregation process in dry granular media. Physical Review E, 70(5), 051307.

Lazai Junior, M., Loures, E. F. R., Santos, E. A. P., & Szejka, A. L. (2020). Avaliação da gestão da segurança funcional de máquinas na indústria automotiva sob a ótica da interoperabilidade. Brazilian Journal of Development, 6(1), 3009-3023.

Liou, J. J. H., Lu, M. T., Hu, S. K., Cheng, C. H., & Chuang, Y. C. (2017). A hybrid MCDM model for improving the electronic health record to better serve client needs. Sustainability, 9(10), 1819.

Liu, K., Alderson, A., Sharp, B., Shah, H., & Dix, A. (1998). Using semiotic techniques to derive requirements from legacy systems. In: First SEBPC Legacy Workshop. Durham: Durham University.

Matsumoto, T., Chen, Y., Nakatsuka, A., & Wang, Q. (2020). Research on horizontal system model for food factories: a case study of process cheese manufacturer. International Journal of Production Economics, 226, 107616.

McKinsey & Company. (2016). Industry 4.0 at McKinsey' s model factories. Chicago: McKinsey & Company.

Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018). The industrial management of SMEs in the era of Industry 4.0. International Journal of Production Research, 56(3), 1118-1136.

Morariu, O., Borangiu, T., Raileanu, S., & Morariu, C. (2016). Redundancy and scalability for virtualized MES systems with programmable infrastructure. Computers in Industry, 81, 26-35.

Muller, A., Crespo Marquez, A., & Iung, B. (2008). On the concept of e-maintenance: review and current research. Reliability Engineering & System Safety, 93(8), 1165-1187.

Oztemel, E., & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1), 127-182.

Patalas-Maliszewska, J., & Skrzeszewska, M. (2018). An Evaluation of the effectiveness of applying the mes in a maintenance department: a case study. Foundations of Management, 10(1), 257-270.

Pedone, G., & Mezgár, I. (2018). Model similarity evidence and interoperability affinity in cloud-ready Industry 4.0 technologies. Computers in Industry, 100, 278-286.

Pintelon, L., & Parodi-herz, A. (2008). Maintenance: an evolutionary perspective. In K. A. H. Kobacy & D. N. P. Murthy (Eds.), Complex system maintenance handbook. London: Springer.

Plattform Industrie 4.0. (2015). Reference architectural model Industrie 4.0 (RAMI 4.0): an introduction. Berlin: Plattform Industrie 4.0.

PWC (2015). The smart manufacturing industry: the industrial internet creates new opportunities for swedish manufacturing companies. Retrieved in 30 April 2021, from

PWC. (2016). Industry 4.0: Building the digital enterprise. Berlin: PwC.

Ramage, M. (2000). Global perspectives on legacy systems. In: P. Henderson (Ed.), Systems engineering for business process change: new directions: collected papers from the EPSRC research programme (pp. 309-316). London, UK: Springer

Rojko, A. (2017). Industry 4.0 concept: background and overview. International Journal of Interactive Mobile Technologies, 11(5), 77-90.

Roland Berger. (2014). Automotive Insights. Munich: Roland Berger Strategy Consultants. Retrieved in 30 April 2021, from

Ruschel, E., Santos, E. A. P., & Loures, E. F. R. (2017). Industrial maintenance decision-making: a systematic literature review. Journal of Manufacturing Systems, 45, 180-194.

Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: the future of productivity and growth in manufacturing industries. The Boston Consulting, 9(1), 54-89.

Saaty, R. W. (1987). The analytic hierarchy process-what and how it is used. Mathematical Modelling, 9(3–5), 161-176.

Santos, M. M., Resende, D., Garzedin, O., Portugal, P., & Vasques, F. (2009). Technical and economical assessment of the use of wireless gateways in industrial networks. In 35th Annual Conference of IEEE Industrial Electronics (pp, 2499–2504). Piscataway, NJ: IEEE.

Schmidt, B., Wang, L., & Galar, D. (2017). Semantic framework for predictive maintenance in a cloud environment. Procedia CIRP, 62, 583-588.

Sipsas, K., Alexopoulos, K., Xanthakis, V., & Chryssolouris, G. (2016). Collaborative maintenance in flow-line manufacturing environments: an Industry 4.0 approach. Procedia CIRP, 55, 236-241.

Sotnyk, I., Zavrazhnyi, K., Kasianenko, V., Roubík, H., & Sidorov, O. (2020). Investment management of business digital innovations. Marketing and Management of Innovations, 6718(1), 95-109.

Ssebuggwawo, D., Hoppenbrouwers, S., & Proper, E. (2009). Group decision making in collaborative modeling: aggregating individual preferences with AHP. In B. van Dongen & H. Reijers (Eds.), Proceedings of the 4th SIKS/BENAIS Conference on Enterprise Information Systems (EIS 2009). Aachen:

Tao, F., & Qi, Q. (2019). New IT driven service-oriented smart manufacturing: Framework and characteristics. IEEE Transactions on Systems, Man, and Cybernetics. Systems, 49(1), 81-91.

The Warwick Manufacturing Group. (2017). An Industry 4 readiness assessment tool. Warmwick: WMG.

Vaisnys, P., Contri, P., Rieg, C., & Bieth, M. (2006). Monitoring the effectiveness of maintenance programs through the use of performance indicators. Safety of Eastern European Type Nuclear Facilities. Retrieved in 30 April 2021, from

Wang, Y., Gogu, C., Binaud, N., Bes, C., Haftka, R. T., & Kim, N. H. (2017). A cost driven predictive maintenance policy for structural airframe maintenance. Chinese Journal of Aeronautics, 30(3), 1242-1257.

Welz, Z., Coble, J., Upadhyaya, B., & Hines, W. (2017). Maintenance-based prognostics of nuclear plant equipment for long-term operation. Nuclear Engineering and Technology, 49(5), 914-919.

Wiech, M., Boffelli, A., Elbe, C., Carminati, P., Friedli, T., & Kalchschmidt, M. (2022). Implementation of big data analytics and Manufacturing Execution Systems: an empirical analysis in German-speaking countries. Production Planning and Control, 33(2-3), 261-276.

Wintrich, N., Gering, P., Meissner, M. (2015). Integrated Process Oriented Requirements Management. In: C. Debruyne, H. Panetto, R. Meersman, T. Dillon, G. Weichhart, Y. An, C. A. Ardagna (Eds.), On the Move to Meaningful Internet Systems: OTM 2015 Conferences (Lecture Notes in Computer Science, Vol. 9415). Berlin: Springer.

Woodhead, R., Stephenson, P., & Morrey, D. (2018). Digital construction: From point solutions to IoT ecosystem. Automation in Construction, 93, 35-46.

Yam, R. C. M., Tse, P. W., Li, L., & Tu, P. (2001). Intelligent predictive decision support system for condition-based maintenance. International Journal of Advanced Manufacturing Technology, 17(5), 383-391.

Yokoyama, A. (2015). Innovative changes for maintenance of railway by using ICT-To achieve “smart Maintenance.”. Procedia CIRP, 38, 24-29.

Yu, Y., Zhang, J. Z., Cao, Y., & Kazancoglu, Y. (2021). Intelligent transformation of the manufacturing industry for Industry 4.0: Seizing financial benefits from supply chain relationship capital through enterprise green management. Technological Forecasting and Social Change, 172, 120999.

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