Production
https://www.prod.org.br/article/doi/10.1590/0103-6513.20220098
Production
Research Article

Digital transformation in maintenance: interoperability-based adequacy aiming smart legacy systems

André Luiz Alcântara Castilho Venâncio; Guilherme Louro Brezinski; Gabriel da Silva Serapião Leal; Eduardo de Freitas Rocha Loures; Fernando Deschamps

Downloads: 0
Views: 368

Abstract

Paper aims: The Industry 4.0 movement highlights the importance of information and communication technologies and the two main reasons for this are advances in digitalization and automation. However, organizations trying to implement technologies face several barriers in their systems. These barriers are intensified in legacy systems, tightly coupled to organizational processes. Thus, to overcome these barriers, an adequacy strategy was structured, and detailed in the context of interoperability.

Originality: This article proposes a digital transformation framework focused on interoperability in maintenance systems.

Research method: Aiming to make legacy systems work together with cyber-physical systems, the proposed framework suggests its suitability based on strategic decisions, using Multicriteria Decision Making (MCDM) methods.

Main findings: The framework outputs demonstrate that people are the main drivers of digital transformation and that the strategies proposed by it are coherent with the actual development of both cases, proven in a new interview one year apart from its application.

Implications for theory and practice: Real industrial cases demonstrate that the framework can guarantee interoperability while facilitating strategic decisions to implement technologies in legacy maintenance systems. In the end, the legacy system, which will interoperate with the new technologies, is called Smart Legacy System.

Keywords

Industry 4.0, Legacy system, Interoperability, Maintenance system, Multicriteria decision making

References

Alencar, L. H., Almeida, A. T., & Morais, D. C. (2010). A multicriteria group decision model aggregating the preferences of decision-makers based on electre methods. Pesquisa Operacional, 30(3), 687-702. http://dx.doi.org/10.1590/S0101-74382010000300010.

Auvray, J. (2018). Définition et mise en oeuvre de la transformation digitale au sein d’ une entreprise de type PMI/PME, ETI: proposition d’ une démarche d’ analyse et de transformation. France: Dépôt Universitaire de Mémoires Après Soutenance.

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.

Biahmou, A., Emmer, C., Pfouga, A., & Stjepandić, J. (2016). Digital master as enabler for Industry 4.0. Amsterdam: IOS Press. http://dx.doi.org/10.3233/978-1-61499-703-0-672.

Bokrantz, J., Skoogh, A., Berlin, C., & Stahre, J. (2017). Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030. International Journal of Production Economics, 191, 154-169. http://dx.doi.org/10.1016/j.ijpe.2017.06.010.

Boulton, B. C. (2019). Digital transformation drivers (pp. 1-7). Paris: Capgemini Research Institute.

Brans, J.-P., & Mareschal, B. (2005). PROMETHEE Methods. International Series in Operations Research and Management Science, 78, 163-186. http://dx.doi.org/10.1007/0-387-23081-5_5.

Brooke, C., & Ramage, M. (2001). Organisational scenarios and legacy systems. International Journal of Information Management, 21(5), 365-384. http://dx.doi.org/10.1016/S0268-4012(01)00023-8.

CBI. (2017). Disrupting the future how businesses can embrace artificial intelligence, blockchain and the internet of things (23 p.). London: CBI.

Chen, D., & Daclin, N. (2006). Framework for enterprise interoperability. In Proceedings of the Workshops and the Doctorial Symposium of the Second IFAC/IFIP I-ESA International Conference (pp. 77-88). Newport Beach: Wiley. http://dx.doi.org/10.1002/9780470612200.ch6.

Chen, D., Dassisti, M., & Elvesæter, B. (2007). Enterprise interoperability framework and knowledge corpus. In D. Chen, M. Dassisti, B. Elvesaeter, H. Panetto, N. Daclin, F. W. Jaekel, T. Knothe, A. Solberg, V. Anaya & R. Sanchis (Eds.), Interoperability research for networked enterprises applications and software (pp. 1-44). Bordeaux: CNRS, IMS-Bordeaux.

Cimitile, A., Fasolino, A.R., & Lanubile, F. (2001). Legacy systems assessment to support decision making. In IEEE Workshop on Empirical Studies of Software Maintenance. New York: IEEE.

Colombo, A. W., Karnouskos, S., Kaynak, O., Shi, Y., & Yin, S. (2017). Industrial cyberphysical systems: a backbone of the Fourth Industrial Revolution. IEEE Industrial Electronics Magazine, 11(1), 6-16. http://dx.doi.org/10.1109/MIE.2017.2648857.

Crotty, J., & Horrocks, I. (2017). Managing legacy system costs: a case study of a meta-assessment model to identify solutions in a large financial services company. Applied Computing and Informatics, 13(2), 175-183. http://dx.doi.org/10.1016/j.aci.2016.12.001.

Deloitte. (2018). The Industry 4.0 paradox (44 p.). New York.

Deloitte. (2019). Our digital future: a perspective for tax professionals. New York.

Di Matteo, U., Pezzimenti, P., & Astiaso Garcia, D. (2016). Methodological proposal for optimal location of emergency operation centers through multi-criteria approach. Sustainability, 8(1), 50. http://dx.doi.org/10.3390/su8010050.

European Union Agency for Cybersecurity – ENISA. (2019). ENISA lists high-level recommendations to different stakeholder groups in order to promote Industry 4.0 cybersecurity and facilitate wider take-up of relevant innovations in a secure manner. 2 Industry 4.0 cybersecurity: challenges & recommendations. Greece.

Fontana, M. E., & Cavalcante, C. A. V. (2013). Electre tri method used to storage location assignment into categories. Pesquisa Operacional, 33(2), 283-303. http://dx.doi.org/10.1590/S0101-74382013000200009.

Gudienė, N., Banaitis, A., Podvezko, V., & Banaitiene, N. (2014). Identification and evaluation of the critical success factors for construction projects in Lithuania: AHP approach. Journal of Civil Engineering and Management, 20(3), 350-359. http://dx.doi.org/10.3846/13923730.2014.914082.

Han, E. S., Goleman, D., Boyatzis, R., & McKee, A. (2017). Basic concepts of effectiveness. Journal of Chemical Information and Modeling, 53(9), 1689-1699.

Hashemi, H., Mousavi, S. M., Zavadskas, E. K., Chalekaee, A., & Turskis, Z. (2018). A new group decision model based on Grey-Intuitionistic Fuzzy-ELECTRE and VIKOR for contractor assessment problem. Sustainability, 10(5), 1635. http://dx.doi.org/10.3390/su10051635.

Huawei. (2017). +Intelligence: an engine driving industry digitalization. Retrieved in 2022, September 8, from http://www.huawei.com/minisite/gci/en/digital-spillover/files/gci_digital_spillover.pdf

Interactive and Reserved. (2018). Digital transformation: an IT pro’s guide. Retrieved in 2022, September 8, from http://b2b.cbsimg.net/downloads/Gilbert/TR_EB_digital_trans2.pdf

Jahedi, S., & Méndez, F. (2014). On the advantages and disadvantages of subjective measures. Journal of Economic Behavior & Organization, 98, 97-114. http://dx.doi.org/10.1016/j.jebo.2013.12.016.

Johnson, T., & Suhaib, S. (2009). Toward improved verification and certification of legacy systems. IFAC Proceedings Volumes, 42(5), 128-133. http://dx.doi.org/10.3182/20090610-3-IT-4004.00027.

Kabir, G., & Sumi, R. S. (2014). Integrating fuzzy analytic hierarchy process with PROMETHEE method for total quality management consultant selection. Production & Manufacturing Research, 2(1), 380-399. http://dx.doi.org/10.1080/21693277.2014.895689.

Kaiser, G., Gross, P., Kc, G., Parekh, J., & Valetto, G. (2005). An approach to autonomizing legacy systems. New York: Programming Systems Lab., Columbia University.

Keeney, R. L., & Gregory, R. S. (2004). Selecting attributes to measure the achievement of objectives. Operations Research, 53(1), 1-11.

Knoll, D., Prüglmeier, M., & Reinhart, G. (2016). Predicting future inbound logistics processes using machine learning. Procedia CIRP, 52, 145-150. http://dx.doi.org/10.1016/j.procir.2016.07.078.

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. http://dx.doi.org/10.1108/13552510910943886.

Lee, M. H., Yun, J. H. J., Pyka, A., Won, D. K., Kodama, F., Schiuma, G., Park, H. S., Jeon, J., Park, K. B., Jung, K. H., Yan, M.-R., Lee, S. Y., & Zhao, X. (2018). How to respond to the Fourth Industrial Revolution, or the second information technology revolution? Dynamic new combinations between technology, market, and society through open innovation. Journal of Open Innovation, 4(3), 21. http://dx.doi.org/10.3390/joitmc4030021.

Liao, Y., Rocha, E., Deschamps, F., Brezinski, G., & Venâncio, A. (2018). The impact of the fourth industrial revolution: a cross-country/region comparison. Production, 28, 18. http://dx.doi.org/10.1590/0103-6513.20180061.

Liere-Netheler, K., Packmohr, S., & Vogelsang, K. (2018). Drivers of digital transformation in manufacturing. In Proceedings of the 51st Hawaii International Conference on System Sciences. Mānoa: University of Hawai. http://dx.doi.org/10.24251/HICSS.2018.493.

Liu, K., Alderson, A., Sharp, B., Shah, H., & Dix, A. (1998). Using semiotic techniques to derive requirements from legacy systems. Staffordshire: School of Computing, Staffordshire University.

Lopes, I., Senra, P., Vilarinho, S., Sá, V., Teixeira, C., Lopes, J., Alves, A., Oliveira, J. A., & Figueiredo, M. (2016). Requirements specification of a computerized maintenance management system: a case study. Procedia CIRP, 52, 268-273. http://dx.doi.org/10.1016/j.procir.2016.07.047.

Maeda, M., Sakurai, Y., Tamaki, T., & Nonaka, Y. (2017). Method for automatically recognizing various operation statuses of legacy machines. Procedia CIRP, 63, 418-423. http://dx.doi.org/10.1016/j.procir.2017.03.150.

Mahraz, M. I., Benabbou, L., & Berrado, A. (2019). A systematic literature review of digital transformation. In Proceedings of the International Conference on Industrial Engineering and Operations Management (pp. 917-931). Michigan: IEOM.

McKinsey & Company. (2015). Industry 4.0: how to navigate digitization of the manufacturing sector. Tel Aviv.

McKinsey & Company. (2018). Unlocking success in digital transformations (pp. 1-14). Tel Aviv.

Mergel, I., Edelmann, N., & Haug, N. (2019). Defining digital transformation: Results from expert interviews. Government Information Quarterly, 36(4), 101385. http://dx.doi.org/10.1016/j.giq.2019.06.002.

Morakanyane, R., Grace, A., & O’Reilly, P. (2017). Conceptualizing digital transformation in business organizations: a systematic review of literature. In 30th Bled eConference: Digital Transformation - From Connecting Things to Transforming Our Lives (pp. 427-444). Bled: Faculty of Organizational Sciences, University of Maribor.

Mourtzis, D., Vlachou, E., Milas, N., & Xanthopoulos, N. (2016). A cloud-based approach for maintenance of machine tools and equipment based on shop-floor monitoring. Procedia CIRP, 41, 655-660. http://dx.doi.org/10.1016/j.procir.2015.12.069.

Naudet, Y., Latour, T., Guedria, W., & Chen, D. (2010). Towards a systemic formalisation of interoperability. Computers in Industry, 61(2), 176-185. http://dx.doi.org/10.1016/j.compind.2009.10.014.

OMRON. (2018). Machine automation concepts to enable innovation for digitalized manufacturing. Osaka.

Panetto, H., Zdravkovic, M., Jardim-Goncalves, R., Romero, D., Cecil, J., & Mezgár, I. (2016). New perspectives for the future interoperable enterprise systems. Computers in Industry, 79, 47-63. http://dx.doi.org/10.1016/j.compind.2015.08.001.

Pieper, R. (2011). Legacy machine monitoring using power signal analysis (pp. 1-8). In ASME 2011 International Manufacturing Science and Engineering Conference. New York: ASME.

Pini, M. (2019). Family management and Industry 4.0: different effects in different geographical areas? An analysis of the less developed regions in Italy. Journal of Entrepreneurship, Management and Innovation, 15(3), 73-102. http://dx.doi.org/10.7341/20191533.

Plattform Industrie 4.0. (2015). Status report - RAMI4.0 (p. 28). Frankfurt: ZVEI – German Electrical and Electronic Manufacturers.

Podgórski, D. (2015). Measuring operational performance of OSH management system: a demonstration of AHP-based selection of leading key performance indicators. Safety Science, 73, 146-166. http://dx.doi.org/10.1016/j.ssci.2014.11.018.

Presley, A., & Liles, D. H. (2015). The use of IDEF0 for the design and specification of methodologies. In Proceedings of the 4th Industrial Engineering Research Conference. Nashville.

Qin, J., Liu, Y., & Grosvenor, R. (2016). A categorical framework of manufacturing for Industry 4.0 and beyond. Procedia CIRP, 52, 173-178. http://dx.doi.org/10.1016/j.procir.2016.08.005.

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: Springer.

Ramos, L., Loures, E., Deschamps, F., & Venâncio, A. (2020). Systems evaluation methodology to attend the digital projects requirements for Industry 4.0. International Journal of Computer Integrated Manufacturing, 33(4), 398-410. http://dx.doi.org/10.1080/0951192X.2019.1699666.

Ransom, J., Somerville, I., & Warren, I. (1998). A method for assessing legacy systems for evolution. In 2nd Euromicro Conference on Software Maintenance and Reengineering (CSMR’98) (128 p.). Washington: IEEE Computer Society. http://dx.doi.org/10.1109/CSMR.1998.665778.

Renna, P. (2017). Allocation improvement policies to reduce process time based on workload evaluation in job shop manufacturing systems. International Journal of Industrial Engineering Computations, 8(3), 373-384. http://dx.doi.org/10.5267/j.ijiec.2016.12.001.

Renna, P., & Ambrico, M. (2019). The allocation of improvement programs in a flow shop for single and multi-products: a simulation assessment. International Journal of Agile Systems and Management, 12(3), 228-244. http://dx.doi.org/10.1504/IJASM.2019.10022795.

Roghanian, E., & Alipour, M. (2014). A fuzzy model for achieving lean attributes for competitive advantages development using AHP-QFD-PROMETHEE. Journal of Industrial Engineering International, 10(3), 68. http://dx.doi.org/10.1007/s40092-014-0068-4.

Romero, D., & Vernadat, F. (2016). Enterprise information systems state of the art: past, present and future trends. Computers in Industry, 79, 3-13. http://dx.doi.org/10.1016/j.compind.2016.03.001.

Rosendahl, R., Schmidt, N. S., Lüder, A., & Ryashentseva, D. (2015). Industry 4.0 value networks in legacy systems. In IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 15-18). New York: IEEE. http://dx.doi.org/10.1109/ETFA.2015.7301598.

Roy, B. (1968). Classement et choix en présence de points de vue multiples. Revue Française d’informatique et de Recherche Opérationnelle, 2(8), 57-75.

Ruschel, E., Santos, E. A. P., & Loures, E. de F.R. (2017). Industrial maintenance decision-making: a systematic literature review. Journal of Manufacturing Systems, 45, 180-194. http://dx.doi.org/10.1016/j.jmsy.2017.09.003.

Saaty, R. W. (1987). The analytic hierarchy process-what and how it is used. Mathematical Modelling, 9(3-5), 161-176. http://dx.doi.org/10.1016/0270-0255(87)90473-8.

Santos, K., Loures, E., Piechnicki, F., & Canciglieri, O. (2017). Opportunities assessment of product development process in Industry 4.0. Procedia Manufacturing, 11, 1358-1365. http://dx.doi.org/10.1016/j.promfg.2017.07.265.

Schuster, C. H., Schuster, J. J., & Oliveira, A. S. (2014). Aplicação do diagrama de Mudge e QFD utilizando como exemplo a hierarquização dos requisitos para um carro voado. Revista Gestão da Produção, Operações e Sistemas, 10(1), 197-213.

Silva Serapião Leal, G., Guédria, W., & Panetto, H. (2019). An ontology for interoperability assessment: a systemic approach. Journal of Industrial Information Integration, 16, 100100. http://dx.doi.org/10.1016/j.jii.2019.07.001.

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. http://dx.doi.org/10.1016/j.procir.2016.09.013.

Stjepić, A.-M., Ivančić, L., & Vugec, D. S. (2020). Mastering digital transformation through business process management: investigating alignments, goals, orchestration, and roles, journal of entrepreneurship. Management and Innovation, 16(1), 41-74. http://dx.doi.org/10.7341/20201612.

Tedeschi, S., Rodrigues, D., Emmanouilidis, C., Erkoyuncu, J., Roy, R., & Starr, A. (2018). A cost estimation approach for IoT modular architectures implementation in legacy systems. Procedia Manufacturing, 19, 103-110. http://dx.doi.org/10.1016/j.promfg.2018.01.015.

Temiz, I., & Calis, G. (2017). Selection of construction equipment by using multi-criteria decision making methods. Procedia Engineering, 196, 286-293. http://dx.doi.org/10.1016/j.proeng.2017.07.201.

Trojan, F., & Morais, D. C. (2012). Using electre tri to support maintenance of water distribution networks. Pesquisa Operacional, 32(2), 423-442. http://dx.doi.org/10.1590/S0101-74382012005000013.

Ullberg, J., Chen, D., & Johnson, P. (2009). Barriers to enterprise interoperability. In R. Poler, M. van Sinderen & R. Sanchis (Eds.), Enterprise interoperability (Lecture Notes in Business Information Processing, No. 38, pp. 13-24). Berlin: Springer. http://dx.doi.org/10.1007/978-3-642-04750-3_2.

Utiyama, M. H. R., Godinho Filho, M., & Oprime, P. C. (2021). An alternative for improving setup times and time between failures aiming at manufacturing lead time reduction. Production Engineering, 15(5), 651-665. http://dx.doi.org/10.1007/s11740-021-01048-0.

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. The Netherlands: European Commission. Retrieved in 2022, September 8, from https://silo.tips/download/monitoring-the-effectiveness-of-maintenance-programs-through-the-use-of-performa

Vallhagen, J., Almgren, T., & Thörnblad, K. (2017). Advanced use of data as an enabler for adaptive production control using mathematical optimization: an application of Industry 4. 0 principles. Procedia Manufacturing, 11, 663-670. http://dx.doi.org/10.1016/j.promfg.2017.07.165.

Venâncio, A. L. A. C., Loures, E. F. R., Deschamps, F., Justus, A. S., Lumikoski, A. F., & Brezinski, G. L. (2022). Technology prioritization framework to adapt maintenance legacy systems for Industry 4.0 requirement: an interoperability approach. Production, 32, e20210035. http://dx.doi.org/10.1590/0103-6513.20210035.

Vernadat, F. B. (2010). Technical, semantic and organizational issues of enterprise interoperability and networking. Annual Reviews in Control, 34(1), 139-144. http://dx.doi.org/10.1016/j.arcontrol.2010.02.009.

Vilarinho, S., Lopes, I., & Oliveira, J. A. (2017). Preventive maintenance decisions through maintenance optimization models: a case study. Procedia Manufacturing, 11, 1170-1177. http://dx.doi.org/10.1016/j.promfg.2017.07.241.

Vinodh, S., Prasanna, M., & Hari Prakash, N. (2014). Integrated Fuzzy AHP-TOPSIS for selecting the best plastic recycling method: a case study. Applied Mathematical Modelling, 38(19-20), 4662-4672. http://dx.doi.org/10.1016/j.apm.2014.03.007.

Weichhart, G., Panetto, H., & Molina, A. (2021). Interoperability in the cyber-physical manufacturing enterprise. Annual Reviews in Control, 51, 346-356. http://dx.doi.org/10.1016/j.arcontrol.2021.03.006.

Xu, M., David, J. M., & Kim, S. H. (2018). The fourth industrial revolution: opportunities and challenges. International Journal of Financial Research, 9(2), 90-95. http://dx.doi.org/10.5430/ijfr.v9n2p90.

Zaman, I., Pazouki, K., Norman, R., Younessi, S., & Coleman, S. (2017). Challenges and opportunities of big data analytics for upcoming regulations and future transformation of the shipping industry. Procedia Engineering, 194, 537-544. http://dx.doi.org/10.1016/j.proeng.2017.08.182.

Zentes, J., Morschett, D., Schramm-Klein, H., Zentes, J., Morschett, D., & Schramm-Klein, H. (2011). Store location – trading area analysis and site selection. In J. Zentes, D. Morschett & H. Schramm-Klein (Eds.), Strategic retail management. Wiesbaden: Gabler Verlag. http://dx.doi.org/10.1007/978-3-8349-6740-4_11.
 


Submitted date:
09/08/2022

Accepted date:
02/23/2023

642ef404a953955a4d383093 production Articles
Links & Downloads

Production

Share this page
Page Sections