ÜRETİM YÖNETİMİNDE DİJİTAL DÖNÜŞÜM: BİBLİYOMETRİK TEMELLİ SİSTEMATİK BİR İNCELEME

Dijital dönüşümün en çok etkilediği alanlarının başında şüphesiz ki Üretim/İşlemler Yönetimi disiplini gelmektedir. Üretim/İşlemler Yönetimindeki bu etkileri ortaya çıkarmak ve gelecekteki araştırma yönlerini yorumlayabilmek için alandaki bilimsel literatürün derinlemesine incelenmesi ve analiz edilmesi gerekmektedir. Bu çalışmada, dijital dönüşüm ve Üretim/İşlemler Yönetimi arasındaki ilişki hakkında geniş bir perspektif çizmek, bu araştırma alanının tematik evrimini ortaya çıkarmak ve gelecekteki potansiyel araştırma yönleri hakkında çıkarım yapmak için sistematik literatür taraması ve bibliyometrik analizi içeren iki aşamalı bir yaklaşım kullanılmıştır. Analize, 2007-2021 yılları arasında bu araştırma alanında Web of Science (Wos) ve Scopus veri tabanlarında taranan dergilerde yayınlanan makaleler dahil edilmiştir. Araştırma örneklemine seçilen 3021 makalenin tanımlayıcı analizleri ile bu araştırma alanında öne çıkan makaleler, yazarlar, ülkeler, dergiler ve anahtar kelimeler belirlenmiştir. Verilerin tanımlayıcı analizlerinin ardından anahtar kelimelerin birlikte oluşum analizi, tematik evrim ve tematik harita analizi RStudio ve VOSviewer kullanılarak gerçekleştirilmiştir. Tüm bibliyometrik analizler R Bibliometrix paketi kullanılarak yapılmıştır.

DIGITAL TRANSFORMATION IN OPERATIONS MANAGEMENT: A BIBLIOMETRIC-BASED SYSTEMATIC REVIEW

Digital transformation undoubtedly has important implications on the discipline of Operations Management. To unveil these effects and interpret the future research directions requires an in-depth review and analysis of the scientific literature on this research area. This study uses a two-stage approach including Systematic Literature Review and bibliometric analysis to draw a broad perspective on the relationship between DT and OM, reveal the thematic evolution of this research area, and inference about potential future research directions. The scope of the analysis includes the articles drawn from the Web of Science and Scopus databases published between 2007 and 2021 in this research area. With the descriptive analysis of 3021 selected articles to the research sample, top articles, authors, countries, journals, and keywords in this research field were determined. Following the descriptive analysis of the data, the co-occurrence analysis of keywords, thematic evolution, and thematic map analysis was conducted using RStudio and VOSviewer.. All bibliometric analyzes were performed using the R Bibliometrix package.

___

  • Agarwal, N., & Brem, A. (2015). Strategic business transformation through technology convergence: implications from General Electric's industrial internet initiative. International Journal of Technology Management, 67(2-4), 196-214.
  • Agrawal, V. K. (2002). Constituencies of journals in production and operations management: implications on reach and quality. Production and Operations Management, 11(2), 101-108.
  • Akmal, A., Podgorodnichenko, N., Greatbanks, R., & Everett, A. M. (2018). Bibliometric analysis of production planning and control (1990–2016). Production Planning & Control, 29(4), 333-351.
  • Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics, 11(4), 959-975.
  • Aria. M. & Cuccurullo. C. (2019). A brief introduction to bibliometrix. Accessed Date: 02.07.2021, https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html.
  • Barnes, C. (2017). The h-index debate: an introduction for librarians. The Journal of Academic Librarianship, 43(6), 487-494.
  • Barreto, L., Amaral, A., & Pereira, T. (2017). Industry 4.0 implications in logistics: an overview. Procedia manufacturing, 13, 1245-1252.
  • Bizubac, D., Popa, M. S., & Hörmann, B. O. (2018). ERP operations in the industry of smart manufacturing. Acta Technica Napocensis-Series: Applied Mathematics, Mechanics, and Engineering, 61(3).
  • Brodny, J., & Tutak, M. (2019). Analysing the utilisation effectiveness of mining machines using independent data acquisition systems: a case study. Energies, 12(13), 2505.
  • Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics, 22(1), 155-205.
  • Caputo, A., Pizzi, S., Pellegrini, M. M., & Dabić, M. (2021). Digitalization and business models: Where are we going? A science map of the field. Journal of Business Research, 123, 489-501.
  • Cardoso, J. A. A., Ishizu, F. T., de Lima, J. T., & de Souza Pinto, J. (2019). Blockchain Based MFA Solution: The use of hydro raindrop MFA for information security on WordPress websites. Brazilian Journal of Operations & Production Management, 16(2), 281-293.
  • Caulkin, R., Ahmad, A., Fairweather, M., Jia, X., & Williams, R. A. (2007). An investigation of sphere packed shell-side columns using a digital packing algorithm. Computers & Chemical Engineering, 31(12), 1715-1724.
  • Chung, K., Yoo, H., Choe, D., & Jung, H. (2019). Blockchain network based topic mining process for cognitive manufacturing. Wireless Personal Communications, 105(2), 583-597.
  • Civerchia, F., Bocchino, S., Salvadori, C., Rossi, E., Maggiani, L., & Petracca, M. (2017). Industrial Internet of Things monitoring solution for advanced predictive maintenance applications. Journal of Industrial Information Integration, 7, 4-12.
  • Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of informetrics, 5(1), 146-166.
  • Culot, G., Nassimbeni, G., Orzes, G., & Sartor, M. (2020). Behind the definition of Industry 4.0: Analysis and open questions. International Journal of Production Economics, 226, 107617.
  • Davis, J., Edgar, T., Porter, J., Bernaden, J., & Sarli, M. (2012). Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers & Chemical Engineering, 47, 145-156.
  • Denyer, D. and Tranfield, D. (2009). Producing a systematic review. In Buchanan, D. and Bryman, A. (Eds.), The Sage Handbook of Organizational Research Methods (pp.671–689), London: Sage.
  • Dhamija, P. and Bag, S. (2020). Role of artificial intelligence in operations environment: a review and bibliometric analysis, The TQM Journal, 32(4), 869-896.
  • Dougherty, D., & Dunne, D. D. (2012). Digital science and knowledge boundaries in complex innovation. Organization Science, 23(5), 1467-1484.
  • Ebert, C., & Duarte, C. H. C. (2018). Digital transformation. IEEE Software, 35(4), 16-21.
  • Esmaeilian, B., Sarkis, J., Lewis, K., & Behdad, S. (2020). Blockchain for the future of sustainable supply chain management in Industry 4.0. Resources, Conservation and Recycling, 163, 105064.
  • Evangelista, R., Guerrieri, P., & Meliciani, V. (2014). The economic impact of digital technologies in Europe. Economics of Innovation and new technology, 23(8), 802-824.
  • Fatorachian, H., & Kazemi, H. (2021). Impact of Industry 4.0 on supply chain performance. Production Planning & Control, 32(1), 63-81.
  • Fry, T. D., Donohue, J. M., Saladin, B. A., & Shang, G. (2013). The origins of research and patterns of authorship in the International Journal of Production Research. International Journal of Production Research, 51(23-24), 7470-7500.
  • Fu, B., Shu, Z., & Liu, X. (2018). Blockchain enhanced emission trading framework in fashion apparel manufacturing industry. Sustainability, 10(4), 1105.
  • Heizer, J. & Render, B. (2014). Operations management (7th ed.), Prentice Hall
  • Helu, M., Morris, K., Jung, K., Lyons, K., & Leong, S. (2015). Identifying performance assurance challenges for smart manufacturing. Manufacturing letters, 6, 1-4.
  • Henfridsson, O., Mathiassen, L., & Svahn, F. (2014). Managing technological change in the digital age: the role of architectural frames. Journal of Information Technology, 29(1), 27-43.
  • Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National academy of Sciences, 102(46), 16569-16572.
  • Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in industry, 89, 23-34.
  • Hosseini, S., Baziyad, H., Norouzi, R., Khiabani, S. J., Gidófalvi, G., Albadvi, A., Alimohammadi, A. and Seyedabrishami, S. (2021). Mapping the intellectual structure of GIS-T field (2008–2019): a dynamic co-word analysis. Scientometrics, 126(4), 2667-2688.
  • Hovanec, M., Píľa, J., Korba, P., & Pačaiová, H. (2015). Plant simulation as an instrument of logistics and transport of materials in a digital factory. NAŠE MORE: znanstveni časopis za more i pomorstvo, 62(3 Special Issue), 187-192.
  • Hsieh, P. N., & Chang, P. L. (2009). An assessment of world-wide research productivity in production and operations management. International Journal of Production Economics, 120(2), 540-551.
  • Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829-846.
  • Ivanov, D., Tang, C. S., Dolgui, A., Battini, D., & Das, A. (2021). Researchers' perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management. International Journal of Production Research, 59(7), 2055-2078.
  • Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., Kim, B.H. and Do Noh, S. (2016). Smart manufacturing: Past research, present findings, and future directions. International journal of precision engineering and manufacturing-green technology, 3(1), 111-128.
  • Kettunen, P., & Laanti, M. (2017). Future software organizations–agile goals and roles. European Journal of Futures Research, 5(1), 1-15.
  • Kiel, D., Müller, J. M., Arnold, C. & Voigt, K. I. (2020). Sustainable industrial value creation: Benefits and challenges of industry 4.0, in Digital Disruptive Innovation, (pp. 231-270).
  • Krajewski, L. J., Ritzman, L. P. & Malhotra, M. K. (2010). Operations management: Processes and supply chains, New Jersey: Pearson.
  • Küsters, D., Praß, N., & Gloy, Y. S. (2017). Textile learning factory 4.0–preparing germany's textile industry for the digital future. Procedia Manufacturing, 9, 214-221.
  • Lao, L., Ellis, M., Durand, H., & Christofides, P. D. (2015). Real‐time preventive sensor maintenance using robust moving horizon estimation and economic model predictive control. AIChE Journal, 61(10), 3374-3389.
  • Lee, J. Y., Shin, S. J., Lee, Y. T., & Libes, D. (2015). Toward development of a testbed for sustainable manufacturing. Concurrent Engineering, 23(1), 64-73.
  • Lee, C. K. M., Zhang, S. Z., & Ng, K. K. H. (2017). Development of an industrial Internet of things suite for smart factory towards re-industrialization. Advances in manufacturing, 5(4), 335-343.
  • Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Rimini, B., & Regattieri, A. (2019). Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning & Control, 30(1), 76-89.
  • Longo, F., Nicoletti, L., Padovano, A., d'Atri, G., & Forte, M. (2019). Blockchain-enabled supply chain: An experimental study. Computers & Industrial Engineering, 136, 57-69.
  • Maamar, A., & Benahmed, K. (2019). A hybrid model for anomalies detection in AMI system combining K-means clustering and deep neural network. Computers, Materials & Continua, 60(1), 15-39.
  • García, N. M. (2019). Multi-agent system for anomaly detection in Industry 4.0 using Machine Learning techniques. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(4), 33-40.
  • Maier, M. A., Korbel, J. J., & Brem, A. (2015). Innovation in supply chains-solving the agency dilemma in supply networks by using industry 4.0 technologies International journal of communication networks and distributed systems, 15(2/3), 235-247.
  • Mohamed, N., Al-Jaroodi, J., & Lazarova-Molnar, S. (2019). Leveraging the capabilities of industry 4.0 for improving energy efficiency in smart factories. IEEE Access, 7, 18008-18020.
  • Nakayama, R. S., de Mesquita Spínola, M., & Silva, J. R. (2020). Towards I4. 0: A comprehensive analysis of evolution from I3. 0. Computers & industrial engineering, 144, 106453.
  • Navas, M. A., Sancho, C., & Carpio, J. (2020). Disruptive maintenance engineering 4.0. International Journal of Quality & Reliability Management, 37(6/7), 853-871.
  • Núñez-Merino, M., Maqueira-Marín, J. M., Moyano-Fuentes, J., & Martínez-Jurado, P. J. (2020). Information and digital technologies of Industry 4.0 and Lean supply chain management: a systematic literature review. International Journal of Production Research, 58(16), 5034-5061.
  • O’donovan, P., Leahy, K., Bruton, K., & O’Sullivan, D. T. (2015). Big data in manufacturing: a systematic mapping study. Journal of Big Data, 2(1), 1-22.
  • Oluyisola, O. E., Sgarbossa, F., & Strandhagen, J. O. (2020). Smart production planning and control: concept, use-cases and sustainability implications. Sustainability, 12(9), 3791.
  • Pal, J. K. (2016). Resolving the confusion over metadata-creation in digital archives, Annals of Library and Information Studies (ALIS), 63(2), 110-116.
  • Peres, R. S., Barata, J., Leitao, P., & Garcia, G. (2019). Multistage quality control using machine learning in the automotive industry. IEEE Access, 7, 79908-79916.
  • Petracca, M., Bocchino, S., Azzarà, A., Pelliccia, R., Ghibaudi, M., & Pagano, P. (2013). WSN and RFID Integration in the IoT scenario: an Advanced safety System for Industrial Plants. Journal of communications software and systems, 9(1), 104-113.
  • Prause, G. (2015). Sustainable business models and structures for Industry 4.0, Journal of Security & Sustainability Issues, 5(2), 159-169.
  • Romero-Silva, R., & Marsillac, E. (2019). Trends and topics in IJPR from 1961 to 2017: a statistical history. International Journal of Production Research, 57(15-16), 4692-4718.
  • Russell, R. S. and Taylor, B. W. (2019). Operations and Supply Chain Management. John Wiley & Sons.
  • Santolaria, J., Guillomía, D., Cajal, C., Albajez, J. A., & Aguilar, J. J. (2009). Modelling and calibration technique of laser triangulation sensors for integration in robot arms and articulated arm coordinate measuring machines. Sensors, 9(9), 7374-7396.
  • Schlüter, N., & Sommerhoff, B. (2017). Development of the DGQ role bundle model of the Q occupations. International Journal of Quality and Service Sciences, 9(3/4), 317-330.
  • Shang, G., Saladin, B., Fry, T., & Donohue, J. (2015). Twenty-six years of operations management research (1985–2010): authorship patterns and research constituents in eleven top rated journals. International Journal of Production Research, 53(20), 6161-6197.
  • Singhal, K., Singhal, J., & Starr, M. K. (2007). The domain of production and operations management and the role of Elwood Buffa in its delineation. Journal of Operations Management, 25(2), 310-327.
  • Strozzi, F., Colicchia, C., Creazza, A., & Noè, C. (2017). Literature review on the ‘Smart Factory’concept using bibliometric tools. International Journal of Production Research, 55(22), 6572-6591.
  • Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, 14(3), 207-222.
  • Tsai, W. H., Lan, S. H., & Lee, H. L. (2020). Applying ERP and MES to implement the IFRS 8 operating segments: A steel group’s activity-based standard costing production decision model. Sustainability, 12(10), 4303.
  • Viriyasitavat, W., Da Xu, L., Bi, Z., & Sapsomboon, A. (2020). Blockchain-based business process management (BPM) framework for service composition in industry 4.0. Journal of Intelligent Manufacturing, 31(7), 1737-1748.
  • Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of manufacturing systems, 48, 144-156.
  • Wang, Q., & Sun, X. (2019). The international journal of production research in the past, the present and the future: a bibliometric analysis. International Journal of Production Research, 57(15-16), 4676-4691.
  • Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer networks, 101, 158-168.
  • Yadav, N., Shankar, R., & Singh, S. P. (2020). Impact of Industry4. 0/ICTs, Lean Six Sigma and quality management systems on organisational performance. The TQM Journal, 32(4), 815-835.
  • Yang, X., Lee, J., & Jung, H. (2019). Fault Diagnosis Management Model using Machine Learning. Journal of information and communication convergence engineering, 17(2), 128-134.
  • Yu, W., Dillon, T., Mostafa, F., Rahayu, W., & Liu, Y. (2019). A global manufacturing big data ecosystem for fault detection in predictive maintenance. IEEE Transactions on Industrial Informatics, 16(1), 183-192.
  • Yue, X., Cai, H., Yan, H., Zou, C., & Zhou, K. (2015). Cloud-assisted industrial cyber-physical systems: An insight. Microprocessors and Microsystems, 39(8), 1262-1270.
  • Zenisek, J., Holzinger, F., & Affenzeller, M. (2019). Machine learning based concept drift detection for predictive maintenance. Computers & Industrial Engineering, 137, 106031.
  • Zhang, Y., Jiang, P., & Huang, G. (2008). RFID-based smart kanbans for just-in-time manufacturing. International Journal of Materials and Product Technology, 33(1-2), 170-184.
  • Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: a review. Engineering, 3(5), 616-630.
Uluslararası Yönetim İktisat ve İşletme Dergisi-Cover
  • ISSN: 2147-9208
  • Başlangıç: 2005
  • Yayıncı: Zonguldak Bülent Ecevit Üniversitesi