Mapping the Impacts of Industry 4.0 on Performance Measurement Systems



Bibliometric Analisys, Industry 4.0, Performance Measurement Systems, Performance Measures, Scientific Mapping, Supply Chain Management


Industry 4.0 technologies have the potential to enhance performance measurement systems dramatically. Although there are many reviews on Industry 4.0, there are none with attention on the interplay between Industry 4.0 and performance measurement systems. Thus, this article presents a scientific map on Industry 4.0 impacts on performance measurement systems. We applied bibliometric analysis and content analysis in a sample of 325 documents gathered from the Web of Science scientific index. R Bibliometrix package and VOSviewer software supported the data processing and analysis. The major results point out that scientific production has been rising since 2015, but it still is in the initial maturity stage without a few top productive authors and with high productivity of technological areas compared to business administration and operations management areas. This latest evidence calls attention to more research on how to use Industry 4.0 technologies in management systems. The results disclose positive impacts of Industry 4.0 technologies on performance measurement systems. The significant trend is the development of intelligent systems that provide adequate information for decision-making in real time with an extension to the entire supply chain. However, this green field calls for an interdisciplinary research effort from technological and management domains to apprehend the proper value to all stakeholders.


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M. Bourne, M. Franco-Santos, P. Micheli, and A. Pavlov, “Performance measurement and management: a system of systems perspective,” Int. J. Prod. Res., vol. 56, no. 8, pp. 2788–2799, 2018, doi: 10.1080/00207543.2017.1404159.

A. Neely, M. Gregory, and K. Platts, “Performance measurement system design : a literature review and research agenda,” Int. J. Oper. Prod. Manag., vol. 25, no. 12, pp. 1228–1263, 2005, doi: 10.1108/01443570510633639.

R. Mello and R. A. Martins, “Can big data analytics enhance performance measurement systems?,” IEEE Eng. Manag. Rev., vol. 47, no. 1, pp. 52–57, 2019, doi: 10.1109/EMR.2019.2900645.

M. Bourne, A. Neely, J. Mills, and K. Platts, “Implementing performance measurement systems: a literature review,” Int. J. Bus. Perform. Manag., vol. 5, no. 1, pp. 1–24, 2003, doi: 10.1504/IJBPM.2003.002097.

Y. Demchenko, P. Grosso, C. De Laat, and P. Membrey, “Addressing big data issues in scientific data infrastructure,” in 2013 International Conference on Collaboration Technologies and Systems (CTS), 2013, pp. 48–55, doi: 10.1109/CTS.2013.6567203.

P. Zikopoulos, C. Eaton, D. Deroos, T. Deutsch, and G. Lapis, Understanding Big Data: analytics for enterprise class Hadoop and streaming data. McGraw-Hill, 2012.

R. Mello, L. R. Leite, and R. A. Martins, “Is big data the next big thing in performance measurement systems?,” in IIE Annual Conference and Expo 2014, 2014, pp. 1837–1846.

U. Bititci, P. Garengo, V. Dörfler, and S. Nudurupati, “Performance measurement: challenges for tomorrow,” Int. J. Manag. Rev., vol. 14, no. 3, pp. 305–327, 2012, doi: 10.1111/j.1468-2370.2011.00318.x.

A. Genovese, S. C. Lenny Koh, N. Kumar, and P. K. Tripathi, “Exploring the challenges in implementing supplier environmental performance measurement models: A case study,” Prod. Plan. Control, vol. 25, no. 13–14, pp. 1198–1211, 2014, doi: 10.1080/09537287.2013.808839.

K. Jung, S. S. Choi, B. Kulvatunyou, H. Cho, and K. C. Morris, “A reference activity model for smart factory design and improvement,” Prod. Plan. Control, vol. 28, no. 2, pp. 108–122, 2017, doi: 10.1080/09537287.2016.1237686.

D. Trotta and P. Garengo, “Industry 4.0 key research topics: a bibliometric review,” in 2018 7th International Conference on

Industrial Technology and Management (ICITM), 2018, pp. 113–117, doi: 10.1109/ICITM.2018.8333930.

I. V. Ferreira, J. A. Bigheti, and E. P. Godoy, “Development of a Wireless Gateway for Industrial Internet of Things Applications,” IEEE Lat. Am. Trans., vol. 17, no. 10, pp. 1637–1644, 2019, doi: 10.1109/TLA.2019.8986441.

S. S. Nudurupati, S. Tebboune, and J. Hardman, “Contemporary performance measurement and management (PMM) in digital economies,” Prod. Plan. Control, vol. 27, no. 3, pp. 226–235, 2016, doi: 10.1080/09537287.2015.1092611.

S. S. Nudurupati, U. S. Bititci, V. Kumar, and F. T. S. Chan, “State of the art literature review on performance measurement,” Comput. Ind. Eng., vol. 60, no. 2, pp. 279–290, 2011, doi: 10.1016/j.cie.2010.11.010.

M. Lebas and K. Euske, “A conceptual and operational delineation of performance,” in Business performance measurement: theory and practice, Cambridge, United Kingdom: Cambridge University Press, 2002, pp. 65–79.

L. Berrah, G. Mauris, and F. Vernadat, “Information aggregation in industrial performance measurement: rationales, issues and definitions,” Int. J. Prod. Res., vol. 42, no. 20, pp. 4271–4293, 2004, doi: 10.1080/00207540410001716534.

M. Franco-Santos, L. Lucianetti, and M. Bourne, “Contemporary performance measurement systems: a review of their consequences and a framework for research,” Manag. Account. Res., vol. 23, no. 2, pp. 79–119, 2012, doi: 10.1016/j.mar.2012.04.001.

V. Maestrini, D. Luzzini, P. Maccarrone, and F. Caniato, “Supply chain performance measurement systems: a systematic review and research agenda,” Int. J. Prod. Econ., vol. 183, pp. 299–315, 2017, doi: 10.1016/j.ijpe.2016.11.005.

A. Tung, K. Baird, and H. P. Schoch, “Factors influencing the effectiveness of performance measurement systems,” Int. J. Oper. Prod. Manag., vol. 31, no. 12, pp. 1287–1310, 2011, doi: 10.1108/01443571111187457.

M. Kennerley and A. Neely, “Measuring performance in a changing business environment,” Int. J. Oper. Prod. Manag., vol. 23, no. 2, pp. 213–229, 2003, doi: 10.1108/01443570310458465.

K. Schwab, The fourth industrial revolution, 1st ed. Cologny/Geneva, Switzerland: World Economic Forum, 2016.

R. Drath and A. Horch, “Industrie 4.0: hit or hype?,” IEEE Industrial Electronics Magazine, IEEE, pp. 56–58, 2014.

T. Lerher, “Warehousing 4.0 by using shuttlebased storage and retrieval systems,” FME Trans., vol. 46, no. 3, pp. 381–385, 2018, doi: 10.5937/fmet1803381L.

C. Öberg and G. Graham, “How smart cities will change supply chain management: a technical viewpoint,” Prod. Plan. Control, vol. 27, no. 6, pp. 529–538, 2016, doi: 10.1080/09537287.2016.1147095.

P. K. Muhuri, A. K. Shukla, and A. Abraham, “Industry 4.0: A bibliometric analysis and detailed overview,” Eng. Appl. Artif. Intell., vol. 78, pp. 218–235, 2019, doi: 10.1016/j.engappai.2018.11.007.

P. Zawadzki and K. Zywicki, “Smart product design and production control for effective mass customization in the industry 4.0 concept,” Manag. Prod. Eng. Rev., vol. 7, no. 3, pp. 105–112, 2016, doi: 10.1515/mper-2016-0030.

F. Li, A. Nucciarelli, S. Roden, and G. Graham, “How smart cities transform operations models: A new research agenda for operations management in the digital economy,” Prod. Plan. Control, vol. 27, no. 6, pp. 514–528, 2016, doi: 10.1080/09537287.2016.1147096.

R. Geissbauer, J. Vedso, and S. Schrauf, “Industry 4.0: building the digital enterprise,” 2016. [Online]. Available:

E. Hofmann and M. Rüsch, “Industry 4.0 and the current status as well as future prospects on logistics,” Comput. Ind., vol. 89, pp. 23–34, 2017, doi: 10.1016/j.compind.2017.04.002.

M. Rüßmann et al., “Industry 4.0: the future of productivity and growth in manufacturing,” 2015. [Online]. Available:


B. Hu and D. Kostamis, “Managing supply disruptions when sourcing from reliable and unreliable suppliers,” Prod. Oper. Manag., vol. 24, no. 5, pp. 808–820, 2015, doi: 10.1111/poms.12293.

H. Maddern, P. A. Smart, R. S. Maull, and S. Childe, “End-to-end process management: implications for theory and practice,” Prod. Plan. Control, vol. 25, no. 16, pp. 1303–1321, 2014, doi: 10.1080/09537287.2013.832821.

G. Hwang, J. Lee, J. Park, and T. W. Chang, “Developing performance measurement system for Internet of Things and smart factory environment,” Int. J. Prod. Res., vol. 55, no. 9, pp. 2590–2602, 2017, doi: 10.1080/00207543.2016.1245883.

R. F. Babiceanu and R. Seker, “Big Data and virtualization for manufacturing cyber-physical systems: a survey of the current status and future outlook,” Comput. Ind., vol. 81, pp. 128–137, 2016, doi: 10.1016/j.compind.2016.02.004.

S. Kumaraguru, B. Kulvatunyou, and K. C. Morris, “Integrating realtime analytics and continuous performance management in smart manufacturing systems,” in IFIP International Conference on Advances in Production Management Systems (APMS), 2014, vol. 440, pp. 175–182, doi: 10.1007/978-3-662-44733-8_22.

F. A. R. Silva, “Analytical intelligence in processes: data science for business,” IEEE Lat. Am. Trans., vol. 16, no. 8, pp. 2240–2247, 2018, doi: 10.1109/TLA.2018.8528241.

N. K. Gimenez Isasi, E. Morosini Frazzon, and M. Uriona, “Big data and business analytics in the supply chain: a review of the literature,” IEEE Lat. Am. Trans., vol. 13, no. 10, pp. 3382–3391, 2015, doi: 10.1109/TLA.2015.7387245.

A. Bonci, M. Pirani, and S. Longhi, “Robotics 4.0: performance improvement made easy,” IEEE Int. Conf. Emerg. Technol. Fact. Autom. ETFA, pp. 1–8, 2017, doi: 10.1109/ETFA.2017.8247682.

L. M. Kipper, L. B. Furstenau, D. Hoppe, R. Frozza, and S. Iepsen, “Scopus scientific mapping production in industry 4.0 (2011–2018): a bibliometric analysis,” Int. J. Prod. Res., vol. 58, no. 6, pp. 1605–1627, 2020, doi: 10.1080/00207543.2019.1671625.

G. Chueke and M. Amatucci, “O que é bibliometria? Uma introdução ao Fórum,” Rev. Eletrônica Negócios Int., vol. 10, no. 2, pp. 1–5, 2015, doi: 10.18568/1980-48651021-52015.

F. Caviggioli and E. Ughetto, “A bibliometric analysis of the research dealing with the impact of additive manufacturing on industry, business and society,” Int. J. Prod. Econ., vol. 208, pp. 254–268, 2019, doi: 10.1016/j.ijpe.2018.11.022.

I. Zupic and T. Čater, “Bibliometric methods in management and organization,” Organ. Res. Methods, vol. 18, no. 3, pp. 429–472, 2015, doi: 10.1177/1094428114562629.

M. Aria and C. Cuccurullo, “bibliometrix: an R-tool for comprehensive science mapping analysis,” J. Informetr., vol. 11, no.

, pp. 959–975, 2017, doi: 10.1016/j.joi.2017.08.007.

S. A. Morris and B. Van Der Veer Martens, “Mapping research specialties,” in Annual Review of Information Science and Technology, vol. 42, 2008, pp. 213–295.

N. Matloff, The Art of R Programming: A Tour of Statistical Software Design. São Francisco, USA: No Starch Press, 2011.

N. J. van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, 2010, doi: 10.1007/s11192-009-0146-3.

Q. He, “Knowledge discovery through co-word analysis,” Libr. Trends, vol. 48, no. 1, pp. 133–159, 1999.

X. Y. Leung, J. Sun, and B. Bai, “Bibliometrics of social media research: a co-citation and co-word analysis,” Int. J. Hosp. Manag., vol. 66, pp. 35–45, 2017, doi: 10.1016/j.ijhm.2017.06.012.

V. L. S. Guedes and S. Borschiver, “Bibliometria: uma ferramenta estatística para a gestão da informação e do conhecimento, em sistemas de informação e de comunicação e de avaliação científica e tecnológica,” in Encontro Nacional de Ciência da Informação, 2005, pp. 1–18.

E. O. Lucas, J. C. Garcia-Zorita, and E. Sanz-Casado, “Evolução histórica de investigação em informetria: ponto de vista espanhol,” Liinc em Rev., vol. 9, no. 1, pp. 255–270, 2013, doi: 10.18617/liinc.v9i1.509.

R. P. Smiraglia, “ISKO 11’s diverse bookshelf: an editorial,” Knowl. Organ., vol. 38, no. 3, pp. 179–186, 2011, doi: 10.5771/0943-7444-2011-3-179.

X. Xu, X. Chen, F. Jia, S. Brown, Y. Gong, and Y. Xu, “Supply chain finance: a systematic literature review and bibliometric analysis,” Int. J. Prod. Econ., vol. 204, pp. 160–173, 2018, doi: 10.1016/j.ijpe.2018.08.003.

T. Bellardo, “The use of co-citations to study science.,” Libr. Res., vol. 2, no. 3, pp. 231–237, 1980.

L. R. Leite, “Systematic literature review on performance measurement and sustainability,” in American Society for Engineering Management (ASEM), 2012, pp. 869–878.

J. Davis, T. Edgar, J. Porter, J. Bernaden, and M. Sarli, “Smart manufacturing, manufacturing intelligence and demand-dynamic performance,” Comput. Chem. Eng., vol. 47, pp. 145–156, 2012, doi: 10.1016/j.compchemeng.2012.06.037.

J. Zhou, R. Qingyang Hu, and Y. Qian, “Scalable distributed communication architectures to support advanced metering infrastructure in smart grid,” IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 9, pp. 1632–173, 2012, doi: 10.1109/TPDS.2012.53.

Q. Wu et al., “Cognitive internet of things: a new paradigm beyond connection,” IEEE Internet Things J., vol. 1, no. 2, pp. 129–143, 2014, doi: 10.1109/JIOT.2014.2311513.

H. Haas, L. Yin, Y. Wang, and C. Chen, “What is LiFi?,” J. Light. Technol., vol. 34, no. 6, pp. 1533–1544, 2016, doi:


P. K. Sharma, M. Y. Chen, and J. H. Park, “A software defined fog node based distributed blockchain cloud architecture for IoT,” IEEE Access, vol. 6, pp. 115–124, 2018, doi: 10.1109/ACCESS.2017.2757955.

F. Leimkuhler and Y. Chen, “A Relationship between Lotka ’ s Law, Bradford ’ s Law , and Zipf ’ s Law,” J. Am. Soc. Inf. Sci., vol. 37, no. 5, pp. 307–314, 1986.

F. Osareh and E. Mostafavi, “Lotka’s Law and authorship distribution in Computer Science using Web of Science (WoS) during 1986–2009,” Collnet J. Sci. Inf. Manag., vol. 5, no. 2, pp. 171–183, 2011, doi: 10.1080/09737766.2011.10700911.

N. J. Van Eck and L. Waltman, “Visualizing Bibliometric Networks,” in Measuring scholarly impact: methods and practice, Springer, 2014, pp. 285–320.

L. Atzori, A. Iera, and G. Morabito, “The internet of things: a survey,” Comput. Networks, vol. 54, no. 15, pp. 2787–2805, 2010, doi: 10.1016/j.comnet.2010.05.010.

J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): a vision, architectural elements, and future directions,” Futur. Gener. Comput. Syst., vol. 29, no. 7, pp. 1645–1660, 2013, doi: 10.1016/j.future.2013.01.010.

M. Wollschlaeger, S. Sauter, and J. Jasperneite, “The future of industrial communication: automation networks in the era of the internet of things and industry 4.0,” IEEE Industrial Electronics Magazine, vol. 11, no. 1, pp. 17–27, 2017.

R. S. Kaplan and D. P. Norton, “Using the Balanced Scorecard as a strategic management system,” Harvard Business Review, p. 14, 1996.

A. Neely, “The performance measurement revolution: why now and what next?,” Int. J. Oper. Prod. Manag., vol. 19, no. 2, pp. 205–228, 1999, doi: 10.1108/01443579910247437.

B. M. Beamon, “Measuring supply chain performance,” Int. J. Oper. Prod. Manag., vol. 19, no. 3, pp. 275–292, 1999, doi:


S. A. Melnyk, U. Bititci, K. Platts, J. Tobias, and B. Andersen, “Is performance measurement and management fit for the future?,” Manag. Account. Res., vol. 25, no. 2, pp. 173–186, 2014, doi: 10.1016/j.mar.2013.07.007.

P. Taticchi, F. Tonelli, and L. Cagnazzo, “Performance measurement and management: a literature review and a research agenda,” Meas. Bus. Excell., vol. 14, no. 1, pp. 4–18, 2010, doi: 10.1108/13683041011027418.

M. Bourne, J. Mills, M. Wilcox, A. Neely, and K. Platts, “Designing, implementing and updating performance measurement systems,” Int. J. Oper. Prod. Manag., vol. 20, no. 7, pp. 754–771, 2000, doi: 10.1108/01443570010330739.

S. LaValle, E. Lesser, R. Shockley, M. S. Hopkins, and N. Kruschwitz, “Big Data, analytics and the path from insights to value,” MIT Sloan Management Review, vol. 52, no. 2, pp. 21–31, 2011.

H. Chen, R. H. L. Chiang, and V. C. Storey, “Business intelligence and analytics: from big data to big impact,” MIS Q., vol. 36, no. 4, pp.1165–1188, 2012.

A. McAfee and E. Brynjolfsson, “Big data: the management revolution,” Harvard Business Review, vol. 90, no. 10, p. 4, 2012.

V. Mayer-Schönberger and K. Cukier, Big data: a revolution that will transform how we live, work, and think. New York, USA: Houghton Mifflin Harcourt, 2013.

T. H. Davenport, “How strategists use ‘big data’ to support internal business decisions, discovery and production,” Strateg. Leadersh., vol. 42, no. 4, pp. 45–50, 2014, doi: 10.1108/SL-05-2014-0034.

S. F. Wamba, S. Akter, A. Edwards, G. Chopin, and D. Gnanzou, “How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study,” Int. J. Prod. Econ., vol. 165, pp. 234–246, 2015, doi: 10.1016/j.ijpe.2014.12.031.

G. Wang, A. Gunasekaran, E. W. T. Ngai, and T. Papadopoulos, “Big data analytics in logistics and supply chain management: certain investigations for research and applications,” Int. J. Prod. Econ., vol. 176, pp. 98–110, 2016, doi: 10.1016/j.ijpe.2016.03.014.

M. A. Waller and S. E. Fawcett, “Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management,” J. Bus. Logist., vol. 34, no. 2, pp. 77–84, 2013, doi: 10.1111/jbl.12010.

A. Gunasekaran et al., “Big data and predictive analytics for supply chain and organizational performance,” J. Bus. Res., vol. 70, pp. 308–317, 2017, doi: 10.1016/j.jbusres.2016.08.004.

S. Akter, S. F. Wamba, A. Gunasekaran, R. Dubey, and S. J. Childe, “How to improve firm performance using big data analytics capability and business strategy alignment?,” Int. J. Prod. Econ., vol. 182, pp. 113–131, 2016, doi: 10.1016/j.ijpe.2016.08.018.

S. F. Wamba, A. Gunasekaran, S. Akter, S. J. fan Ren, R. Dubey, and S. J. Childe, “Big data analytics and firm performance: effects of dynamic capabilities,” J. Bus. Res., vol. 70, pp. 356–365, 2017, doi: 10.1016/j.jbusres.2016.08.009.

M. Gupta and J. F. George, “Toward the development of a big data analytics capability,” Inf. Manag., vol. 53, no. 8, pp. 1049–1064, 2016, doi: 10.1016/

B. T. Hazen, C. A. Boone, J. D. Ezell, and L. A. Jones-Farmer, “Data quality for data science, predictive analytics, and big data in supply chain management: an introduction to the problem and suggestions for research and applications,” Int. J. Prod. Econ., vol. 154, pp. 72–80, 2014, doi: 10.1016/j.ijpe.2014.04.018.

H. Lasi, P. Fettke, H. G. Kemper, T. Feld, and M. Hoffmann, “Industry 4.0,” Bus. Inf. Syst. Eng., vol. 6, no. 4, pp. 239–242, 2014, doi: 10.1007/s12599-014-0334-4.

R. Schmidt, M. Möhring, R. C. Härting, C. Reichstein, P. Neumaier, and P. Jozinović, “Industry 4.0 - Potentials for creating smart products: empirical research results,” in 18th International Conference on Business Information Systems, 2015, vol. 208, pp. 16–27.

J. Lee, H. A. Kao, and S. Yang, “Service innovation and smart analytics for Industry 4.0 and big data environment,” Procedia CIRP, vol. 16, pp. 3–8, 2014, doi: 10.1016/j.procir.2014.02.001.

H. Kagermann, W. Wahlster, and J. Helbig, “Recommendations for implementing the strategic initiative Industrie 4.0,” 2013.

J. Lee, B. Bagheri, and H. A. Kao, “A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems,” Manuf. Lett., vol. 3, pp. 18–23, 2015, doi: 10.1016/j.mfglet.2014.12.001.

M. Hermann, T. Pentek, and B. Otto, “Design principles for industrie 4.0 scenarios,” in 49th Hawaii International Conference on System Sciences (HICSS), 2016, pp. 3928–3937, doi: 10.1109/HICSS.2016.488.

D. Ivanov, A. Dolgui, B. Sokolov, F. Werner, and M. Ivanova, “A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0,” Int. J. Prod. Res., vol. 54, no. 2, pp. 386–402, 2016, doi: 10.1080/00207543.2014.999958.

A. Sanders, C. Elangeswaran, and J. Wulfsberg, “Industry 4.0 implies lean manufacturing: research activities in industry 4.0 function as enablers for lean manufacturing,” J. Ind. Eng. Manag., vol. 9, no. 3, pp. 811–833, 2016, doi: 10.3926/jiem.1940.

Y. Liao, F. Deschamps, E. de F. R. Loures, and L. F. P. Ramos, “Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal,” Int. J. Prod. Res., vol. 55, no. 12, pp. 3609–3629, 2017, doi: 10.1080/00207543.2017.1308576.

S. S. Kamble and A. Gunasekaran, “Big data-driven supply chain performance measurement system: a review and framework for implementation,” Int. J. Prod. Res., vol. 58, no. 1, pp. 65–86, 2020, doi: 10.1080/00207543.2019.1630770.

D. Appelbaum, A. Kogan, M. Vasarhelyi, and Z. Yan, “Impact of business analytics and enterprise systems on managerial accounting,” Int. J. Account. Inf. Syst., vol. 25, pp. 29–44, 2017, doi: 10.1016/j.accinf.2017.03.003.

S. Krishnamoorthi and S. K. Mathew, “Business analytics and business value: a comparative case study,” Inf. Manag., vol. 55, no. 5, pp. 643–666, 2018, doi: 10.1016/

A. Raffoni, F. Visani, M. Bartolini, and R. Silvi, “Business performance analytics: exploring the potential for performance management systems,” Prod. Plan. Control, vol. 29, no. 1, pp. 51–67, 2018, doi: 10.1080/09537287.2017.1381887.

S. Jeble, R. Dubey, S. J. Childe, T. Papadopoulos, D. Roubaud, and A. Prakash, “Impact of big data and predictive analytics capability on supply chain sustainability,” Int. J. Logist. Manag., vol. 29, no. 2, pp. 513–538, 2018, doi: 10.1108/IJLM-05-2017-0134.

G. Gravili, M. Benvenuto, A. Avram, and C. Viola, “The influence of the Digital Divide on Big Data generation within supply chain management,” Int. J. Logist. Manag., vol. 29, no. 2, pp. 592–628, 2018, doi: 10.1108/IJLM-06-2017-0175.

A. J. Dweekat, G. Hwang, and J. Park, “A supply chain performance measurement approach using the internet of things,” Ind. Manag. Data Syst., vol. 117, no. 2, pp. 267–286, 2017, doi: 10.1108/IMDS-03-2016-0096.

M. Rezaei, M. A. Shirazi, and B. Karimi, “IoT-based framework for performance measurement,” Ind. Manag. Data Syst., vol. 117, no. 4, pp. 688–712, 2017, doi: 10.1108/IMDS-08-2016-0331.


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