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1.
W. Aulbur, C. Arvind, and R. Bigghe, Skills Development for Industry 4.0. BRICS Skill Development Working Group, Roland Berger GMBH, 2016. [Online]. Available: https://rda.worldskills.ru/storage/app/media/Reports/2016_BRICS%20Skills%20Development%20for%20Industry%204.0/2016_BRICS_Skill-development-for-industry-4.0_report.pdf [Google Scholar]
2.
R. Berger, Industry 4.0. The New Industrial Revolution—How Europe will Succeed. Roland Berger Strategy Consultants GMBH, Munich, Germany, 2014. [Online]. Available: http://www.iberglobal.com/files/Roland_Berger_Industry.pdf [Google Scholar]
3.
J. Basl, “Companies on the way to industry 4.0 and their readiness,” J. Syst. Integr. (1804-2724), vol. 9, no. 3, 2018. [Google Scholar] [Crossref]
4.
S. Dabic-Miletic, “Autonomous vehicles as an essential component of industry 4.0 for meeting last-mile logistics requirements,” J. Ind. Intell., vol. 1, no. 1, pp. 55–62, 2023. [Google Scholar] [Crossref]
5.
S. Chang, H. Chang, and M. Lu, “Evaluating industry 4.0 technology application in SMEs: Using a hybrid MCDM approach,” Mathematics, vol. 9, no. 4, p. 414, 2021. [Google Scholar] [Crossref]
6.
V. Kumar, P. Vrat, and R. Shankar, “Prioritization of strategies to overcome the barriers in Industry 4.0: A hybrid MCDM approach,” Opsearch, vol. 58, no. 3, pp. 711–750, 2021. [Google Scholar] [Crossref]
7.
K. Elibal and E. Özceylan, “Comparing industry 4.0 maturity models in the perspective of TQM principles using fuzzy MCDM methods,” Technol. Forecast. Soc. Change, vol. 175, p. 121379, 2022. [Google Scholar] [Crossref]
8.
W. Torbacki, “A hybrid MCDM model combining DANP and PROMETHEE II methods for the assessment of cybersecurity in industry 4.0,” Sustainability, vol. 13, no. 16, p. 8833, 2021. [Google Scholar] [Crossref]
9.
M. Erdogan, B. Ozkan, A. Karasan, and I. Kaya, Selecting the Best Strategy for Industry 4.0 Applications with a Case Study. Springer, Cham, 2017. [Google Scholar]
10.
L. Yang, H. Zou, C. Shang, X. Ye, and P. Rani, “Adoption of information and digital technologies for sustainable smart manufacturing systems for industry 4.0 in small, medium, and micro enterprises (SMMEs),” Technol. Forecast. Soc. Change, vol. 188, p. 122308, 2023. [Google Scholar] [Crossref]
11.
A. Mardani and S. Saberi, “Industry 4.0 adoption drivers for sustainable supply chain in the manufacturing sector using a hybrid decision-making approach under q-rung orthopair fuzzy information,” IEEE Trans. Eng. Manage., pp. 1–18, 2023. [Google Scholar] [Crossref]
12.
V. Simic, S. Dabic-Miletic, E. B. Tirkolaee, Ž. Stević, A. Ala, and A. Amirteimoori, “Neutrosophic LOPCOW-ARAS model for prioritizing industry 4.0-based material handling technologies in smart and sustainable warehouse management systems,” Appl. Soft Comput., vol. 143, p. 110400, 2023. [Google Scholar] [Crossref]
13.
S. Miškić, S. Tadić, Ž. Stević, M. Krstić, and V. Roso, “A novel hybrid model for the evaluation of industry 4.0 technologies’ applicability in logistics centers,” J. Math., vol. 2023, Article ID 3532862, 2023. [Google Scholar] [Crossref]
14.
P. Rani, D. Pamucar, A. R. Mishra, M. Ibrahim Hezam, J. Ali, and S. K. Hasane Ahammad, “An integrated interval-valued Pythagorean fuzzy WISP approach for industry 4.0 technology assessment and digital transformation,” Ann. Oper. Res., 2023. [Google Scholar] [Crossref]
15.
P. Dhamija, “South Africa in the era of Industry 4.0: An insightful investigation,” Scientometrics, vol. 127, no. 9, pp. 5083–5110, 2022. [Google Scholar] [Crossref]
16.
S. M. Sackey, A. Bester, and D. Adams, “Industry 4.0 learning factory didactic design parameters for industrial engineering education in South Africa,” S. Afr. J. Ind. Eng., vol. 28, no. 1, pp. 114–124, 2017. [Google Scholar] [Crossref]
17.
M. Eustace Dogo, A. F. Salami, O. Clinton Aigbavboa, and T. Nkonyana, Taking Cloud Computing to the Extreme Edge: A Review of Mist Computing for Smart Cities and Industry 4.0 in Africa. Springer, Cham, 2018. [Online]. Available: [Google Scholar] [Crossref]
18.
J. N. Anitah, S. O. Nyamwange, P. O. Magutu, M. Chirchir, and J. M. Mose, “Industry 4.0 technologies and operational performance of unilever Kenya and L’Oreal East Africa,” Noble Int. J. Bus. Manage. Res., vol. 3, no. 10, pp. 125–134, 2019. [Google Scholar]
19.
E. Ukwandu, N. C. Ephraim Okafor, C. Ikerionwu, C. Olebara, and C. Ugwu, Assessing Cyber-Security Readiness of Nigeria to Industry 4.0. Springer, Cham, 2023. [Google Scholar]
20.
O. Mbadiwe, U. Eze, and C. Ikerionwu, “Regulation of blockchain technology in Nigeria: Need and risks mitigation towards industry 4.0,” in in 15th International Conference on Emerging Applications and Technologies for Industry, Nigeria, 2020, pp. 229–237. [Google Scholar]
21.
O. Bongomin, E. O. Nganyi, M. R. Abswaidi, E. Hitiyise, and G. Tumusiime, “Sustainable and dynamic competitiveness towards technological leadership of industry 4.0: Implications for East african community,” J. Eng., vol. 2020, pp. 1–22, 2020. [Google Scholar] [Crossref]
22.
W. Maisiri and L. van Dyk, “Industry 4.0 readiness assessment for South African industries,” S. Afr. J. Ind. Eng., vol. 30, no. 3, pp. 134–148, 2019. [Google Scholar] [Crossref]
23.
W. Maisiri, L. van Dyk, and R. Coeztee, “Factors that inhibit sustainable adoption of Industry 4.0 in the South African manufacturing industry,” Sustainability, vol. 13, no. 3, p. 1013, 2021. [Google Scholar] [Crossref]
24.
M. B. Bouraima, C. K. Kiptum, K. M. Ndiema, Y. Qiu, and I. Tanackov, “Prioritization road safety strategies towards zero road traffic injury using ordinal priority approach,” Oper. Res. Eng. Sci. Theor. Appl., vol. 5, no. 2, pp. 206–221, 2022. [Google Scholar] [Crossref]
25.
D. Pamucar, M. Deveci, I. Gokasar, D. Delen, M. Köppen, and W. Pedrycz, “Evaluation of metaverse integration alternatives of sharing economy in transportation using fuzzy Schweizer-Sklar based ordinal priority approach,” Decis. Support Syst., vol. 171, p. 113944, 2023. [Google Scholar] [Crossref]
26.
M. Deveci, I. Gokasar, D. Pamucar, Y. Chen, and D. Coffman, “Sustainable E-scooter parking operation in urban areas using fuzzy Dombi based RAFSI model,” Sustainable Cities Soc., vol. 91, p. 104426, 2023. [Google Scholar] [Crossref]
27.
F. Ecer, H. Küçükönder, S. Kayapınar Kaya, and Ö. Faruk Görçün, “Sustainability performance analysis of micro-mobility solutions in urban transportation with a novel IVFNN-Delphi-LOPCOW-CoCoSo framework,” Transp. Res. Part A, vol. 172, p. 103667, 2023. [Google Scholar] [Crossref]
28.
E. B. Tirkolaee and N. S. Aydin, “Integrated design of sustainable supply chain and transportation network using a fuzzy bi-level decision support system for perishable products,” Expert Syst. Appl., vol. 195, p. 116628, 2022. [Google Scholar] [Crossref]
29.
M. Akram, A. Khan, A. Luqman, T. Senapati, and D. Pamucar, “An extended MARCOS method for MCGDM under 2-tuple linguistic q-rung picture fuzzy environment,” Eng. Appl. Artif. Intell., vol. 120, p. 105892, 2023. [Google Scholar] [Crossref]
30.
I. Badi and M. Kridish, “Landfill site selection using a novel FUCOM-CODAS model: A case study in Libya,” Sci. Afr., vol. 9, p. e00537, 2020. [Google Scholar] [Crossref]
31.
Ž. Stević, D. Pamučar, A. Puška, and P. Chatterjee, “Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS),” Comput. Ind. Eng., vol. 140, p. 106231, 2020. [Google Scholar] [Crossref]
32.
C. K. Kiptum, M. B. Bouraima, Ž. Stević, S. Okemwa, S. Birech, and Y. Qiu, “Sustainable strategies for the successful operation of the bike-sharing system using an ordinal priority approach,” J. Eng. Manage. Syst. Eng., vol. 1, no. 2, pp. 43–50, 2022. [Google Scholar] [Crossref]
33.
M. B. Bouraima, Y. Qiu, C. K. Kiptum, and K. M. Ndiema, “Evaluation of factors affecting road maintenance in Kenyan counties using the ordinal priority approach,” J. Comput. Cognit. Eng., pp. 1–6, 2022. [Google Scholar] [Crossref]
34.
E. Celik, O. N. Bilisik, M. Erdogan, A. T. Gumus, and H. Baracli, “An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul,” Transp. Res. Part E, vol. 58, pp. 28–51, 2013. [Google Scholar] [Crossref]
35.
S. Qahtan, A. Hassan Alsattar, A. A. Zaidan, M. Deveci, D. Pamucar, and D. Delen, “Performance assessment of sustainable transportation in the shipping industry using a q-rung orthopair fuzzy rough sets-based decision making methodology,” Expert Syst. Appl., vol. 223, p. 119958, 2023. [Google Scholar] [Crossref]
36.
D. Pamucar, I. Gokasar, A. Ebadi Torkayesh, M. Deveci, L. Martínez, and Q. Wu, “Prioritization of unmanned aerial vehicles in transportation systems using the integrated stratified fuzzy rough decision-making approach with the hamacher operator,” Inf. Sci., vol. 622, pp. 374–404, 2023. [Google Scholar] [Crossref]
37.
T. Senapati, V. Simic, A. Saha, M. Dobrodolac, Y. Rong, and E. B. Tirkolaee, “Intuitionistic fuzzy power Aczel-Alsina model for prioritization of sustainable transportation sharing practices,” Eng. Appl. Artif. Intell., vol. 119, p. 105716, 2023. [Google Scholar] [Crossref]
38.
M. B. Bouraima, Ž. Stević, I. Tanackov, and Y. Qiu, “Assessing the performance of Sub-Saharan African (SSA) railways based on an integrated Entropy-MARCOS approach,” Oper. Res. Eng. Sci. Theor. Appl., vol. 4, no. 2, pp. 13–35, 2021. [Google Scholar] [Crossref]
39.
M. B. Bouraima, Y. Qiu, E. Ayyildiz, and A. Yildiz, “Prioritization of strategies for a sustainable regional transportation infrastructure by hybrid spherical fuzzy group decision-making approach,” Neural Comput. Appl., 2023. [Google Scholar] [Crossref]
40.
M. Kovač, S. Tadić, M. Krstić, and M. B. Bouraima, “Novel spherical fuzzy MARCOS method for assessment of drone-based city logistics concepts,” Complexity, vol. 2021, pp. 1–17, 2021. [Google Scholar] [Crossref]
41.
Ž. Stević, M. B. Bouraima, M. Subotić, Y. Qiu, P. A. Buah, K. M. Ndiema, and C. M. Ndjegwes, “Assessment of causes of delays in the road construction projects in the Benin Republic using fuzzy PIPRECIA method,” Math. Probl. Eng., vol. 2022, pp. 1–18, 2022. [Google Scholar] [Crossref]
42.
M. Deveci, I. Gokasar, A. R. Mishra, P. Rani, and Z. Ye, “Evaluation of climate change-resilient transportation alternatives using fuzzy Hamacher aggregation operators based group decision-making model,” Eng. Appl. Artif. Intell., vol. 119, p. 105824, 2023. [Google Scholar] [Crossref]
43.
A. Ala, V. Simic, D. Pamucar, and E. B. Tirkolaee, “Appointment scheduling problem under fairness policy in healthcare services: Fuzzy ant lion optimizer,” Expert Sys. Appl., vol. 207, p. 117949, 2022. [Google Scholar] [Crossref]
44.
M. Akram, R. Bibi, and M. Deveci, “An outranking approach with 2-tuple linguistic Fermatean fuzzy sets for multi-attribute group decision-making,” Eng. Appl. Artif. Intell., vol. 121, p. 105992, 2023. [Google Scholar] [Crossref]
45.
V. Simic, S. Dabic-Miletic, E. B. Tirkolaee, Ž. Stević, M. Deveci, and T. Senapati, “Neutrosophic CEBOM-MACONT model for sustainable management of end-of-life tires,” Appl. Soft Comput., vol. 143, p. 110399, 2023. [Google Scholar] [Crossref]
46.
I. Badi, M. B. Bouraima, and M. L. Jibril, “Risk Assessment in Construction Projects Using the Grey Theory,” J. Eng. Manage. Syst. Eng., vol. 1, no. 2, pp. 58–66, 2022. [Google Scholar] [Crossref]
47.
I. Badi, M. B. Bouraima, and L. J. Muhammad, “The role of intelligent transportation systems in solving traffic problems and reducing environmental negative impact of urban transport,” Decis. Making Anal., vol. 1, pp. 1–9, 2022. [Google Scholar]
48.
S. Moslem, Ž. Stević, I. Tanackov, and F. Pilla, “Sustainable development solutions of public transportation:An integrated IMF SWARA and fuzzy bonferroni operator,” Sustainable Cities Soc., vol. 93, p. 104530, 2023. [Google Scholar] [Crossref]
49.
D. Pamučar, A. Puška, V. Simić, I. Stojanović, and M. Deveci, “Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina function,” Eng. Appl. Artif. Intell., vol. 121, p. 106025, 2023. [Google Scholar] [Crossref]
50.
M. Deveci, I. Gokasar, D. Pamucar, A. A. Zaidan, X. Wen, and B. Brij Gupta, “Evaluation of cooperative intelligent transportation system scenarios for resilience in transportation using type-2 neutrosophic fuzzy VIKOR,” Transp. Res. Part A, vol. 172, p. 103666, 2023. [Google Scholar] [Crossref]
51.
Ö. F. Görçün, D. Pamucar, R. Krishankumar, and H. Küçükönder, “The selection of appropriate Ro-Ro Vessel in the second-hand market using the WASPAS’ Bonferroni approach in type 2 neutrosophic fuzzy environment,” Eng. Appl. Artif. Intell., vol. 117, p. 105531, 2023. [Google Scholar] [Crossref]
52.
I. Badi, D. Pamucar, L. Gigović, and S. Tatomirović, “Optimal site selection for sitting a solar park using a novel GIS- SWA’TEL model: A case study in Libya,” Int. J. Green Energy, vol. 18, no. 4, pp. 336–350, 2021. [Google Scholar] [Crossref]
53.
S. Hashemkhani Zolfani, M. H. Aghdaie, A. Derakhti, E. K. Zavadskas, and M. H. Morshed Varzandeh, “Decision making on business issues with foresight perspective; an application of new hybrid MCDM model in shopping mall locating,” Expert Sys. Appl., vol. 40, no. 17, pp. 7111–7121, 2013. [Google Scholar] [Crossref]
54.
M. Göksel Saraç, T. Dedebaş, E. Hastaoğlu, and E. Arslan, “Influence of using scarlet runner bean flour on the production and physicochemical, textural, and sensorial properties of vegan cakes: WASPAS-SWARA techniques,” Int. J. Gastronomy Food Sci., vol. 27, p. 100489, 2022. [Google Scholar] [Crossref]
55.
G. N. Yücenur and A. Ipekçi, “SWARA/WASPAS methods for a marine current energy plant location selection problem,” Renewable Energy, vol. 163, pp. 1287–1298, 2021. [Google Scholar] [Crossref]
56.
S. Agarwal, R. Kant, and R. Shankar, “Evaluating solutions to overcome humanitarian supply chain management barriers: A hybrid fuzzy SWARA – Fuzzy WASPAS approach,” Int. J. Disaster Risk Reduct., vol. 51, p. 101838, 2020. [Google Scholar] [Crossref]
57.
S. S. Hosseini Dehshiri, “New hybrid multi criteria decision making method for offshore windfarm site location in Persian Gulf, Iran,” Ocean Eng., vol. 256, p. 111498, 2022. [Google Scholar] [Crossref]
58.
S. J. Ghoushchi, S. R. Bonab, A. M. Ghiaci, G. Haseli, H. Tomaskova, and M. Hajiaghaei-Keshteli, “Landfill site selection for medical waste using an integrated SWARA-WASPAS framework based on spherical fuzzy set,” Sustainability, vol. 13, no. 24, p. 13950, 2021. [Google Scholar] [Crossref]
59.
E. Ayyildiz and A. Taskin, “A novel spherical fuzzy AHP-VIKOR methodology to determine serving petrol station selection during COVID-19 lockdown: A pilot study for İstanbul,” Socio Econ. Plann. Sci., vol. 83, p. 101345, 2022. [Google Scholar] [Crossref]
60.
F. Kutlu Gündoğdu and C. Kahraman, “Spherical fuzzy sets and spherical fuzzy TOPSIS method,” J. Intell. Fuzzy Syst., vol. 36, no. 1, pp. 337–352, 2019. [Google Scholar] [Crossref]
61.
F. Kutlu Gündoğdu and C. Kahraman, “A novel spherical fuzzy analytic hierarchy process and its renewable energy application,” Soft Comput., vol. 24, no. 6, pp. 4607–4621, 2019. [Google Scholar] [Crossref]
62.
C. Kahraman, F. Kutlu Gundogdu, S. Cevik Onar, and B. Oztaysi, “Hospital location selection using spherical fuzzy TOPSIS,” in Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), 2019, pp. 77–82. [Google Scholar] [Crossref]
63.
A. Mardani, M. Nilashi, N. Zakuan, N. Loganathan, S. Soheilirad, M. Z. M. Saman, and O. Ibrahim, “A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments,” Appl. Soft Comput., vol. 57, pp. 265–292, 2017. [Google Scholar] [Crossref]
64.
S. Hashemkhani Zolfani, M. Yazdani, and E. K. Zavadskas, “An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process,” Soft Comput., vol. 22, pp. 7399–7405, 2018. [Google Scholar] [Crossref]
65.
M. Akram, S. Naz, F. Feng, and A. Shafiq, “Assessment of hydropower plants in Pakistan: Muirhead mean-based 2-tuple linguistic t-spherical fuzzy model combining SWARA with COPRAS,” Arab. J. Sci. Eng., vol. 48, no. 5, pp. 5859–5888, 2022. [Google Scholar] [Crossref]
66.
S. Banihashemi, M. Khalilzadeh, J. Antucheviciene, and J. Šaparauskas, “Trading off time–cost–quality in construction project scheduling problems with fuzzy SWARA–TOPSIS approach,” Buildings, vol. 11, no. 9, p. 387, 2021. [Google Scholar] [Crossref]
67.
M. B. Bouraima, Y. Qiu, Ž. Stević, and V. Simić, “Assessment of alternative railway systems for sustainable transportation using an integrated IRN SWARA and IRN CoCoSo model,” Socio Econ. Plann. Sci., vol. 86, p. 101475, 2023. [Google Scholar] [Crossref]
68.
M. B. Bouraima, N. A. Tengecha, Ž. Stević, V. Simić, and Y. Qiu, “An integrated fuzzy MCDM model for prioritizing strategies for successful implementation and operation of the bus rapid transit system,” Ann. Oper. Res., 2023. [Google Scholar] [Crossref]
69.
H. Garg, J. Vimala, S. Rajareega, D. Preethi, and L. Perez-Dominguez, “Complex intuitionistic fuzzy soft SWARA - COPRAS approach: An application of ERP software selection,” AIMS Math., vol. 7, no. 4, pp. 5895–5909, 2022. [Google Scholar] [Crossref]
70.
M. B. Bouraima, Y. Qiu, Ž. Stević, D. Marinković, and M. Deveci, “Integrated intelligent decision support model for ranking regional transport infrastructure programmes based on performance assessment,” Expert Syst. Appl., vol. 222, p. 119852, 2023. [Google Scholar] [Crossref]
71.
M. Keshavarz-Ghorabaee, M. Amiri, E. Zavadskas, Z. Turskis, and J. Antucheviciene, “An extended step-wise weight assessment ratio analysis with symmetric interval type-2 fuzzy sets for determining the subjective weights of criteria in multi-criteria decision-making problems,” Symmetry, vol. 10, no. 4, p. 91, 2018. [Google Scholar] [Crossref]
72.
A. R. Mishra, P. Rani, K. Pandey, A. Mardani, J. Streimikis, D. Streimikiene, and M. Alrasheedi, “Novel multi-criteria intuitionistic fuzzy SWARA–COPRAS approach for sustainability evaluation of the bioenergy production process,” Sustainability, vol. 12, no. 10, p. 4155, 2020. [Google Scholar] [Crossref]
73.
P. Rani, A. R. Mishra, R. Krishankumar, A. Mardani, F. Cavallaro, K. Soundarapandian Ravichandran, and K. Balasubramanian, “Hesitant fuzzy SWARA-complex proportional assessment approach for sustainable supplier selection (HF-SWARA-COPRAS),” Symmetry, vol. 12, no. 7, p. 1152, 2020. [Google Scholar] [Crossref]
74.
P. Rani, A. R. Mishra, A. Mardani, F. Cavallaro, D. Štreimikienė, and S. A. R. Khan, “Pythagorean fuzzy SWARA–VIKOR framework for performance evaluation of solar panel selection,” Sustainability, vol. 12, no. 10, p. 4278, 2020. [Google Scholar] [Crossref]
75.
A. Ulutaş, C. B. Karakuş, and A. Topal, “Location selection for logistics center with fuzzy SWARA and CoCoSo methods,” J. Intell. Fuzzy Syst., vol. 38, no. 4, pp. 4693–4709, 2020. [Google Scholar] [Crossref]
76.
S. Jafarzadeh Ghoushchi, S. Shaffiee Haghshenas, A. Memarpour Ghiaci, G. Guido, and A. Vitale, “Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment,” Neural Comput. Appl., vol. 35, no. 6, pp. 4549–4567, 2022. [Google Scholar] [Crossref]
77.
M. Deveci, D. Pamucar, I. Gokasar, M. Isik, and D. Coffman, “Fuzzy Einstein WASPAS approach for the economic and societal dynamics of the climate change mitigation strategies in urban mobility planning,” Struct. Change Econ. Dyn., vol. 61, pp. 1–17, 2022. [Google Scholar] [Crossref]
78.
A. R. Mishra and P. Rani, “Interval-valued intuitionistic fuzzy WASPAS method: application in reservoir flood control management policy,” Group Decis. Negot., vol. 27, no. 6, pp. 1047–1078, 2018. [Google Scholar] [Crossref]
79.
D. Pamucar, M. Deveci, F. Canıtez, and V. Lukovac, “Selecting an airport ground access mode using novel fuzzy LBWA-WASPAS-H decision making model,” Eng. Appl. Artif. Intell., vol. 93, p. 103703, 2020. [Google Scholar] [Crossref]
80.
P. Rani, A. R. Mishra, and K. R. Pardasani, “A novel WASPAS approach for multi-criteria physician selection problem with intuitionistic fuzzy type-2 sets,” Soft Comput., vol. 24, no. 3, pp. 2355–2367, 2019. [Google Scholar] [Crossref]
81.
E. Ayyildiz and A. Taskin Gumus, “A novel spherical fuzzy AHP-integrated spherical WASPAS methodology for petrol station location selection problem: a real case study for İstanbul,” Environ. Sci. Pollut. Res., vol. 27, no. 29, pp. 36109–36120, 2020. [Google Scholar] [Crossref]
82.
E. Ayyildiz, M. Erdogan, and A. Taskin Gumus, “A Pythagorean fuzzy number-based integration of AHP and WASPAS methods for refugee camp location selection problem: a real case study for Istanbul, Turkey,” Neural Comput. Appl., vol. 33, no. 22, pp. 15751–15768, 2021. [Google Scholar] [Crossref]
83.
B. Yalcin Kavus, E. Ayyildiz, P. Gulum Tas, and A. Taskin, “A hybrid Bayesian BWM and Pythagorean fuzzy WASPAS-based decision-making framework for parcel locker location selection problem,” Environ. Sci. Pollut. Res., pp. 1–18, 2022. [Google Scholar] [Crossref]
84.
E. Ayyildiz and A. Taskin, A Novel Interval Valued Neutrosophic AHP-WASPAS Methodology for Emergency Supply Depot Location Selection Problems. CRC Press, New York, FL, 2022. [Google Scholar]
85.
T. Senapati and G. Chen, “Picture fuzzy WASPAS technique and its application in multi-criteria decision-making,” Soft Comput., vol. 26, no. 9, pp. 4413–4421, 2022. [Google Scholar] [Crossref]
86.
T. Senapati, R. Ronald Yager, and G. Chen, “Cubic intuitionistic WASPAS technique and its application in multi-criteria decision-making,” J. Ambient Intell. Human Comput., vol. 12, no. 9, pp. 8823–8833, 2021. [Google Scholar] [Crossref]
87.
V. Simić, D. Lazarević, and M. Dobrodolac, “Picture fuzzy WASPAS method for selecting last-mile delivery mode: a case study of Belgrade,” Eur. Transp. Res. Rev., vol. 13, no. 1, pp. 1–22, 2021. [Google Scholar] [Crossref]
88.
N. Aydin and S. Seker, “WASPAS based MULTIMOORA method under IVIF environment for the selection of hub location,” J. Enterp. Inf. Manage., vol. 33, no. 5, pp. 1233–1256, 2020. [Google Scholar] [Crossref]
89.
T. Nguyen, P. Nguyen, H. Pham, T. Nguyen, D. Nguyen, T. Tran, H. Le, and H. Phung, “A novel integrating data envelopment analysis and spherical fuzzy MCDM approach for sustainable supplier selection in steel industry,” Mathematics, vol. 10, no. 11, p. 1897, 2022. [Google Scholar] [Crossref]
90.
Ž. Stević, D. Pamučar, M. Subotić, J. Antuchevičiene, and E. K. Zavadskas, “The location selection for roundabout construction using Rough BWM-Rough WASPAS approach based on a new Rough Hamy aggregator,” Sustainability, vol. 10, no. 8, p. 2817, 2018. [Google Scholar] [Crossref]
91.
F. Kutlu Gundogdu and C. Kahraman, “Extension of WASPAS with spherical fuzzy sets,” Informatica, vol. 30, no. 2, pp. 269–292, 2019. [Google Scholar]
92.
G. Mukwawaya, B. Emwanu, and S. Mdakane, “Assessing the readiness of South Africa for Industry 4.0-analysis of government policy, skills and education,” in Proceedings of the International Conference on Industrial Engineering and Operations Management  Pretoria/Johannesburg, South Africa, 2018. [Google Scholar]
93.
C. Akamanzi, P. Deutscher, B. Guerich, A. Lobelle, and A. Ooko-Ombaka, “Silicon Savannah: the Kenya ICT services cluster,” Microecon. Competitiveness, vol. 7, pp. 36–49, 2016. [Google Scholar]
94.
G. Spoettl and V. Tūtlys, “Education and training for the fourth industrial revolution,” Jurnal Pendidikan Teknologi dan Kejuruan, vol. 26, no. 1, pp. 83–93, 2020. [Google Scholar] [Crossref]
95.
D. Schneider, T. Huth, and T. Vietor, “Development of an Industry 4.0 method and knowledge platform for strategic technology implementation,” Procedia CIRP, vol. 100, pp. 613–618, 2021. [Google Scholar] [Crossref]
96.
D. Dikhanbayeva, A. Tokbergenova, Y. Lukhmanov, E. Shehab, Z. Pastuszak, and A. Turkyilmaz, “Critical factors of industry 4.0 implementation in an emerging country: Empirical study,” Future Internet, vol. 13, no. 6, p. 137, 2021. [Google Scholar] [Crossref]
97.
R. M. Mitra, “Digital transformation and industry 4.0 in Southeast Asia,” Digital Asia, pp. 109–133, 2019. [Google Scholar]
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Strategies for Enhancing Industry 4.0 Adoption in East Africa: An Integrated Spherical Fuzzy SWARA-WASPAS Approach
yanjun qiu1,
mouhamed bayane bouraima1*,
clement kiprotich kiptum2,
ertugrul ayyildiz3,
željko stević4,
ibrahim badi5,
kevin maraka ndiema2
1
School of Civil Engineering, Southwest Jiaotong University, 610031 Chengdu, China
2
Department of Civil and Structural Engineering, School of Engineering, University of Eldoret, 1125-30100 Eldoret, Kenya
3
Department of Industrial Engineering, Karadeniz Technical University: Karadeniz Teknik Universitesi, 61080 Trabzon, Turkey
4
Faculty of Transport and Traffic Engineering, University of East Sarajevo, Vojvode Mišića 52, 74000 Doboj, Bosnia and Herzegovina
5
Mechanical Engineering Department, Libyan Academy, 2429 Misurata, Libya
Journal of Industrial Intelligence
|
Volume 1, Issue 2, 2023
|
Pages 87-100
Received: 05-04-2023,
Revised: 05-28-2023,
Accepted: 05-31-2023,
Available online: 06-25-2023
View Full Article|Download PDF
Abstract:

Developed countries have successfully implemented various Industry 4.0 (I4.0) initiatives, showcasing their ability to reap the benefits of this new industrial revolution. Active pursuit of excellence in Industry 4.0 is evident in these nations. However, in Africa, many countries still lack a clear understanding of Industry 4.0, with some remaining trapped in Industry 1.0 and others facing challenges in transitioning to Industry 2.0. Moreover, a significant number of these African countries continue to grapple with limited access to reliable electricity. To address the issue, this study examines seven strategies identified as criteria for enhancing the adoption of Industry 4.0 within the East African Community (EAC). These strategies are derived from observations of Industry 4.0 initiatives implemented in developed countries. Subsequently, the criteria are used to evaluate and rank the level of Industry 4.0 adoption in two specific East African countries. To tackle the challenges of complex group decision-making, the study integrates the Weighted Aggregated Sum Product Assessment (WASPAS) technique with the Step-Wise Weight Assessment Ratio Analysis (SWARA) within a spherical fuzzy (SF) framework. The SF-SWARA approach is applied to determine the weight and importance of the criteria, while SF-WASPAS is employed to rank the countries based on the criteria weighted by SF-SWARA. According to the findings, it was revealed that education and training, research, development, and innovation, as well as public-private partnerships and policy innovation, are the three most influential strategies for significantly improving the adoption of Industry 4.0 within the East African community. Furthermore, the results indicate that Rwanda stands out as the leading country in terms of implementing these strategies to enhance the adoption of Industry 4.0 technology. To verify the reliability and suitability of the proposed methodology, a sensitivity analysis was conducted, which affirmed the stability and practicality of the suggested approach.

Keywords: Industry 40 (I40) adoption, WASPAS, SWARA, Spherical fuzzy sets

1. Introduction

The urgency and competitiveness of achieving Industry 4.0 (I4.0) have been widely recognized, necessitating proactive participation from Africa in this industrial revolution [1]. Unlike previous industrial transformations that excluded Africa, I4.0 is rapidly evolving and pervasive, making it essential for every African country to prepare thoroughly and early for its adoption [2]. The rapid pace of I4.0 evolution presents a multitude of opportunities, yet it can exacerbate existing inequalities if nations are unprepared for the changes it brings [3]. Governments, private sectors, and public-private partnerships have initiated national strategic initiatives to facilitate the implementation and widespread adoption of I4.0 in various countries. However, a lack of understanding remains regarding the number and nature of these initiatives.

2. Literature Review

Studies have focused on the adoption of I4.0 technology [4], [5], [6], with methodologies such as DEMATEL-based ANP [5], modified SWARA and WASPAS techniques [6], and fuzzy DEMATEL-TOPSIS approach [7] being employed to investigate various aspects of I4.0 implementation. Other methodologies have also been proposed, including DANP and PROMETHEE II [8], hybrid AHP-VIKOR [9], q-ROF-MEREC-RS approach [10], q-ROF-CRITIC model [11], neutrosophic MCDM methodology [12], MEREC-fuzzy MARCOS model [13], and a new decision-making framework by Rani et al. [14].

In the African context, studies have examined the development and application of I4.0 technology, in various countries such as South Africa, Kenya, Nigeria [15], [16], [17], [18], [19], [20], and a broader literature review covering various regions [21]. However, few researchers have proposed strategies to strengthen the adoption of I4.0 technology in Africa [17], [18], [19], [20], [21], [22], [23]. Furthermore, only one study by Bongomin et al. [21] has addressed the strategies for enhancing I4.0 adoption in the East Africa Community, but it did not prioritize these strategies nor conduct a comparative analysis between the countries within this specific region.

Considering the limitations of previous research, it becomes evident that the adoption of I4.0 technology necessitates the evaluation of a diverse set of paradoxical parameters. Conventional decision-making approaches centered around a single criterion are insufficient for addressing the inherent complexities of these challenges [24], [25], [26], [27], [28], [29], [30]. Consequently, multi-criteria decision-making (MCDM) approaches have gained traction, demonstrating promise in providing policymakers and managers with flexible and adaptable tools [31], [32], [33], [34], [35], [36], [37], [38], [39]. These approaches use predetermined parameters to classify and select one or more elements from a set of alternatives [40], [41], [42], [43], [44], [45], [46], and the chosen parameters are subsequently assessed based on their effectiveness in fulfilling their respective functions and determining the suitability of alternative options [47], [48], [49], [50], [51], [52].

In this study, an integrated approach combining SWARA and WASPAS methods under a spherical fuzzy set (SFS) framework is presented. This methodology aims to address the limitations of previous research by facilitating the identification and prioritization of strategies for enhancing the adoption of I4.0 within the East Africa Community. Additionally, it enables a comparative analysis of the effectiveness of these strategies between two countries within this specific region of Africa.

3. Methodology

This study adopts the integrated methodology initially developed by Hashemkhani Zolfani et al. [53] and further applies it to various research contexts [54], [55], [56], [57], [58].

3.1 Preliminaries

Spherical fuzzy sets (SFS) enable the representation of imprecision and uncertainty through linguistic expressions. Ayyildiz and Taskin [59] have defined three functions that can be extended to cover a wider area, providing experts with increased flexibility in expressing their opinions. These functions are defined within a range of 0 to 1. Kutlu Gündoğdu and Kahraman [60] stipulate that a spherical fuzzy number (SFN) must fulfill the following condition.

Definition 1: A SFN is presented as $\tilde{S}$:

$\widetilde{\mathrm{S}} \cong\left\{\mathrm{x}, \tilde{\mathrm{S}}\left(\mu_{\tilde{\mathrm{s}}}(\mathrm{x}), \mathrm{v}_{\tilde{\mathrm{s}}}(\mathrm{x}), \pi_{\tilde{\mathrm{s}}}(\mathrm{x})\right) ; \mathrm{x} \in \mathrm{X}\right\}$
(1)

$\mu_{\tilde{s}}(\mathrm{x}): \mathrm{X} \mapsto[0,1], \mathrm{v}_{\tilde{\mathrm{s}}}(\mathrm{x}): \mathrm{X} \mapsto[0,1]$ and $\pi_{\tilde{\mathrm{s}}}(\mathrm{x}): \mathrm{X} \mapsto[0,1]$ characterize the membership, non-membership, and hesitancy function of the component $\mathrm{x} \in \mathrm{X}$ to $\tilde{S}$, respectively and $X$ is a fixed set. And their sum of squares cannot be greater than 1.

$0 \leq \mu_{\tilde{s}}(\mathrm{x})^2+\mathrm{v}_{\tilde{s}}(\mathrm{x})^2+\pi_{\tilde{s}}(\mathrm{x})^2 \leq 1 ; \mathrm{x} \in \mathrm{U}$
(2)

Definition 2: Two SFNs $\widetilde{\alpha}=\mathrm{S}\left(\mu_\alpha, \mathrm{v}_\alpha, \pi_\alpha\right)$ and $\tilde{\beta}=S\left(\mu_\beta, v_\beta, \pi_\beta\right)$ are summed [61]:

$\widetilde{\alpha} \oplus \tilde{\beta}=\tilde{S}\left(\sqrt{\mu_{\tilde{\alpha}}^2+\mu_{\widetilde{\beta}}^2-\mu_{\tilde{\alpha}}^2 \mu_{\widetilde{\beta}}^2}, v_{\widetilde{\alpha}} v_{\widetilde{\beta}}, \sqrt{\left(1-\mu_{\tilde{\alpha}}^2\right) \pi_{\widetilde{\beta}}^2+\left(1-\mu_{\widetilde{\beta}}^2\right) \pi_{\widetilde{\alpha}}^2-\pi_{\tilde{\alpha}}^2 \pi_{\widetilde{\beta}}^2}\right)$
(3)

Definition 3: Two SFNs $\widetilde{\alpha}=S\left(\mu_\alpha, v_\alpha, \pi_\alpha\right)$ and $\tilde{\beta}=\mathrm{S}\left(\mu_\beta, v_\beta, \pi_\beta\right)$ are multiplied:

$\widetilde{\alpha} \otimes \tilde{\beta}=\widetilde{S}\left(\mu_{\widetilde{\alpha}} \mu_{\widetilde{\beta}}, \sqrt{v_{\widetilde{\alpha}}^2+v_{\widetilde{\beta}}^2-v_{\widetilde{\alpha}}^2 v_{\widetilde{\beta}}^2}, \sqrt{\left(1-v_{\widetilde{\alpha}}^2\right) \pi_{\widetilde{\beta}}^2+\left(1-v_{\widetilde{\beta}}^2\right) \pi_{\widetilde{\alpha}}^2-\pi_{\widetilde{\alpha}}^2 \pi_{\widetilde{\beta}}^2}\right)$
(4)

Definition 4: A SFN $\widetilde{\alpha}=S\left(\mu_\alpha, v_\alpha, \pi_\alpha\right)$ is multiplied by a positive scalar:

$\lambda \widetilde{\alpha}=\widetilde{S}\left(\sqrt{1-\left(1-\mu_{\tilde{\alpha}}^2\right)^\lambda}, v_{\widetilde{\alpha}}^\lambda, \sqrt{\left(1-\mu_{\widetilde{\alpha}}^2\right)^\lambda-\left(1-\mu_{\widetilde{\alpha}}^2-\pi_{\widetilde{\alpha}}^2\right)^\lambda}\right)$
(5)

Definition 5: The positive power of SFN $\widetilde{\alpha}=\mathrm{S}\left(\mu_\alpha, \mathrm{v}_\alpha, \pi_\alpha\right)$:

$\widetilde{\alpha}^\lambda=\tilde{S}\left(\mu_{\widetilde{\alpha}}^\lambda \sqrt{1-\left(1-v_{\widetilde{\alpha}}^2\right)^\lambda}, \sqrt{\left(1-v_{\tilde{\alpha}}^2\right)^\lambda-\left(1-v_{\widetilde{\alpha}}^2-\pi_{\widetilde{\alpha}}^2\right)^\lambda}\right)$
(6)

Definition 6: The score function for an $\widetilde{\alpha}=S\left(\mu_\alpha, v_\alpha, \pi_\alpha\right)$ [59]:

$\text { Score }(\tilde{\alpha})=\left(2 \mu_{\tilde{\alpha}}-\pi_{\tilde{\alpha}}\right)^2-\left(v_{\tilde{\alpha}}-\pi_{\tilde{\alpha}}\right)^2$
(7)

Definition 7: Spherical Weighted Arithmetic Mean (SWAM) is given below where $w=\left(w_1, w_2 \ldots \ldots, w_n\right) ; w_i \in[0,1] ; \sum_{i=1}^n w_i=1$ [62]:

$\begin{aligned} & S W A M_w\left(\widetilde{\alpha}_1, \ldots \ldots, \widetilde{\alpha}_n\right)=w_1 \widetilde{\alpha}_1+w_2 \widetilde{\alpha}_2+\cdots \ldots+w_n \widetilde{\alpha}_n \\ & =\left\{\begin{array}{c} {\left[1-\prod_{i=1}^n\left(1-\mu_{\widetilde{\alpha}_i}^2\right)^{w_i}\right]^{1 / 2}, \prod_{i=1}^n v_{\widetilde{\alpha}_i}^{w_i},} \\ {\left[\prod_{i=1}^n\left(1-\mu_{\widetilde{\alpha}_i}^2\right)^{w_i}-\prod_{i=1}^n\left(1-\mu_{\widetilde{\alpha}_i}^2-\pi_{\widetilde{\alpha}_i}^2\right)^{w_i}\right]^{1 / 2}} \end{array}\right\} \\ & \end{aligned}$
(8)
3.2 Spherical Fuzzy SWARA

The SWARA approach has demonstrated its utility as a decision-making tool across various domains [63]. It has been effectively employed in diverse areas, such as enhancing decision-making quality through the incorporation of experts’ evaluations of their ideas’ reliability [64], addressing humanitarian supply chains [56], analyzing performance in hydropower plants [65], managing trade-off problems [66], evaluating the sustainability of railway systems [67], conducting analysis on the operation and implementation of BRT systems [68], examining ERP software [69], classifying the performance of regional transport infrastructures projects [70], assessing intellectual capital aspects in companies [71], studying bioenergy technology production [72], facilitating supplier selection [73], making solar panel selections [74], and determining suitable locations for logistics centers [75]. In this study, the SF-SWARA methodology is employed for criteria weighting. Unlike the conventional SWARA approach that uses crisp numbers to evaluate the criteria, spherical fuzzy numbers (SFNs) are utilized in SF-SWARA. The steps of SF-SWARA are as follows.

Step 1: A decision matrix is assigned to each expert, where the significance of the criteria is evaluated based on linguistic terms provided in Table 1. Let $\tilde{A}_{j k}=\left(\mu_{\mathrm{jk}}, \mathrm{v}_{\mathrm{jk}}, \pi_{\mathrm{jk}}\right)$ be the SFN for an evaluation of criterion $j$ by expert $k$.

Table 1. Definition and fuzzy rate of linguistic terms [76]

Linguistic terms

Spherical fuzzy numbers

$\boldsymbol{\mu}$

$\mathbf{v}$

$\boldsymbol{\pi}$

Extremely Low -EL

0.1

0.9

0

Very Low -VL

0.2

0.8

0.1

Low -L

0.3

0.7

0.2

Medium Low -ML

0.4

0.6

0.3

Medium -M

0.5

0.5

0.4

Medium High -MH

0.6

0.4

0.3

High -H

0.7

0.3

0.2

Very High -VH

0.8

0.2

0.1

Extremely High -EH

0.9

0.1

0

Step 2: The SWAM operator, as defined in Definition 7, is used to aggregate expert opinions.

$\begin{aligned} & S W A M_{\omega_k}\left(\tilde{A}_{j k}, \ldots \ldots, \tilde{A}_{j t}\right)=\omega_1 A \tilde{A}_{j 1}+\omega_2 \tilde{A}_{j 2}+\cdots \cdots+\omega_t \tilde{A}_{j t} \\ & \tilde{z}_j=\left(\mu_j, v_j, \pi_j\right)=\left\{\begin{array}{c} {\left[1-\prod_{k=1}^t\left(1-\mu_{\tilde{A}_{j k}}^2\right)^{\omega_k}\right]^{1 / 2}, \prod_{k=1}^t v_{\tilde{A}_{j k}}^{\omega_k}}, \\ {\left[\prod_{k=1}^t\left(1-\mu_{\tilde{A}_{j k}}^2\right)^{\omega_k}-\prod_{k=1}^t\left(1-\mu_{\tilde{A}_{j k}}^2-\pi_{\tilde{A}_{j k}}^2\right)^{\omega_k}\right]^{1 / 2}} \end{array}\right\} \\ & \end{aligned}$
(9)

The weight of expert $k$ is denoted by $\omega_k$, where t represents the total number of experts. The aggregated value for criterion $j$ is represented as $Z_j$.

Step 3: The score value for each criterion is calculated according to Definition 6.

$\operatorname{Score}\left(\tilde{z}_j\right)=\left(2 \mu_j-\pi_j\right)^2-\left(v_j-\pi_j\right)^2$
(10)

Step 4: Criteria are sorted in descending order based on their score values, from highest to lowest.

Step 5: Starting from the second criterion, the comparative significance $\left(c_j\right)$ is determined by calculating the difference between the score values of the criterion $(j)$ and criterion $(j-1)$.

Step 6: The comparative coefficient $\left(k_j\right)$ is established for each criterion.

$k_j= \begin{cases}1, & j=1 \\ c_j+1, & j>1\end{cases}$
(11)

Step 7: The estimated weight for each criterion $\left(q_j\right)$ is recalculated.

$q_j= \begin{cases}1, & j=1 \\ \frac{q_{(j-1)}}{k_j}, & j>1\end{cases}$
(12)

Step 8: The recalculated weights are normalized to obtain the weight of each criterion, where $n$ represents the total number of criteria.

$w_j=\frac{q_j}{\sum_{j=1}^n q_j}$
(13)
3.3 Spherical Fuzzy WASPAS

The WASPAS approach has been widely utilized as a valuable decision-making tool across various fields, such as determining the optimal site for solid waste disposal [63], analyzing societal dynamics [77], managing reservoir flood control [78], evaluating green suppliers [78], selecting the ideal mode of transportation [79], choosing physicians [80], deciding petrol station locations [81], determining refugee camp sites [82], selecting parker locker locations [83], choosing emergency supply depot locations [84], assessing the quality of public transportation services [84], selecting air conditioning systems [85], determining bridge construction locations [86], selecting last-mile delivery modes [87], selecting hub location [88], choosing a supplier in steel industry [89], and identifying suitable locations for constructing roundabouts [90]. A comprehensive evaluation of two selected countries is conducted in this study using the integrated SF-WASPAS method based on a set of weighted criteria (strategies). The initial step involves defining the weighted criteria with the SF-SWARA method, followed by the application of the SF-WASPAS method to identify the country that has adopted the technology to a greater extent based on these established criteria. The sequential steps involved in the SF-WASPAS methodology are outlined as follows:

Step 1: A matrix for alternative assessment is established for each expert using linguistic variables (Table 1). Let $\widetilde{\mathrm{X}}_{\mathrm{ijk}}=\left(\mu_{\mathrm{ijk}}, \mathrm{v}_{\mathrm{ijk}}, \pi_{\mathrm{ijk}}\right)$ be the SFN for an evaluation of alternative $i$ concerning criterion $j$ by expert $k$.

Step 2: Expert opinions are aggregated using the SWAM operator defined in Definition 7.

$\begin{gathered} \operatorname{sWAM}_{\omega_{\mathrm{k}}}\left(\widetilde{\mathrm{X}}_{\mathrm{ijk}}, \ldots \ldots, \widetilde{\mathrm{X}}_{\mathrm{ijt}}\right)=\omega_1 \widetilde{\mathrm{X}}_{\mathrm{ij} 1}+\omega_2 \widetilde{\mathrm{X}}_{\mathrm{ij} 2}+\cdots \cdots+\omega_{\mathrm{t}} \widetilde{\mathrm{X}}_{\mathrm{ijt}} \\ \widetilde{\mathrm{R}}_{\mathrm{ij}}=\left(\mu_{\mathrm{ij}}, \mathrm{v}_{\mathrm{ij}}, \pi_{\mathrm{ij}}\right)=\left\{\begin{array}{c} {\left[1-\prod_{\mathrm{k}=1}^{\mathrm{t}}\left(1-\mu_{\widetilde{\mathrm{X}}_{\mathrm{ijk}}}^2\right)^{\omega_{\mathrm{k}}}\right]^{1 / 2}, \prod_{\mathrm{k}=1}^{\mathrm{t}} \mathrm{v}_{\widetilde{\mathrm{X}}_{\mathrm{ijk}}}^{\omega_{\mathrm{k}}}}, \\ {\left[\prod_{\mathrm{k}=1}^{\mathrm{t}}\left(1-\mu_{\widetilde{\mathrm{X}}_{\mathrm{ijk}}}^2\right)^{\omega_{\mathrm{k}}}-\prod_{\mathrm{k}=1}^{\mathrm{t}}\left(1-\mu_{\widetilde{\mathrm{X}}_{\mathrm{ijk}}}^2-\pi_{\widetilde{\mathrm{X}}_{\mathrm{ijk}}}^2\right)^{\omega_{\mathrm{k}}}\right]^{1 / 2}} \end{array}\right\} \end{gathered}$
(14)

Step 3: A weighted decision matrix is constructed, taking into account the weights of the criteria.

Step 4: Using the criteria weights obtained through SF-SWARA, WSM $\left(\widetilde{\mathrm{Q}}^1\right)$ is computed for each alternative. It is worth noting that n denotes the number of criteria.

$\widetilde{\mathrm{Q}}_{\mathrm{i}}^1=\sum_{\mathrm{i}=1}^{\mathrm{n}} \widetilde{\mathrm{R}}_{\mathrm{ijw}}$
(15)
$\widetilde{\mathrm{R}}_{\mathrm{ijw}}=\widetilde{\mathrm{R}}_{\mathrm{ij}} \mathrm{w}_{\mathrm{j}}=\left(\sqrt{1-\left(1-\mu_{\widetilde{\mathrm{R}}_{\mathrm{ij}}}^2\right)^{\mathrm{w}_{\mathrm{j}}}}, \mathrm{v}_{\widetilde{\mathrm{R}}_{\mathrm{ij}} }^{\mathrm{w}_{\mathrm{i}}}, \sqrt{\left(1-\mu_{\widetilde{\mathrm{R}}_{\mathrm{ij}}}^2\right)^{\mathrm{w}_{\mathrm{j}}}-\left(1-\mu_{\widetilde{\mathrm{R}}_{\mathrm{ij}}}^2-\pi_{\widetilde{\mathrm{R}}_{\mathrm{ij}}}^2\right)^{\mathrm{w}_{\mathrm{j}}}}\right)$
(16)

Step 5: WPM $\left(\widetilde{\mathrm{Q}}^2\right)$ is computed, taking into account the criteria weights obtained through SF-SWARA, for each alternative.

$\widetilde{\mathrm{Q}}_{\mathrm{i}}^2=\prod_{\mathrm{j}=1}^{\mathrm{n}} \widetilde{\mathrm{R}}_{\mathrm{ij}}^{\mathrm{w}_{\mathrm{j}}}$
(17)
$\widetilde{\mathrm{R}}_{\mathrm{ij}}^{\mathrm{w}_{\mathrm{j}}}=\left(\mu_{\widetilde{\mathrm{R}}_{\mathrm{ij}}}^{\mathrm{w}_{\mathrm{j}}} \sqrt{1-\left(1-\mathrm{v}_{\widetilde{\mathrm{R}}_{\mathrm{ij}}}^2\right)^{\mathrm{w}_{\mathrm{j}}}}, \sqrt{\left(1-\mathrm{v}_{\widetilde{\mathrm{R}}_{\mathrm{ij}}}^2\right)^{\mathrm{w}_{\mathrm{j}}}-\left(1-\mathrm{v}_{\widetilde{\mathrm{R}}_{\mathrm{ij}}}^2-\pi_{\widetilde{\mathrm{R}}_{\mathrm{ij}}}^2\right)^{\mathrm{w}_{\mathrm{j}}}}\right)$
(18)

Step 6: The integration of WSM and WMP is carried out by incorporating the threshold value $(\lambda)$.

$\lambda \widetilde{\mathrm{Q}}_{\mathrm{i}}^1=\left(\sqrt{1-\left(1-\mu_{\widetilde{\mathrm{Q}}_{\mathrm{i}}^1}^2\right)^\lambda}, \mathrm{v}_{\widetilde{\mathrm{Q}}_{\mathrm{i}}^1}^\lambda, \sqrt{\left(1-\mu_{\widetilde{\mathrm{Q}}_{\mathrm{i}}^1}^2\right)^\lambda-\left(1-\mu_{\widetilde{\mathrm{Q}}_i^1}^2-\pi_{\widetilde{\mathrm{Q}}_i^1}^2\right)^\lambda}\right)$
(19)
$(1-\lambda) \widetilde{\mathrm{Q}}_{\mathrm{i}}^2=\left(\sqrt{1-\left(1-\mu_{\widetilde{\mathrm{Q}}_{\mathrm{i}}^2}^2\right)^{(1-\lambda)}}, \mathrm{v}_{\widetilde{\mathrm{Q}}_{\mathrm{i}}^2}^{1-\lambda}, \sqrt{\left(1-\mu_{\widetilde{\mathrm{Q}}_i^2}^2\right)^{(1-\lambda)}-\left(1-\mu_{\widetilde{\mathrm{Q}}_{\mathrm{i}}^2}^2-\pi_{\widetilde{\mathrm{Q}}_{\mathrm{i}}^2}^2\right)^{(1-\lambda)}}\right)$
(20)

Step 7: The relative weight is determined for each alternative.

$\widetilde{\mathrm{Q}}_{\mathrm{i}}=\lambda \widetilde{\mathrm{Q}}_{\mathrm{i}}^1+(1-\lambda) \widetilde{\mathrm{Q}}_{\mathrm{i}}^2$
(21)

Step 8: The final scores are determined by de-fuzzifying the SFNs using the score function specified in Definition 6 [91].

Step 9: Alternatives are arranged in descending order based on their final scores, with the highest score indicating the best alternative.

4. Application

The study aimed to identify strategies (criteria) for enhancing the adoption of I.40, based on expert opinions and a literature review. These criteria included international collaboration and partnerships (C1), international and regional cooperation (C2), education and training (C3), open innovation initiative (C4), research, development, and innovation (C5), public-private partnership (PPP) and policy innovation (C6), and a focus on small and medium enterprises (C7). In this context, the alternatives under consideration were Kenya (A1) and Rwanda (A2). Rwanda was distinguished as one of three African nations (alongside South Africa and Morocco) that had taken steps toward developing I.40 strategies and technology centers, in addition to having ICT policies [92]. In the digital realm, Kenya had been labeled “Africa’s Silicon Savannah” due to its robust, targeted ICT policy aligned with its Vision 2030 plan [93]. To evaluate the factors promoting I4.0 implementation, a team of three experts (referred to as “Es”) was established. The Es group was responsible for assessing I4.0 adoption and included specialists in various fields such as the Internet of Things (IoTs), Big Data, Artificial Intelligence (AI), and Blockchain technologies. Specifically, two of the experts had backgrounds in Big Data and AI, while the third was knowledgeable in blockchain technology and IoTs.

4.1 Application of SF-SWARA for Weighting Strategies

A questionnaire was employed to collect data from the expert teams on the importance of each criterion. The weight assigned to each criterion by the expert panel, following the SF-SWARA method, is presented in Table 2 as linguistic indicators.

After collecting the opinions of the experts, the integration process was conducted using SWAM operators, as indicated in Table 3, while considering the experts’ weights. The determination of expert weights took into account their reputations, which were evaluated based on factors such as years of work, experience, and expertise in the subject. During the interviews with the experts, their weights were determined accordingly, resulting in E1 having a weight of 0.35, E2 with a weight of 0.30, and E3 carrying a weight of 0.35.

Following Eq. (7), the score value was calculated, and the weight of the criteria was determined using the SF-SWARA method, as presented in Table 4.

The final weights of each challenge were determined by normalizing the recalculated weights (${q}_j$) using Eq. (13). According to the findings depicted in Figure 1, experts acknowledged that education and training was the most appropriate strategy. Following education and training, the second to seventh positions were occupied by research, development, and innovation; public-private partnership and policy innovation; international and regional cooperation; international collaboration and partnerships; open innovation initiative; and small and medium enterprises focus, respectively. The normalized weight assigned to criterion C3, which pertained to education and training, was 0.410. Conversely, criterion C7, which focused on small and medium enterprises, received the least consideration from experts with a normalized weight of 0.004. In Figure 1, education and training were significantly emphasized by the experts. The findings of this study were in line with previous research conducted by Spoettl and Tūtlys [94], which highlighted the importance of vocational education and training in response to the challenges posed by I4.0. The study emphasized the need for vocational systems to cater to new technological demands while aligning with the workforce’s needs and expectations.

Table 2. Importance of criteria weights

Criteria

E1

E2

E3

C1

H

H

H

C2

VH

H

MH

C3

EH

EH

EH

C4

VH

MH

MH

C5

EH

VH

EH

C6

H

VH

H

C7

MH

M

M

Table 3. Weights of criteria according to SWAM operator

Criteria

Criterion weight

$\boldsymbol{\mu}$

$\mathbf{v}$

$\boldsymbol{\pi}$

C1

0.700

0.300

0.640

C2

0.719

0.284

0.623

C3

0.900

0.100

0.409

C4

0.690

0.314

0.642

C5

0.873

0.127

0.443

C6

0.741

0.260

0.614

C7

0.539

0.462

0.753

Table 4. Results of SF-SWARA

Criteria

Score value

$\boldsymbol{s}_{\boldsymbol{j}}$

$\boldsymbol{k}_{\boldsymbol{j}}$

$\boldsymbol{q}_{\boldsymbol{j}}$

C3

1.839

1

1

C5

1.600

0.239

1.239

0.807

C6

0.628

0.972

2.211

0.365

C2

0.549

0.079

2.290

0.159

C1

0.463

0.086

2.376

0.067

C4

0.439

0.024

2.400

0.028

C7

0.022

0.417

2.817

0.010

According to the information presented in Figure 1, research, development, and innovation were identified as the second most significant strategy, following education and training. These findings were consistent with research conducted by Schneider et al. [95], which highlighted that research efforts in the context of I4.0 had expanded beyond the development of innovative technologies to include the creation of methodological tools. Additionally, Dikhanbayeva et al. [96] found that the outcomes of research in this field were valuable for policymakers, scientists, and other stakeholders, as they could be utilized to formulate forecasts, strategic plans, and further investigations about the implementation of I4.0.

The public-private partnership and policy innovation, which ranked third in importance, served as notable strategies for I4.0 adoption within a country [97]. While the ICT policy had its limitations, it was becoming increasingly insufficient in the face of the I4.0 revolution. Consequently, the EAC should consider incorporating an I4.0 strategy alongside the existing ICT policy to maintain competitiveness. This necessitated a re-evaluation of leadership infrastructure by governments. To successfully embrace I4.0, it was crucial to implement structural transformations through the development of national policies similar to the ICT policy established by EAC member countries [21]. In this context, the effective adoption of I4.0 depended on the commitment of governments, businesses, and citizens through public-private partnerships to support societal transformation into a modern and technologically advanced society driven by innovation, advanced skills, responsive policies, and cutting-edge technology.

Figure 1. Weights of strategies for the sustainable adoption of Industry 4.0 in the East Africa community
4.2 Application of SF-WASPAS for Ranking Countries

Subsequent to the determination of strategy importance, a panel of experts, with appropriate qualifications, was employed to establish the initial decision grid using linguistic variables. This aimed at evaluating which country had adopted I4.0 technology to a greater extent, based on the identified strategies, via the SF-WASPAS approach (Table 5).

Table 5. Decision grid for the strategy evaluation

Criteria

E-1

E-2

E-3

A1

C1

H

VH

VH

C2

MH

MH

M

C3

VH

H

VH

C4

L

ML

L

C5

VL

EL

ML

C6

H

MH

H

C7

M

H

MH

A2

C1

H

H

H

C2

M

H

H

C3

EH

H

VH

C4

VL

VL

L

C5

M

ML

ML

C6

VH

VH

VH

C7

M

M

M

Initially, the linguistic variables were converted into spherical fuzzy numbers by utilizing the scale presented in Table 1. Then, the SWAM operator was employed for aggregating expert opinions and determining expert weights. This procedure resulted in the formation of a spherical fuzzy decision matrix, as depicted in Table 6.

Table 6. Spherical fuzzy decision grid

Criteria

$\boldsymbol{\mu}$

$\mathbf{v}$

$\boldsymbol{\pi}$

A1

C1

0.770

0.230

0.136

C2

0.569

0.432

0.336

C3

0.775

0.226

0.131

C4

0.334

0.668

0.237

C5

0.274

0.749

0.198

C6

0.674

0.327

0.231

C7

0.607

0.397

0.307

A2

C1

0.700

0.300

0.200

C2

0.645

0.359

0.273

C3

0.825

0.177

0.104

C4

0.240

0.763

0.145

C5

0.439

0.563

0.343

C6

0.800

0.200

0.100

C7

0.500

0.500

0.400

Following the acquisition of weights for each strategy, these values were employed to rank the countries. The execution of SF-WASPAS steps led to the calculation of the Weighted Sum Model (WSM) and Weighted Product Model (WPM) for each country, based on strategy weights (Table 7).

Table 7. The WSM and WPM models

WSM

WPM

$\boldsymbol{\mu}$

$\mathbf{v}$

$\boldsymbol{\pi}$

$\boldsymbol{\mu}$

$\mathbf{v}$

$\boldsymbol{\pi}$

A1

0.933

1.000

0.081

0.496

0.571

0.197

A2

0.950

0.000

0.041

0.499

0.567

0.182

A threshold value $(\lambda)$ of 0.5 was established to integrate the WSM and WPM models. Subsequently, the spherical fuzzy outcomes derived from the SF-WASPAS method were defuzzified. The results, obtained from the integrated SF-SWARA-WASPAS methodology, were presented as final scores, with countries ranked accordingly (Table 8).

Table 8. The classification of alternatives
RankingCountryFinal score
2A11.560
1A23.703

As indicated in Table 8, Rwanda (alternative A2) emerged as the country that had predominantly embraced strategies aimed at enhancing the adoption of I4.0 technology, with a final score of 3.703. On the other hand, Kenya was identified as the country exhibiting the lowest level of technology adoption.

5. Sensitivity Analysis

To ascertain the accuracy of the proposed method, a sensitivity analysis was conducted. This analysis entailed systematically adjusting the threshold value in increments of 0.25, ranging from 0 to 1. Subsequently, the recalculated final scores for countries were determined using the updated threshold values, as presented in Table 9.

Table 9. The final scores for different threshold values

Threshold value

0

0.25

0.5

0.75

1

A1

1.560

1.527

1.085

0.583

4.40E-05

A2

3.702

3.991

4

4

4

Figure 2. The final rankings of countries

The sensitivity analysis results indicate that altering the threshold value impacts the final scores as expected, due to the influence of WSM and WPM. However, the final ranking of countries remained consistent despite changes in threshold values. The ranking of alternatives is illustrated in Figure 2.

6. Conclusions

This study presents an approach for enhancing Industry 4.0 adoption in a spherical fuzzy environment by integrating the Weighted Aggregated Sum Product Assessment and Step-Wise Weight Assessment Ratio Analysis methods. Through a case study involving two East African Community countries, the practical effectiveness of the proposed model was demonstrated. The findings revealed that the most influential strategies for substantially improving Industry 4.0 adoption within the East African community include education and training, research and development, innovation, public-private partnerships, and policy innovation. Furthermore, the results identified Rwanda as the leading country, in comparison to Kenya, regarding the successful implementation of these strategies to promote Industry 4.0 technology adoption.

The study offers two primary contributions that can be regarded from distinct perspectives. First, it provides a framework aimed at enhancing Industry 4.0 adoption within the East African Community, outlining suitable strategies for logical and systematic implementation, thereby representing a professional contribution. Second, the study introduces a scientific contribution by applying integrated SWARA and WASPAS methods within a spherical fuzzy environment to support Industry 4.0 adoption in this specific African region. This approach is innovative and has not been explored in existing literature.

Despite the study’s significant contributions, it is essential to acknowledge its limitations. First, the proposed methodology was not compared to other fuzzy-based multicriteria procedures specifically addressing this issue, presenting an opportunity for future research to conduct comparative analyses. Second, as the data collection focused solely on the East African Community, the applicability of findings to other regional economic communities within the African Union may be limited due to their unique conditions and environments. Consequently, it is crucial to replicate the research framework used in this study across other regional economic communities and perform a comparative analysis of the outcomes. Finally, it is important to note that the data collection process involved a limited number of experts. Future research should aim to include a larger and more diverse pool of experts and establish clear criteria for their selection, ensuring comprehensive and reliable analysis.

Data Availability

The data used to support the research findings are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflict of interest.

References
1.
W. Aulbur, C. Arvind, and R. Bigghe, Skills Development for Industry 4.0. BRICS Skill Development Working Group, Roland Berger GMBH, 2016. [Online]. Available: https://rda.worldskills.ru/storage/app/media/Reports/2016_BRICS%20Skills%20Development%20for%20Industry%204.0/2016_BRICS_Skill-development-for-industry-4.0_report.pdf [Google Scholar]
2.
R. Berger, Industry 4.0. The New Industrial Revolution—How Europe will Succeed. Roland Berger Strategy Consultants GMBH, Munich, Germany, 2014. [Online]. Available: http://www.iberglobal.com/files/Roland_Berger_Industry.pdf [Google Scholar]
3.
J. Basl, “Companies on the way to industry 4.0 and their readiness,” J. Syst. Integr. (1804-2724), vol. 9, no. 3, 2018. [Google Scholar] [Crossref]
4.
S. Dabic-Miletic, “Autonomous vehicles as an essential component of industry 4.0 for meeting last-mile logistics requirements,” J. Ind. Intell., vol. 1, no. 1, pp. 55–62, 2023. [Google Scholar] [Crossref]
5.
S. Chang, H. Chang, and M. Lu, “Evaluating industry 4.0 technology application in SMEs: Using a hybrid MCDM approach,” Mathematics, vol. 9, no. 4, p. 414, 2021. [Google Scholar] [Crossref]
6.
V. Kumar, P. Vrat, and R. Shankar, “Prioritization of strategies to overcome the barriers in Industry 4.0: A hybrid MCDM approach,” Opsearch, vol. 58, no. 3, pp. 711–750, 2021. [Google Scholar] [Crossref]
7.
K. Elibal and E. Özceylan, “Comparing industry 4.0 maturity models in the perspective of TQM principles using fuzzy MCDM methods,” Technol. Forecast. Soc. Change, vol. 175, p. 121379, 2022. [Google Scholar] [Crossref]
8.
W. Torbacki, “A hybrid MCDM model combining DANP and PROMETHEE II methods for the assessment of cybersecurity in industry 4.0,” Sustainability, vol. 13, no. 16, p. 8833, 2021. [Google Scholar] [Crossref]
9.
M. Erdogan, B. Ozkan, A. Karasan, and I. Kaya, Selecting the Best Strategy for Industry 4.0 Applications with a Case Study. Springer, Cham, 2017. [Google Scholar]
10.
L. Yang, H. Zou, C. Shang, X. Ye, and P. Rani, “Adoption of information and digital technologies for sustainable smart manufacturing systems for industry 4.0 in small, medium, and micro enterprises (SMMEs),” Technol. Forecast. Soc. Change, vol. 188, p. 122308, 2023. [Google Scholar] [Crossref]
11.
A. Mardani and S. Saberi, “Industry 4.0 adoption drivers for sustainable supply chain in the manufacturing sector using a hybrid decision-making approach under q-rung orthopair fuzzy information,” IEEE Trans. Eng. Manage., pp. 1–18, 2023. [Google Scholar] [Crossref]
12.
V. Simic, S. Dabic-Miletic, E. B. Tirkolaee, Ž. Stević, A. Ala, and A. Amirteimoori, “Neutrosophic LOPCOW-ARAS model for prioritizing industry 4.0-based material handling technologies in smart and sustainable warehouse management systems,” Appl. Soft Comput., vol. 143, p. 110400, 2023. [Google Scholar] [Crossref]
13.
S. Miškić, S. Tadić, Ž. Stević, M. Krstić, and V. Roso, “A novel hybrid model for the evaluation of industry 4.0 technologies’ applicability in logistics centers,” J. Math., vol. 2023, Article ID 3532862, 2023. [Google Scholar] [Crossref]
14.
P. Rani, D. Pamucar, A. R. Mishra, M. Ibrahim Hezam, J. Ali, and S. K. Hasane Ahammad, “An integrated interval-valued Pythagorean fuzzy WISP approach for industry 4.0 technology assessment and digital transformation,” Ann. Oper. Res., 2023. [Google Scholar] [Crossref]
15.
P. Dhamija, “South Africa in the era of Industry 4.0: An insightful investigation,” Scientometrics, vol. 127, no. 9, pp. 5083–5110, 2022. [Google Scholar] [Crossref]
16.
S. M. Sackey, A. Bester, and D. Adams, “Industry 4.0 learning factory didactic design parameters for industrial engineering education in South Africa,” S. Afr. J. Ind. Eng., vol. 28, no. 1, pp. 114–124, 2017. [Google Scholar] [Crossref]
17.
M. Eustace Dogo, A. F. Salami, O. Clinton Aigbavboa, and T. Nkonyana, Taking Cloud Computing to the Extreme Edge: A Review of Mist Computing for Smart Cities and Industry 4.0 in Africa. Springer, Cham, 2018. [Online]. Available: [Google Scholar] [Crossref]
18.
J. N. Anitah, S. O. Nyamwange, P. O. Magutu, M. Chirchir, and J. M. Mose, “Industry 4.0 technologies and operational performance of unilever Kenya and L’Oreal East Africa,” Noble Int. J. Bus. Manage. Res., vol. 3, no. 10, pp. 125–134, 2019. [Google Scholar]
19.
E. Ukwandu, N. C. Ephraim Okafor, C. Ikerionwu, C. Olebara, and C. Ugwu, Assessing Cyber-Security Readiness of Nigeria to Industry 4.0. Springer, Cham, 2023. [Google Scholar]
20.
O. Mbadiwe, U. Eze, and C. Ikerionwu, “Regulation of blockchain technology in Nigeria: Need and risks mitigation towards industry 4.0,” in in 15th International Conference on Emerging Applications and Technologies for Industry, Nigeria, 2020, pp. 229–237. [Google Scholar]
21.
O. Bongomin, E. O. Nganyi, M. R. Abswaidi, E. Hitiyise, and G. Tumusiime, “Sustainable and dynamic competitiveness towards technological leadership of industry 4.0: Implications for East african community,” J. Eng., vol. 2020, pp. 1–22, 2020. [Google Scholar] [Crossref]
22.
W. Maisiri and L. van Dyk, “Industry 4.0 readiness assessment for South African industries,” S. Afr. J. Ind. Eng., vol. 30, no. 3, pp. 134–148, 2019. [Google Scholar] [Crossref]
23.
W. Maisiri, L. van Dyk, and R. Coeztee, “Factors that inhibit sustainable adoption of Industry 4.0 in the South African manufacturing industry,” Sustainability, vol. 13, no. 3, p. 1013, 2021. [Google Scholar] [Crossref]
24.
M. B. Bouraima, C. K. Kiptum, K. M. Ndiema, Y. Qiu, and I. Tanackov, “Prioritization road safety strategies towards zero road traffic injury using ordinal priority approach,” Oper. Res. Eng. Sci. Theor. Appl., vol. 5, no. 2, pp. 206–221, 2022. [Google Scholar] [Crossref]
25.
D. Pamucar, M. Deveci, I. Gokasar, D. Delen, M. Köppen, and W. Pedrycz, “Evaluation of metaverse integration alternatives of sharing economy in transportation using fuzzy Schweizer-Sklar based ordinal priority approach,” Decis. Support Syst., vol. 171, p. 113944, 2023. [Google Scholar] [Crossref]
26.
M. Deveci, I. Gokasar, D. Pamucar, Y. Chen, and D. Coffman, “Sustainable E-scooter parking operation in urban areas using fuzzy Dombi based RAFSI model,” Sustainable Cities Soc., vol. 91, p. 104426, 2023. [Google Scholar] [Crossref]
27.
F. Ecer, H. Küçükönder, S. Kayapınar Kaya, and Ö. Faruk Görçün, “Sustainability performance analysis of micro-mobility solutions in urban transportation with a novel IVFNN-Delphi-LOPCOW-CoCoSo framework,” Transp. Res. Part A, vol. 172, p. 103667, 2023. [Google Scholar] [Crossref]
28.
E. B. Tirkolaee and N. S. Aydin, “Integrated design of sustainable supply chain and transportation network using a fuzzy bi-level decision support system for perishable products,” Expert Syst. Appl., vol. 195, p. 116628, 2022. [Google Scholar] [Crossref]
29.
M. Akram, A. Khan, A. Luqman, T. Senapati, and D. Pamucar, “An extended MARCOS method for MCGDM under 2-tuple linguistic q-rung picture fuzzy environment,” Eng. Appl. Artif. Intell., vol. 120, p. 105892, 2023. [Google Scholar] [Crossref]
30.
I. Badi and M. Kridish, “Landfill site selection using a novel FUCOM-CODAS model: A case study in Libya,” Sci. Afr., vol. 9, p. e00537, 2020. [Google Scholar] [Crossref]
31.
Ž. Stević, D. Pamučar, A. Puška, and P. Chatterjee, “Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS),” Comput. Ind. Eng., vol. 140, p. 106231, 2020. [Google Scholar] [Crossref]
32.
C. K. Kiptum, M. B. Bouraima, Ž. Stević, S. Okemwa, S. Birech, and Y. Qiu, “Sustainable strategies for the successful operation of the bike-sharing system using an ordinal priority approach,” J. Eng. Manage. Syst. Eng., vol. 1, no. 2, pp. 43–50, 2022. [Google Scholar] [Crossref]
33.
M. B. Bouraima, Y. Qiu, C. K. Kiptum, and K. M. Ndiema, “Evaluation of factors affecting road maintenance in Kenyan counties using the ordinal priority approach,” J. Comput. Cognit. Eng., pp. 1–6, 2022. [Google Scholar] [Crossref]
34.
E. Celik, O. N. Bilisik, M. Erdogan, A. T. Gumus, and H. Baracli, “An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul,” Transp. Res. Part E, vol. 58, pp. 28–51, 2013. [Google Scholar] [Crossref]
35.
S. Qahtan, A. Hassan Alsattar, A. A. Zaidan, M. Deveci, D. Pamucar, and D. Delen, “Performance assessment of sustainable transportation in the shipping industry using a q-rung orthopair fuzzy rough sets-based decision making methodology,” Expert Syst. Appl., vol. 223, p. 119958, 2023. [Google Scholar] [Crossref]
36.
D. Pamucar, I. Gokasar, A. Ebadi Torkayesh, M. Deveci, L. Martínez, and Q. Wu, “Prioritization of unmanned aerial vehicles in transportation systems using the integrated stratified fuzzy rough decision-making approach with the hamacher operator,” Inf. Sci., vol. 622, pp. 374–404, 2023. [Google Scholar] [Crossref]
37.
T. Senapati, V. Simic, A. Saha, M. Dobrodolac, Y. Rong, and E. B. Tirkolaee, “Intuitionistic fuzzy power Aczel-Alsina model for prioritization of sustainable transportation sharing practices,” Eng. Appl. Artif. Intell., vol. 119, p. 105716, 2023. [Google Scholar] [Crossref]
38.
M. B. Bouraima, Ž. Stević, I. Tanackov, and Y. Qiu, “Assessing the performance of Sub-Saharan African (SSA) railways based on an integrated Entropy-MARCOS approach,” Oper. Res. Eng. Sci. Theor. Appl., vol. 4, no. 2, pp. 13–35, 2021. [Google Scholar] [Crossref]
39.
M. B. Bouraima, Y. Qiu, E. Ayyildiz, and A. Yildiz, “Prioritization of strategies for a sustainable regional transportation infrastructure by hybrid spherical fuzzy group decision-making approach,” Neural Comput. Appl., 2023. [Google Scholar] [Crossref]
40.
M. Kovač, S. Tadić, M. Krstić, and M. B. Bouraima, “Novel spherical fuzzy MARCOS method for assessment of drone-based city logistics concepts,” Complexity, vol. 2021, pp. 1–17, 2021. [Google Scholar] [Crossref]
41.
Ž. Stević, M. B. Bouraima, M. Subotić, Y. Qiu, P. A. Buah, K. M. Ndiema, and C. M. Ndjegwes, “Assessment of causes of delays in the road construction projects in the Benin Republic using fuzzy PIPRECIA method,” Math. Probl. Eng., vol. 2022, pp. 1–18, 2022. [Google Scholar] [Crossref]
42.
M. Deveci, I. Gokasar, A. R. Mishra, P. Rani, and Z. Ye, “Evaluation of climate change-resilient transportation alternatives using fuzzy Hamacher aggregation operators based group decision-making model,” Eng. Appl. Artif. Intell., vol. 119, p. 105824, 2023. [Google Scholar] [Crossref]
43.
A. Ala, V. Simic, D. Pamucar, and E. B. Tirkolaee, “Appointment scheduling problem under fairness policy in healthcare services: Fuzzy ant lion optimizer,” Expert Sys. Appl., vol. 207, p. 117949, 2022. [Google Scholar] [Crossref]
44.
M. Akram, R. Bibi, and M. Deveci, “An outranking approach with 2-tuple linguistic Fermatean fuzzy sets for multi-attribute group decision-making,” Eng. Appl. Artif. Intell., vol. 121, p. 105992, 2023. [Google Scholar] [Crossref]
45.
V. Simic, S. Dabic-Miletic, E. B. Tirkolaee, Ž. Stević, M. Deveci, and T. Senapati, “Neutrosophic CEBOM-MACONT model for sustainable management of end-of-life tires,” Appl. Soft Comput., vol. 143, p. 110399, 2023. [Google Scholar] [Crossref]
46.
I. Badi, M. B. Bouraima, and M. L. Jibril, “Risk Assessment in Construction Projects Using the Grey Theory,” J. Eng. Manage. Syst. Eng., vol. 1, no. 2, pp. 58–66, 2022. [Google Scholar] [Crossref]
47.
I. Badi, M. B. Bouraima, and L. J. Muhammad, “The role of intelligent transportation systems in solving traffic problems and reducing environmental negative impact of urban transport,” Decis. Making Anal., vol. 1, pp. 1–9, 2022. [Google Scholar]
48.
S. Moslem, Ž. Stević, I. Tanackov, and F. Pilla, “Sustainable development solutions of public transportation:An integrated IMF SWARA and fuzzy bonferroni operator,” Sustainable Cities Soc., vol. 93, p. 104530, 2023. [Google Scholar] [Crossref]
49.
D. Pamučar, A. Puška, V. Simić, I. Stojanović, and M. Deveci, “Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina function,” Eng. Appl. Artif. Intell., vol. 121, p. 106025, 2023. [Google Scholar] [Crossref]
50.
M. Deveci, I. Gokasar, D. Pamucar, A. A. Zaidan, X. Wen, and B. Brij Gupta, “Evaluation of cooperative intelligent transportation system scenarios for resilience in transportation using type-2 neutrosophic fuzzy VIKOR,” Transp. Res. Part A, vol. 172, p. 103666, 2023. [Google Scholar] [Crossref]
51.
Ö. F. Görçün, D. Pamucar, R. Krishankumar, and H. Küçükönder, “The selection of appropriate Ro-Ro Vessel in the second-hand market using the WASPAS’ Bonferroni approach in type 2 neutrosophic fuzzy environment,” Eng. Appl. Artif. Intell., vol. 117, p. 105531, 2023. [Google Scholar] [Crossref]
52.
I. Badi, D. Pamucar, L. Gigović, and S. Tatomirović, “Optimal site selection for sitting a solar park using a novel GIS- SWA’TEL model: A case study in Libya,” Int. J. Green Energy, vol. 18, no. 4, pp. 336–350, 2021. [Google Scholar] [Crossref]
53.
S. Hashemkhani Zolfani, M. H. Aghdaie, A. Derakhti, E. K. Zavadskas, and M. H. Morshed Varzandeh, “Decision making on business issues with foresight perspective; an application of new hybrid MCDM model in shopping mall locating,” Expert Sys. Appl., vol. 40, no. 17, pp. 7111–7121, 2013. [Google Scholar] [Crossref]
54.
M. Göksel Saraç, T. Dedebaş, E. Hastaoğlu, and E. Arslan, “Influence of using scarlet runner bean flour on the production and physicochemical, textural, and sensorial properties of vegan cakes: WASPAS-SWARA techniques,” Int. J. Gastronomy Food Sci., vol. 27, p. 100489, 2022. [Google Scholar] [Crossref]
55.
G. N. Yücenur and A. Ipekçi, “SWARA/WASPAS methods for a marine current energy plant location selection problem,” Renewable Energy, vol. 163, pp. 1287–1298, 2021. [Google Scholar] [Crossref]
56.
S. Agarwal, R. Kant, and R. Shankar, “Evaluating solutions to overcome humanitarian supply chain management barriers: A hybrid fuzzy SWARA – Fuzzy WASPAS approach,” Int. J. Disaster Risk Reduct., vol. 51, p. 101838, 2020. [Google Scholar] [Crossref]
57.
S. S. Hosseini Dehshiri, “New hybrid multi criteria decision making method for offshore windfarm site location in Persian Gulf, Iran,” Ocean Eng., vol. 256, p. 111498, 2022. [Google Scholar] [Crossref]
58.
S. J. Ghoushchi, S. R. Bonab, A. M. Ghiaci, G. Haseli, H. Tomaskova, and M. Hajiaghaei-Keshteli, “Landfill site selection for medical waste using an integrated SWARA-WASPAS framework based on spherical fuzzy set,” Sustainability, vol. 13, no. 24, p. 13950, 2021. [Google Scholar] [Crossref]
59.
E. Ayyildiz and A. Taskin, “A novel spherical fuzzy AHP-VIKOR methodology to determine serving petrol station selection during COVID-19 lockdown: A pilot study for İstanbul,” Socio Econ. Plann. Sci., vol. 83, p. 101345, 2022. [Google Scholar] [Crossref]
60.
F. Kutlu Gündoğdu and C. Kahraman, “Spherical fuzzy sets and spherical fuzzy TOPSIS method,” J. Intell. Fuzzy Syst., vol. 36, no. 1, pp. 337–352, 2019. [Google Scholar] [Crossref]
61.
F. Kutlu Gündoğdu and C. Kahraman, “A novel spherical fuzzy analytic hierarchy process and its renewable energy application,” Soft Comput., vol. 24, no. 6, pp. 4607–4621, 2019. [Google Scholar] [Crossref]
62.
C. Kahraman, F. Kutlu Gundogdu, S. Cevik Onar, and B. Oztaysi, “Hospital location selection using spherical fuzzy TOPSIS,” in Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), 2019, pp. 77–82. [Google Scholar] [Crossref]
63.
A. Mardani, M. Nilashi, N. Zakuan, N. Loganathan, S. Soheilirad, M. Z. M. Saman, and O. Ibrahim, “A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments,” Appl. Soft Comput., vol. 57, pp. 265–292, 2017. [Google Scholar] [Crossref]
64.
S. Hashemkhani Zolfani, M. Yazdani, and E. K. Zavadskas, “An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process,” Soft Comput., vol. 22, pp. 7399–7405, 2018. [Google Scholar] [Crossref]
65.
M. Akram, S. Naz, F. Feng, and A. Shafiq, “Assessment of hydropower plants in Pakistan: Muirhead mean-based 2-tuple linguistic t-spherical fuzzy model combining SWARA with COPRAS,” Arab. J. Sci. Eng., vol. 48, no. 5, pp. 5859–5888, 2022. [Google Scholar] [Crossref]
66.
S. Banihashemi, M. Khalilzadeh, J. Antucheviciene, and J. Šaparauskas, “Trading off time–cost–quality in construction project scheduling problems with fuzzy SWARA–TOPSIS approach,” Buildings, vol. 11, no. 9, p. 387, 2021. [Google Scholar] [Crossref]
67.
M. B. Bouraima, Y. Qiu, Ž. Stević, and V. Simić, “Assessment of alternative railway systems for sustainable transportation using an integrated IRN SWARA and IRN CoCoSo model,” Socio Econ. Plann. Sci., vol. 86, p. 101475, 2023. [Google Scholar] [Crossref]
68.
M. B. Bouraima, N. A. Tengecha, Ž. Stević, V. Simić, and Y. Qiu, “An integrated fuzzy MCDM model for prioritizing strategies for successful implementation and operation of the bus rapid transit system,” Ann. Oper. Res., 2023. [Google Scholar] [Crossref]
69.
H. Garg, J. Vimala, S. Rajareega, D. Preethi, and L. Perez-Dominguez, “Complex intuitionistic fuzzy soft SWARA - COPRAS approach: An application of ERP software selection,” AIMS Math., vol. 7, no. 4, pp. 5895–5909, 2022. [Google Scholar] [Crossref]
70.
M. B. Bouraima, Y. Qiu, Ž. Stević, D. Marinković, and M. Deveci, “Integrated intelligent decision support model for ranking regional transport infrastructure programmes based on performance assessment,” Expert Syst. Appl., vol. 222, p. 119852, 2023. [Google Scholar] [Crossref]
71.
M. Keshavarz-Ghorabaee, M. Amiri, E. Zavadskas, Z. Turskis, and J. Antucheviciene, “An extended step-wise weight assessment ratio analysis with symmetric interval type-2 fuzzy sets for determining the subjective weights of criteria in multi-criteria decision-making problems,” Symmetry, vol. 10, no. 4, p. 91, 2018. [Google Scholar] [Crossref]
72.
A. R. Mishra, P. Rani, K. Pandey, A. Mardani, J. Streimikis, D. Streimikiene, and M. Alrasheedi, “Novel multi-criteria intuitionistic fuzzy SWARA–COPRAS approach for sustainability evaluation of the bioenergy production process,” Sustainability, vol. 12, no. 10, p. 4155, 2020. [Google Scholar] [Crossref]
73.
P. Rani, A. R. Mishra, R. Krishankumar, A. Mardani, F. Cavallaro, K. Soundarapandian Ravichandran, and K. Balasubramanian, “Hesitant fuzzy SWARA-complex proportional assessment approach for sustainable supplier selection (HF-SWARA-COPRAS),” Symmetry, vol. 12, no. 7, p. 1152, 2020. [Google Scholar] [Crossref]
74.
P. Rani, A. R. Mishra, A. Mardani, F. Cavallaro, D. Štreimikienė, and S. A. R. Khan, “Pythagorean fuzzy SWARA–VIKOR framework for performance evaluation of solar panel selection,” Sustainability, vol. 12, no. 10, p. 4278, 2020. [Google Scholar] [Crossref]
75.
A. Ulutaş, C. B. Karakuş, and A. Topal, “Location selection for logistics center with fuzzy SWARA and CoCoSo methods,” J. Intell. Fuzzy Syst., vol. 38, no. 4, pp. 4693–4709, 2020. [Google Scholar] [Crossref]
76.
S. Jafarzadeh Ghoushchi, S. Shaffiee Haghshenas, A. Memarpour Ghiaci, G. Guido, and A. Vitale, “Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment,” Neural Comput. Appl., vol. 35, no. 6, pp. 4549–4567, 2022. [Google Scholar] [Crossref]
77.
M. Deveci, D. Pamucar, I. Gokasar, M. Isik, and D. Coffman, “Fuzzy Einstein WASPAS approach for the economic and societal dynamics of the climate change mitigation strategies in urban mobility planning,” Struct. Change Econ. Dyn., vol. 61, pp. 1–17, 2022. [Google Scholar] [Crossref]
78.
A. R. Mishra and P. Rani, “Interval-valued intuitionistic fuzzy WASPAS method: application in reservoir flood control management policy,” Group Decis. Negot., vol. 27, no. 6, pp. 1047–1078, 2018. [Google Scholar] [Crossref]
79.
D. Pamucar, M. Deveci, F. Canıtez, and V. Lukovac, “Selecting an airport ground access mode using novel fuzzy LBWA-WASPAS-H decision making model,” Eng. Appl. Artif. Intell., vol. 93, p. 103703, 2020. [Google Scholar] [Crossref]
80.
P. Rani, A. R. Mishra, and K. R. Pardasani, “A novel WASPAS approach for multi-criteria physician selection problem with intuitionistic fuzzy type-2 sets,” Soft Comput., vol. 24, no. 3, pp. 2355–2367, 2019. [Google Scholar] [Crossref]
81.
E. Ayyildiz and A. Taskin Gumus, “A novel spherical fuzzy AHP-integrated spherical WASPAS methodology for petrol station location selection problem: a real case study for İstanbul,” Environ. Sci. Pollut. Res., vol. 27, no. 29, pp. 36109–36120, 2020. [Google Scholar] [Crossref]
82.
E. Ayyildiz, M. Erdogan, and A. Taskin Gumus, “A Pythagorean fuzzy number-based integration of AHP and WASPAS methods for refugee camp location selection problem: a real case study for Istanbul, Turkey,” Neural Comput. Appl., vol. 33, no. 22, pp. 15751–15768, 2021. [Google Scholar] [Crossref]
83.
B. Yalcin Kavus, E. Ayyildiz, P. Gulum Tas, and A. Taskin, “A hybrid Bayesian BWM and Pythagorean fuzzy WASPAS-based decision-making framework for parcel locker location selection problem,” Environ. Sci. Pollut. Res., pp. 1–18, 2022. [Google Scholar] [Crossref]
84.
E. Ayyildiz and A. Taskin, A Novel Interval Valued Neutrosophic AHP-WASPAS Methodology for Emergency Supply Depot Location Selection Problems. CRC Press, New York, FL, 2022. [Google Scholar]
85.
T. Senapati and G. Chen, “Picture fuzzy WASPAS technique and its application in multi-criteria decision-making,” Soft Comput., vol. 26, no. 9, pp. 4413–4421, 2022. [Google Scholar] [Crossref]
86.
T. Senapati, R. Ronald Yager, and G. Chen, “Cubic intuitionistic WASPAS technique and its application in multi-criteria decision-making,” J. Ambient Intell. Human Comput., vol. 12, no. 9, pp. 8823–8833, 2021. [Google Scholar] [Crossref]
87.
V. Simić, D. Lazarević, and M. Dobrodolac, “Picture fuzzy WASPAS method for selecting last-mile delivery mode: a case study of Belgrade,” Eur. Transp. Res. Rev., vol. 13, no. 1, pp. 1–22, 2021. [Google Scholar] [Crossref]
88.
N. Aydin and S. Seker, “WASPAS based MULTIMOORA method under IVIF environment for the selection of hub location,” J. Enterp. Inf. Manage., vol. 33, no. 5, pp. 1233–1256, 2020. [Google Scholar] [Crossref]
89.
T. Nguyen, P. Nguyen, H. Pham, T. Nguyen, D. Nguyen, T. Tran, H. Le, and H. Phung, “A novel integrating data envelopment analysis and spherical fuzzy MCDM approach for sustainable supplier selection in steel industry,” Mathematics, vol. 10, no. 11, p. 1897, 2022. [Google Scholar] [Crossref]
90.
Ž. Stević, D. Pamučar, M. Subotić, J. Antuchevičiene, and E. K. Zavadskas, “The location selection for roundabout construction using Rough BWM-Rough WASPAS approach based on a new Rough Hamy aggregator,” Sustainability, vol. 10, no. 8, p. 2817, 2018. [Google Scholar] [Crossref]
91.
F. Kutlu Gundogdu and C. Kahraman, “Extension of WASPAS with spherical fuzzy sets,” Informatica, vol. 30, no. 2, pp. 269–292, 2019. [Google Scholar]
92.
G. Mukwawaya, B. Emwanu, and S. Mdakane, “Assessing the readiness of South Africa for Industry 4.0-analysis of government policy, skills and education,” in Proceedings of the International Conference on Industrial Engineering and Operations Management  Pretoria/Johannesburg, South Africa, 2018. [Google Scholar]
93.
C. Akamanzi, P. Deutscher, B. Guerich, A. Lobelle, and A. Ooko-Ombaka, “Silicon Savannah: the Kenya ICT services cluster,” Microecon. Competitiveness, vol. 7, pp. 36–49, 2016. [Google Scholar]
94.
G. Spoettl and V. Tūtlys, “Education and training for the fourth industrial revolution,” Jurnal Pendidikan Teknologi dan Kejuruan, vol. 26, no. 1, pp. 83–93, 2020. [Google Scholar] [Crossref]
95.
D. Schneider, T. Huth, and T. Vietor, “Development of an Industry 4.0 method and knowledge platform for strategic technology implementation,” Procedia CIRP, vol. 100, pp. 613–618, 2021. [Google Scholar] [Crossref]
96.
D. Dikhanbayeva, A. Tokbergenova, Y. Lukhmanov, E. Shehab, Z. Pastuszak, and A. Turkyilmaz, “Critical factors of industry 4.0 implementation in an emerging country: Empirical study,” Future Internet, vol. 13, no. 6, p. 137, 2021. [Google Scholar] [Crossref]
97.
R. M. Mitra, “Digital transformation and industry 4.0 in Southeast Asia,” Digital Asia, pp. 109–133, 2019. [Google Scholar]

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Qiu, Y. J., Bouraima, M. B., Kiptum, C. K., Ayyildiz, E., Stević, Ž., Badi, I., & Ndiema, K. M. (2023). Strategies for Enhancing Industry 4.0 Adoption in East Africa: An Integrated Spherical Fuzzy SWARA-WASPAS Approach. J. Ind Intell., 1(2), 87-100. https://doi.org/10.56578/jii010202
Y. J. Qiu, M. B. Bouraima, C. K. Kiptum, E. Ayyildiz, Ž. Stević, I. Badi, and K. M. Ndiema, "Strategies for Enhancing Industry 4.0 Adoption in East Africa: An Integrated Spherical Fuzzy SWARA-WASPAS Approach," J. Ind Intell., vol. 1, no. 2, pp. 87-100, 2023. https://doi.org/10.56578/jii010202
@research-article{Qiu2023StrategiesFE,
title={Strategies for Enhancing Industry 4.0 Adoption in East Africa: An Integrated Spherical Fuzzy SWARA-WASPAS Approach},
author={Yanjun Qiu and Mouhamed Bayane Bouraima and Clement Kiprotich Kiptum and Ertugrul Ayyildiz and žEljko Stević and Ibrahim Badi and Kevin Maraka Ndiema},
journal={Journal of Industrial Intelligence},
year={2023}
}
Yanjun Qiu, et al. "Strategies for Enhancing Industry 4.0 Adoption in East Africa: An Integrated Spherical Fuzzy SWARA-WASPAS Approach." Journal of Industrial Intelligence, v 1, pp 87-100. doi: https://doi.org/10.56578/jii010202
Yanjun Qiu, Mouhamed Bayane Bouraima, Clement Kiprotich Kiptum, Ertugrul Ayyildiz, žEljko Stević, Ibrahim Badi and Kevin Maraka Ndiema. "Strategies for Enhancing Industry 4.0 Adoption in East Africa: An Integrated Spherical Fuzzy SWARA-WASPAS Approach." Journal of Industrial Intelligence, 1, (2023): 87-100. doi: https://doi.org/10.56578/jii010202
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©2023 by the authors. Licensee Acadlore Publishing Services Limited, Hong Kong. This article can be downloaded for free, and reused and quoted with a citation of the original published version, under the CC BY 4.0 license.
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Figure 1. Weights of strategies for the sustainable adoption of Industry 4.0 in the East Africa community
Table 1. Definition and fuzzy rate of linguistic terms [76]
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