Processing, Please wait...

  • Home
  • About Us
  • Search:
  • Advanced Search

Growing Science » Journal of Project Management » Implementation of artificial intelligence project, quality internal and external supply chain integration, responsiveness for operational performance in manufacturing

Journals

  • IJIEC (747)
  • MSL (2643)
  • DSL (668)
  • CCL (508)
  • USCM (1092)
  • ESM (413)
  • AC (562)
  • JPM (271)
  • IJDS (912)
  • JFS (91)
  • HE (32)
  • SCI (26)

JPM Volumes

    • Volume 1 (8)
      • Issue 1 (5)
      • Issue 2 (3)
    • Volume 2 (13)
      • Issue 1 (4)
      • Issue 2 (3)
      • Issue 3 (3)
      • Issue 4 (3)
    • Volume 3 (17)
      • Issue 1 (4)
      • Issue 2 (5)
      • Issue 3 (4)
      • Issue 4 (4)
    • Volume 4 (24)
      • Issue 1 (4)
      • Issue 2 (8)
      • Issue 3 (8)
      • Issue 4 (4)
    • Volume 5 (20)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)
    • Volume 6 (20)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)
    • Volume 7 (21)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (6)
    • Volume 8 (21)
      • Issue 1 (6)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)
    • Volume 9 (35)
      • Issue 1 (6)
      • Issue 2 (5)
      • Issue 3 (9)
      • Issue 4 (15)
    • Volume 10 (68)
      • Issue 1 (15)
      • Issue 2 (21)
      • Issue 3 (13)
      • Issue 4 (19)
    • Volume 11 (24)
      • Issue 1 (24)

Keywords

Supply chain management(166)
Jordan(161)
Vietnam(149)
Customer satisfaction(120)
Performance(113)
Supply chain(110)
Service quality(98)
Competitive advantage(95)
Tehran Stock Exchange(94)
SMEs(87)
optimization(86)
Financial performance(83)
Trust(83)
TOPSIS(83)
Sustainability(81)
Job satisfaction(80)
Factor analysis(78)
Social media(78)
Knowledge Management(77)
Artificial intelligence(77)


» Show all keywords

Authors

Naser Azad(82)
Mohammad Reza Iravani(64)
Zeplin Jiwa Husada Tarigan(63)
Endri Endri(45)
Muhammad Alshurideh(42)
Hotlan Siagian(39)
Jumadil Saputra(36)
Dmaithan Almajali(36)
Muhammad Turki Alshurideh(35)
Barween Al Kurdi(32)
Ahmad Makui(32)
Basrowi Basrowi(31)
Hassan Ghodrati(31)
Mohammad Khodaei Valahzaghard(30)
Sautma Ronni Basana(29)
Shankar Chakraborty(29)
Ni Nyoman Kerti Yasa(29)
Sulieman Ibraheem Shelash Al-Hawary(28)
Prasadja Ricardianto(28)
Haitham M. Alzoubi(27)


» Show all authors

Countries

Iran(2183)
Indonesia(1290)
India(787)
Jordan(786)
Vietnam(504)
Saudi Arabia(453)
Malaysia(441)
United Arab Emirates(220)
China(206)
Thailand(153)
United States(111)
Turkey(106)
Ukraine(104)
Egypt(98)
Canada(92)
Peru(88)
Pakistan(85)
United Kingdom(80)
Morocco(79)
Nigeria(78)


» Show all countries

Journal of Project Management

ISSN 2371-8374 (Online) - ISSN 2371-8366 (Print)
Quarterly Publication
Volume 10 Issue 4 pp. 689-702 , 2025

Implementation of artificial intelligence project, quality internal and external supply chain integration, responsiveness for operational performance in manufacturing Pages 689-702 Right click to download the paper Download PDF

Authors: Zeplin Jiwa Husada Tarigan, Maria Natalia Damayanti Maer, Mariana Ing Malelak, Sautma Ronni Basana, Hotlan Siagian, Zarul Azhar bin Nasir

DOI: 10.5267/j.jpm.2025.8.002

Keywords: Artificial intelligence, Operational performance, Supply chain quality, Integration, and responsiveness

Abstract: This paper analyzes the effect of Artificial Intelligence (AI) implementation projects on the operational performance of manufacturing companies in Indonesia, considering the mediating role of quality internal integration, quality external integration, and supply chain responsiveness. This study uses a quantitative research approach, utilizing a survey method, with 109 manufacturing companies in Java and Bali that have adopted AI technology in their production processes. Data processing was carried out using the Partial Least Squares (PLS) approach. The results indicate that the AI implementation project has a significant impact on both internal quality integration and external quality integration but does not directly affect supply chain responsiveness or operational performance. Quality internal integration is proven to be a key variable that significantly affects quality external integration, supply chain responsiveness, and operational performance. Meanwhile, quality external integration has a significant effect on supply chain responsiveness but not on operational performance directly. Additionally, supply chain responsiveness has been proven to positively contribute to improving operational performance. The conceptual model developed in this study successfully demonstrates a multi-layered influence path from AI implementation project to operational performance through quality integration and supply chain responsiveness. This study highlights the importance of synergy between AI technology and internal and externally integrated quality management systems in achieving operational excellence in the manufacturing sector. These findings expand the theoretical understanding of the strategic role of AI in the context of supply chain and operational quality.

How to cite this paper
Tarigan, Z., Maer, M., Malelak, M., Basana, S., Siagian, H & Nasir, Z. (2025). Implementation of artificial intelligence project, quality internal and external supply chain integration, responsiveness for operational performance in manufacturing.Journal of Project Management, 10(4), 689-702.

Refrences
Abadie, A., Roux, M., Chowdhury, S., & Dey, P. (2023). Interlinking organisational resources, AI adoption and omnichannel integration quality in Ghana’s healthcare supply chain. Journal of Business Research, 162, 113866, https://doi.org/10.1016/j.jbusres.2023.113866 Acquah, I.N., Kumi, C.A., Asamoah, D., Agyei-Owusu, B., Agbodza, M. & Agyabeng-Mensah, Y. (2023a). Unearthing the relationship between supply chain social capital and firm performance: The role of supply chain responsiveness. Benchmarking: An International Journal. https://doi.org/10.1108/BIJ-01-2022-0002 Acquah, I.S.K., Quaicoe, J. & Arhin, M. (2023b). How to invest in total quality management practices for enhanced operational performance: findings from PLS-SEM and fsQCA. The TQM Journal, 35(7), 1830-1859. https://doi.org/10.1108/TQM-05-2022-0161 Al-khatib, A.W., AL-Shboul, M.A., & Khattab, M. (2024). How can generative artificial intelligence improve digital supply chain performance in manufacturing firms? Analyzing the mediating role of innovation ambidexterity using hybrid analysis through CB-SEM and PLS-SEM. Technology in Society, 78, 102676, https://doi.org/10.1016/j.techsoc.2024.102676 Asamoah, D., Nuertey, D., Agyei-Owusu, B. & Akyeh, J. (2021). The effect of supply chain responsiveness on customer development. The International Journal of Logistics Management, 32(4), 1190-1213. https://doi.org/10.1108/IJLM-03-2020-0133 Baryannis, G., Validi, S., & Dani, S. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202, doi.org/10.1080/00207543.2018.1530476 Basana, S.R., Malelak, M.I., Suprapto, W., Tarigan, Z.J.H., Tarigan, Z.V.B., & Doron, R.O. (2025). The influence of information technology integration on firm performance through supply chain quality and supply chain resilience. Decision Science Letters, 14(1), 225-238, DOI: 10.5267/j.dsl.2024.9.004 Chen, W., Liu, C., Xing, F., Peng, G. & Yang, X. (2022). Establishment of a maturity model to assess the development of industrial AI in smart manufacturing. Journal of Enterprise Information Management, 35 (3), 701-728. https://doi.org/10.1108/JEIM-10-2020-0397 Chunsheng, L., Wong, C.W., Yang, C.-C., Shang, K.-C., & Lirn, T.-C. (2020). Value of supply chain resilience: Roles of culture, flexibility, and integration. International Journal of Physical Distribution & Logistics Management, 50(1), 80–100, DOI 10.1108/IJPDLM-02-2019-0041 Dhamija, P. & Bag, S. (2020). Role of artificial intelligence in operations environment: a review and bibliometric analysis. The TQM Journal, 32(4), 869-896. https://doi.org/10.1108/TQM-10-2019-0243 Frederico, G.F., Kumar, V. & Garza-Reyes, J.A. (2021). Impact of the strategic sourcing process on the supply chain response to the COVID-19 effects. Business Process Management Journal, 27(6), 1775-1803. https://doi.org/10.1108/BPMJ-01-2021-0050 Giuggioli, G. & Pellegrini, M.M. (2023). Artificial intelligence as an enabler for entrepreneurs: a systematic literature review and an agenda for future research. International Journal of Entrepreneurial Behavior & Research, 29(4), 816-837. https://doi.org/10.1108/IJEBR-05-2021-0426 Goswami, M., Daultani, Y. & Ramkumar, M. (2024). Leveraging product quality and price for attainment of the manufacturer's economic objectives. International Journal of Quality & Reliability Management, 41(2), 469–488. https://doi.org/10.1108/IJQRM-11-2022-0335 Helo, P. & Hao, Y. (2022). Artificial intelligence in operations management and supply chain management: an exploratory case study. Production Planning & Control, 33(16), 1573-1590, DOI: 10.1080/09537287.2021.1882690 Huo, B., Ye, Y., Zhao, X., & Zhu, K. (2019). Supply chain quality integration: A taxonomy perspective. International Journal of Production Economics, 207, 236-246, https://doi.org/10.1016/j.ijpe.2016.05.004 Kaplan, A. & Haenlein, M. (2019). Rulers of the world, unite! the challenges and opportunities of artificial intelligence. Business Horizons, 63(1), 37-50, https://doi.org/10.1016/j.bushor.2019.09.003 Kim, S.Y. & Kim, J. (2025). The impact of AI recommendation quality on service satisfaction: the moderating roles of standardization and customization. Journal of Services Marketing, 39(4), 365-386. https://doi.org/10.1108/JSM-05-2024-0214 Kumar, A., Bhattacharyya, S.S. & Krishnamoorthy, B. (2023). Automation-augmentation paradox in organizational artificial intelligence technology deployment capabilities; an empirical investigation for achieving simultaneous economic and social benefits. Journal of Enterprise Information Management, 36(6), 1556–1582. https://doi.org/10.1108/JEIM-09-2022-0307 Leoni, L., Ardolino, M., El Baz, J., Gueli, G. & Bacchetti, A. (2022). The mediating role of knowledge management processes in the effective use of artificial intelligence in manufacturing firms. International Journal of Operations & Production Management, 42(13), 411-437. https://doi.org/10.1108/IJOPM-05-2022-0282 Li, D., Zhao, Y., Zhang, L., Chen, X., and Cao, C. (2018). Impact of quality management on green innovation. Journal of Cleaner Production, 170(1), 462-470, doi: 10.1016/j.jclepro.2017.09.158. Marjerison, R. K., Jun, J. Y., & Kim, J. M. (2025). The moderating effects of operations and supply chain issues on digital readiness, value creation, and firm satisfaction. Systems, 13(5), 369. https://doi.org/10.3390/systems13050369 Mota, B., Faria, P., Ramos, C., & Vale, Z. (2025). Review of manufacturing integration between production, maintenance and quality artificial intelligence systems. Journal of Industrial Information Integration, doi:https://doi.org/10.1016/j.jii.2025.100910 Mukherjee, S., Baral, M.M., Nagariya, R., Chittipaka, V. & Pal, S.K. (2024). Artificial intelligence-based supply chain resilience for improving firm performance in emerging markets. Journal of Global Operations and Strategic Sourcing, 17(3), 516–540. https://doi.org/10.1108/JGOSS-06-2022-0049 Munir, M., Jajja, M.S.S. & Chatha, K.A. (2022). Capabilities for enhancing supply chain resilience and responsiveness in the COVID-19 pandemic: Exploring the role of improvisation, anticipation, and data analytics capabilities. International Journal of Operations & Production Management, 42(10), 1576–1604. https://doi.org/10.1108/IJOPM-11-2021-0677 Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, B.E., Kazancoglu, Y. & Narwane, V. (2022). Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. The International Journal of Logistics Management, 33(3), 744–772. https://doi.org/10.1108/IJLM-12-2020-0493 Nenavani, J. & Jain, R.K. (2022). Examining the impact of strategic supplier partnership, customer relationship and supply chain responsiveness on operational performance: the moderating effect of demand uncertainty. Journal of Business & Industrial Marketing, 37(5), 995-1011. https://doi.org/10.1108/JBIM-10-2020-0461 Odugbesan, J.A., Aghazadeh, S., Al Qaralleh, R.E. & Sogeke, O.S. (2023). Green talent management and employees' innovative work behavior: the roles of artificial intelligence and transformational leadership. Journal of Knowledge Management, 27(3), 696–716. https://doi.org/10.1108/JKM-08-2021-0601 Paesano, A. (2023). Artificial intelligence and creative activities inside organizational behavior. International Journal of Organizational Analysis, 31(5), 1694-1723. https://doi.org/10.1108/IJOA-09-2020-2421 Phan, A.C., Nguyen, H.A., Trieu, P.D., Nguyen, H.T. & Matsui, Y. (2019). Impact of supply chain quality management practices on operational performance: empirical evidence from manufacturing companies in Vietnam. Supply Chain Management: An International Journal, 24(6), 855–871, doi: 10.1108/SCM-12-2018-0445. Pirmanta, P., Tarigan, Z., & Basana, S. (2021). The effect of ERP on firm performance through information quality and supply chain integration in Covid-19 era. Uncertain Supply Chain Management, 9(3), 659-666. DOI: 10.5267/j.uscm.2021.5.004 Sègbotangni, E.A., Laguir, I., & Gupta, S. (2025). Exploring the effect of supply chain integration and supply chain transparency on SME environmental performance under conditions of environmental unpredictability. Journal of Environmental Management, 375, 124225, https://doi.org/10.1016/j.jenvman.2025.124225 Serrano-Torres, G. J., López-Naranjo, A. L., Larrea-Cuadrado, P. L., & Mazón-Fierro, G. (2025). Transformation of the dairy supply chain through artificial intelligence: a systematic review. Sustainability, 17(3), 982. https://doi.org/10.3390/su17030982 Siagian, H., Tarigan, Z.J.H., & Basana, R.B. (2022). The role of top management commitment in enhancing competitive advantage: The mediating role of green innovation, supplier, and customer integration. Uncertain Supply Chain Management, 10(2), 477-494, DOI: 10.5267/j.uscm.2021.12.003 Sharma, S. & Modgil, S. (2020). TQM, SCM and operational performance: an empirical study of Indian pharmaceutical industry. Business Process Management Journal, 26(1), 331-370. https://doi.org/10.1108/BPMJ-01-2018-0005 Tarigan, Z.J.H., Tanuwijaya, N.C., & Siagian, H. (2020). Does top management attentiveness affect green performance through green purchasing and supplier collaboration? Academy of Strategic Management Journal, 19(4), 1-10 Upadhyay, N., Upadhyay, S., Al-Debei, M.M., Baabdullah, A.M. & Dwivedi, Y.K. (2023). The influence of digital entrepreneurship and entrepreneurial orientation on intention of family businesses to adopt artificial intelligence: examining the mediating role of business innovativeness. International Journal of Entrepreneurial Behavior & Research, 29(1), 80-115. https://doi.org/10.1108/IJEBR-02-2022-0154 Wamba-Taguimdje, S.L., Wamba, S.F., Kamdjoug, J.R.K, & Wanko, C.E.T. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924, https://doi.org/10.1108/BPMJ-10-2019-0411 Younis, H., Sundarakani, B. & Alsharairi, M. (2022). Applications of artificial intelligence and machine learning within supply chains: Systematic review and future research directions. Journal of Modelling in Management, 17(3), 916–940. https://doi.org/10.1108/JM2-12-2020-0322 Wang, S. & Zhang, H. (2025). Enhancing environmental, social, and governance performance through artificial intelligence supply chains in the energy industry: Roles of innovation, collaboration, and proactive sustainability strategy. Renewable Energy, 245, 122855, https://doi.org/10.1016/j.renene.2025.122855 Wang, J., Zhao, M., Huang, X., Song, Z., & Sun, D. (2024). Supply chain diffusion mechanisms for AI applications: A perspective on audit pricing. International Review of Financial Analysis, 93, 103113, https://doi.org/10.1016/j.irfa.2024.103113 Wungkana, F.A., Siagian, H. & Tarigan, Z.J.H. (2023). The influence of eco-design, green information systems, green manufacturing, and green purchasing on manufacturing performance. International Journal of Data and Network Science, 7(3), 1045-1058, DOI: 10.5267/j.ijdns.2023.6.001
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: Journal of Project Management | Year: 2025 | Volume: 10 | Issue: 4 | Views: 1007 | Reviews: 0

Related Articles:
  • The impact of configuration management decisions on firm resilience: Integr ...
  • The influence of information technology integration on firm performance thr ...
  • The influence of information technology on supply chain resilience through ...
  • The effect of integrated information technology on competitive advantage th ...
  • The effect of key user capability on supply chain digital and flexibility i ...

Add Reviews

Name:*
E-Mail:
Review:
Bold Italic Underline Strike | Align left Center Align right | Insert smilies Insert link URLInsert protected URL Select color | Add Hidden Text Insert Quote Convert selected text from selection to Cyrillic (Russian) alphabet Insert spoiler
winkwinkedsmileam
belayfeelfellowlaughing
lollovenorecourse
requestsadtonguewassat
cryingwhatbullyangry
Security Code: *
Include security image CAPCHA.
Refresh Code

® 2010-2026 GrowingScience.Com