Skip to main content

Advertisement

Springer Nature Link
Log in

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

  • Original Research
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  • Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2020). Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03620-w.

    Article  Google Scholar 

  • Ali, A., Mahfouz, A., & Arisha, A. (2017). Analysing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review. Supply Chain Management, 22(1), 16–39.

    Google Scholar 

  • Altay, N., Gunasekaran, A., Dubey, R., & Childe, S. J. (2018). Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: A dynamic capability view. Production Planning & Control, 29(14), 1158–1174.

    Google Scholar 

  • Arlbjørn, J. S., Haas, H. d., & Munksgaard, K. B. (2011). Exploring supply chain innovation. Logistics Research, 3, 3–18.

    Google Scholar 

  • Armstrong, J. S., & Overton, T. S. (1977). Estimating non-response bias in mail surveys. Journal of marketing research, 14(3), 396–402.

    Google Scholar 

  • Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (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.

    Google Scholar 

  • Belhadi, A., Kamble, S., Jabbour, C. J., Gunasekaran, A., Ndubisi, N. O., & Venkatesh, M. (2020a). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2020.120447.

    Article  PubMed  PubMed Central  Google Scholar 

  • Belhadi, A., Kamble, S. S., Zkik, K., Cherrafi, A., & Touriki, F. E. (2020b). The integrated effect of Big Data Analytics, Lean Six Sigma and Green Manufacturing on the environmental performance of manufacturing companies: The case of North Africa. Journal of Cleaner Production, 252, 119903.

    Google Scholar 

  • Belhadi, A., Zkik, K., Cherrafi, A., Yusof, S. M., & Elfezazi, S. (2019). Understanding big data analytics for manufacturing processes: Insights from literature review and multiple case studies. Computers & Industrial Engineering, 137, 106099.

    Google Scholar 

  • Beltagui, A., Kunz, N., & Gold, S. (2020). The role of 3D printing and open design on adoption of socially sustainable supply chain innovation. International Journal of Production Economics, 221, 107462.

    Google Scholar 

  • Bottani, E., Centobelli, P., Gallo, M., Kaviani, M. A., Jain, V., & Murino, T. (2019). Modelling wholesale distribution operations: an artificial intelligence framework. Industrial Management & Data Systems, 119(4), 698–718.

    Google Scholar 

  • Butler, C. (2018). Five steps to organisational resilience: Being adaptive and flexible during both normal operations and times of disruption. Journal of Business Continuity & Emergency Planning, 12(2), 103–112.

    Google Scholar 

  • Carvalho, H., Azevedo, S. G., & Cruz-Machado, V. (2012). Agile and resilient approaches to supply chain management: influence on performance and competitiveness. Logistics research, 4(1–2), 49–62.

    Google Scholar 

  • Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86–97.

    Google Scholar 

  • Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39.

    Google Scholar 

  • Choi, T.-M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1883.

    Google Scholar 

  • Chowdhury, M., Quaddus, M., & Agarwal, R. (2019). Supply chain resilience for performance: Role of relational practices and network complexities. Supply Chain Management: An International Journal, 24(5), 659–676.

    Google Scholar 

  • Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1–13.

    Google Scholar 

  • Cooper, V., & Molla, A. (2017). Information systems absorptive capacity for environmentally driven IS-enabled transformation. Information Systems Journal, 24(7), 379–425.

    Google Scholar 

  • Cudeck, R. (1989). Analysis of correlation matrices using covariance structure models. Psychological Bulletin, 105(2), 317–327.

    Google Scholar 

  • Cui, Y., Idota, H., & Ota, M. (2019). Improving supply chain resilience with implementation of new system architecture. In IEEE (Ed.), Social Implications of Technology (SIT) and Information Management (SITIM), (pp. 1–6).

  • Datta, P. (2017). Supply network resilience: A systematic literature review and future research. The International Journal of Logistics Management, 28(4), 1387–1424.

    Google Scholar 

  • Dhamija, P., & Bag, S. (2020). Role of artificial intelligence in operations environment: a review and bibliometric analysis. The TQM Journal, 32(4), 869–896.

  • Dovers, S. R., & Handmer, J. W. (1992). Uncertainty, sustainability and change. Global Environmental Change, 2(4), 262–276.

    Google Scholar 

  • Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., et al. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226, 107599.

    Google Scholar 

  • Dzhengiz, T., & Niesten, E. (2020). Competences for environmental sustainability: A systematic review on the impact of absorptive capacity and capabilities. Journal of Business Ethics, 162(4), 881–906.

    Google Scholar 

  • Elhoone, H., Zhang, T., Anwar, M., & Desai, S. (2020). Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0. International Journal of Production Research, 58(9), 2841–2861.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388.

    Google Scholar 

  • Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28–36.

    Google Scholar 

  • Giannakis, M., & Louis, M. (2016). A multi-agent based system with big data processing for enhanced supply chain agility. Journal of Enterprise Information Management, 29(5), 706–727.

    Google Scholar 

  • Grover, P., Kar, A. K., & Dwivedi, Y. K. (2020). Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03683-9.

  • Guide, V. D., Jr., & Ketokivi, M. (2015). Notes from the editors: Redefining some methodological criteria for the journal. Journal of Operations Management, 37(1), v–viii.

    Google Scholar 

  • Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting & Social Change. https://doi.org/10.1016/j.techfore.2020.120392.

    Article  Google Scholar 

  • Hahn, G. J. (2020). Industry 4.0: A supply chain innovation perspective. International Journal of Production Research, 58(5), 1425–1441.

    Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). London: Pearson.

    Google Scholar 

  • Hazen, B. T., Skipper, J. B., Ezell, J. D., & Boone, C. A. (2016). Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda. Computers & Industrial Engineering, 101, 592–598.

    Google Scholar 

  • Hendry, L. C., Stevenson, M., MacBryde, J., Ball, P., Sayed, M., & Liu, L. (2019). Local food supply chain resilience to constitutional change: The Brexit effect. International Journal of Operations & Production Management, 39(3), 429–453.

    Google Scholar 

  • Hosseini, S., & Ivanov, D. (2020). Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review. Expert Systems with Applications, 161, 113649.

    PubMed  PubMed Central  Google Scholar 

  • Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846.

    Google Scholar 

  • Jain, V., Kumar, S., Soni, U., & Chandra, C. (2017). Supply chain resilience: model development and empirical analysis. International Journal of Production Research, 55(22), 6779–6800.

    Google Scholar 

  • Jin, S., Vegelius, J., & Yang-Wallentin, F. (2020). A marginal maximum likelihood approach for extended quadratic structural equation modeling with ordinal data. Structural Equation Modeling: A Multidisciplinary Journal, 27, 864–873.

    MathSciNet  Google Scholar 

  • Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics, 171, 116–133.

    Google Scholar 

  • Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408–425.

    CAS  Google Scholar 

  • Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179–194. https://doi.org/10.1016/j.ijpe.2019.05.022.

    Article  Google Scholar 

  • Khan, A., Bakkappa, B., Metri, B. A., & Sahay, B. S. (2009). Impact of agile supply chains’ delivery practices on firms’ performance: Cluster analysis and validation. Supply Chain Management: An International Journal, 14(1), 41–48.

    Google Scholar 

  • Klumpp, M. (2018). Automation and artificial intelligence in business logistics systems: Human reactions and collaboration requirements. International Journal of Logistics Research and Applications, 21(3), 224–242.

    Google Scholar 

  • Kochan, C. G., & Nowicki, D. R. (2018). Supply chain resilience: A systematic literature review and typological framework. International Journal of Physical Distribution & Logistics Management, 48(8), 842–865.

    Google Scholar 

  • Kwak, D.-W., Seo, Y.-J., & Mason, R. (2018). Investigating the relationship between supply chain innovation, risk management capabilities and competitive advantage in global supply chains. International Journal of Operations & Production Management, 38(1), 2–21.

    Google Scholar 

  • Lee, H. L. (2018). Big data and the innovation cycle. Production and Operations Management, 27(9), 1642–1646.

    MathSciNet  Google Scholar 

  • Lee, H.-Y., Seo, Y.-J., & Dinwoodie, J. (2016). Supply chain integration and logistics performance: The role of supply chain dynamism. The International Journal of Logistics Management, 27(3), 668–685.

    Google Scholar 

  • Lee, S. M., Lee, D., & Schniederjans, M. J. (2011). Supply chain innovation and organizational performance in the healthcare industry. International Journal of Operations & Production Management, 31(11), 1193–1214.

    Google Scholar 

  • Leitao, P. (2009). Holonic rationale and bio-inspiration on design of complex emergent and evolvable systems. In A. Hameurlain, J. Küng, & R. Wagner (Eds.), Transactions on large-scale data- and knowledge-centered systems (pp. 243–266). Berlin: Springer.

    Google Scholar 

  • Leung, K. H., Luk, C. C., Choy, K. L., Lam, H. Y., & Lee, C. K. (2019). A B2B flexible pricing decision support system for managing the request for quotation process under e-commerce business environment. International Journal of Production Research, 57(20), 6528–6551.

    Google Scholar 

  • Li, G., Kou, C., Wang, Y., & Yang, H. (2020). System dynamics modelling for improving urban resilience in Beijing, China. Resources, Conservation and Recycling, 161, 104954.

    Google Scholar 

  • Li, J., Fang, H., & Song, W. (2018). Sustainability evaluation via variable precision rough set approach: A photovoltaic module supplier case study. Journal of Cleaner Production, 192, 751–765.

    Google Scholar 

  • Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of applied psychology, 86(1), 114.

    CAS  PubMed  Google Scholar 

  • Macías-Escrivá, F. D., Haber, R., Toro, R., & Hernandez, V. (2013). Self-adaptive systems: A survey of current approaches, research challenges and applications. Expert Systems with Applications, 40(18), 7267–7279.

    Google Scholar 

  • Malhotra, M. K., & Grover, V. (1998). An assessment of survey research in POM: from constructs to theory. Journal of operations management, 16(4), 407–425.

    Google Scholar 

  • Mamillo, D. (2015). Supply chain collaboration under uncertainty in the Albanian beer market. Management Dynamics in the Knowledge Economy, 3(1), 99–117.

    Google Scholar 

  • Mandal, S. (2019). The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility. Information Technology & People, 32(2), 297–318.

    Google Scholar 

  • Mehdizadeh, M. (2020). Integrating ABC analysis and rough set theory to control the inventories of distributor in the supply chain of auto spare parts. Computers & Industrial Engineering, 139, 105673.

    Google Scholar 

  • Min, Q., Lu, Y., Liu, Z., Su, C., & Wang, B. (2019). Machine learning based digital twin framework for production optimization in petrochemical industry. International Journal of Information Management, 49, 502–519.

    Google Scholar 

  • Muravev, D., Hu, H., Rakhmangulov, A., & Mishkurov, P. (2020). Multi-agent optimization of the intermodal terminal main parameters by using AnyLogic simulation platform: Case study on the Ningbo-Zhoushan Port. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2020.102133.

  • Namdar, J., Li, X., Sawhney, R., & Pradhan, N. (2018). Supply chain resilience for single and multiple sourcing in the presence of disruption risks. International Journal of Production Research, 56(6), 2339–2360.

    Google Scholar 

  • Nawaz, F., Janjua, N. K., & Hussain, O. K. (2019). PERCEPTUS: Predictive complex event processing and reasoning for IoT-enabled supply chain. Knowledge-Based Systems, 180, 133–146.

    Google Scholar 

  • Paschen, U., Pitt, C., & Kietzmann, J. (2020). Christine Pitt b, Jan Kietzmann. Artificial Intelligence: Building Blocks and an Innovation Typology, 63(2), 147–155.

    Google Scholar 

  • Peng, D. X., Heim, G. R., & Mallick, D. N. (2014). Collaborative product development: The effect of project complexity on the use of information technology tools and new product development practices. Production and Operations Management, 23(8), 1421–1438.

    Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    PubMed  Google Scholar 

  • Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The international journal of logistics management, 20(1), 124–143.

    Google Scholar 

  • Pradana, M., Pérez-Luñ, A., & Fuentes-Blasco, M. (2019). Revisiting measure of absorptive capacity: Applying the scales in Spanish wine industry. Journal of Management Information and Decision Sciences, 22(4), 515–526.

    Google Scholar 

  • Priore, P., Ponte, B., Rosillo, R., & Fuente, D. d. (2019). Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments. International Journal of Production Research, 57(11), 3663–3677.

    Google Scholar 

  • Rodrigues, L.-J. F., & Carpinetti, L. C. (2017). Quantitative models for supply chain performance evaluation: A literature review. Computers & Industrial Engineering, 113, 333–346.

    Google Scholar 

  • Rodríguez-Espíndola, O., Chowdhury, S., Beltagui, A., & Albores, P. (2020). The potential of emergent disruptive technologies for humanitarian supply chains: The integration of blockchain, Artificial Intelligence and 3D printing. International Journal of Production Research, 58, 4610–4630.

    Google Scholar 

  • Sabet, E., Yazdani, B., Kian, R., & Galanakis, K. (2020). A strategic and global manufacturing capacity management optimisation model: A scenario-based multi-stage stochastic programming approach. Omega, 93, 102026.

    Google Scholar 

  • Schniederjans, D. G., Curado, C., & Khalajhedayati, M. (2020). Supply chain digitisation trends: An integration of knowledge management. International Journal of Production Economics, 220, 107439.

    Google Scholar 

  • Scholten, K., Scott, P. S., & Fynes, B. (2019). Building routines for non-routine events: Supply chain resilience learning mechanisms and their antecedents. Supply Chain Management: An International Journal, 24(3), 430–442.

    Google Scholar 

  • Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers & Operations Research, 119, 104926.

    MathSciNet  Google Scholar 

  • Shidpour, H., Cunha, C. D., & Bernard, A. (2016). Group multi-criteria design concept evaluation using combined rough set theory and fuzzy set theory. Expert Systems with Applications, 64, 633–644.

    Google Scholar 

  • Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Production and Operations Management, 27(10), 1849–1867.

    Google Scholar 

  • Tarafdar, M., & Qrunfleh, S. (2017). Agile supply chain strategy and supply chain performance: Complementary roles of supply chain practices and information systems capability for agility. International Journal of Production Research, 55(4), 925–938.

    Google Scholar 

  • Thomassey, S. (2010). Sales forecasts in clothing industry: The key success factor of the supply chain management. International Journal of Production Economics, 128(2), 470–483.

    Google Scholar 

  • Trabucchi, D., & Buganza, T. (2019). Data-driven innovation: Switching the perspective on Big Data. European Journal of Innovation Management, 22(1), 23–40.

    Google Scholar 

  • Tushman, M. L., & Nadler, D. A. (1978). Academy of management review. Information Processing as an Integrating Concept in Organizational Design, 3(3), 613–624.

    Google Scholar 

  • Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212–227.

    Google Scholar 

  • Wamba, S. F., & Akter, S. (2019). Understanding supply chain analytics capabilities and agility for data-rich environments. International Journal of Operations & Production Management, 39(6/7/8), 887–912.

    Google Scholar 

  • Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2020). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journal of Production Economics, 222, 107498.

    Google Scholar 

  • Wong, C. W., Lirn, T.-C., Yang, C.-C., & Shang, K.-C. (2020). Supply chain and external conditions under which supply chain resilience pays: An organizational information processing theorization. International Journal of Production Economics, 226, 107610.

    Google Scholar 

  • Yen, B. P.-C., & Zeng, B. (2011). Modeling and analysis of supply chain risk system under the influence of partners' collaboration. In Dans, I. E. E. E. (Ed.), 44th Hawaii International Conference on System Sciences (pp. 1–10).

  • Yu, W., Jacobs, M. A., Chavez, R., & Yang, J. (2019). Dynamism, disruption orientation, and resilience in the supply chain and the impacts on financial performance: A dynamic capabilities perspective. International Journal of Production Economics, 218, 352–362.

    Google Scholar 

  • Zanjani, M. K., Bajgiran, O. S., & Nourelfath, M. (2016). A hybrid scenario cluster decomposition algorithm for supply chain tactical planning under uncertainty. European Journal of Operational Research, 252(2), 466–476.

    MathSciNet  Google Scholar 

  • Zhang, Q., Liu, P., & Pannek, J. (2019). Combining MPC and integer operators for capacity adjustment in job-shop systems with RMTs. International Journal of Production Research, 57(8), 2498–2513.

    Google Scholar 

  • Zhou, H., & Benton, W. C. (2007). Supply chain practice and information sharing. Journal of Operations Management, 25(6), 1348–1365.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cadi Ayyad University, Marrakech, Morocco

    Amine Belhadi

  2. Montpellier Business School (MBS), Montpellier, France

    Venkatesh Mani

  3. EDHEC Business School Roubaix, Roubaix, France

    Sachin S. Kamble

  4. School of Economics and Management, Tsinghua University, Beijing, China

    Syed Abdul Rehman Khan

  5. Department of Marketing and Management, University of Southern Denmark, Odense, Denmark

    Surabhi Verma

Authors
  1. Amine Belhadi
    View author publications

    Search author on:PubMed Google Scholar

  2. Venkatesh Mani
    View author publications

    Search author on:PubMed Google Scholar

  3. Sachin S. Kamble
    View author publications

    Search author on:PubMed Google Scholar

  4. Syed Abdul Rehman Khan
    View author publications

    Search author on:PubMed Google Scholar

  5. Surabhi Verma
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Venkatesh Mani.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: measurement constructs used in the study

Appendix: measurement constructs used in the study

Constructs

Source

Items

Artificial Intelligence (AI)

Dubey et al. (2020)

AI_1. We possess the infrastructure and skilled resources to apply AI information processing system

AI_2. We use AI techniques to forecast and predict environmental behavior

AI_3. We develop statistical, self-learning, and prediction using AI techniques

AI_4. We use AI techniques at all level of the supply chain

AI_5. We use AI outcomes in a shared way to inform supply chain decision-making

Supply Chain Resilience (SCRes)

Yu et al. (2019), Altay et al. (2018 )

SCRes_1. Our firm's supply chain is well prepared to face constraints of supply chain disruptions

SCRes_2.Our firm's supply chain can rapidly plan and execute contingency plans during disruptions

SCRes_3. Our firm's supply chain can adequately respond to unexpected disruptions by quickly restoring its product flow

SCRes_4. Our firm's supply chain can swiftly return to its original state after being disrupted

SCRes_5. Our firm's supply chain can gain a superior state compared to its original state after being disrupted

Supply Chain Performance (SCP)

Srinivasan and Swink (2018)

SCP_1. Order fill rate (% complete, error-free orders shipped on time)

SCP_2. On-time delivery

SCP_3. Order fulfillment lead time (speed)

SCP_4. Product unit cost

Adaptive Capabilities (AC)

Tarafdar and Qrunfleh (2017), Srinivasan and Swink (2018)

AC_1. We can rapidly adjust capacity to accelerate or decelerate production in response to external changes

AC_2. We can meet particular customer specification

AC_3. We can swiftly introduce large numbers of product improvements/variation

Supply Chain Collaboration (SCC)

Dubey et al. (2020), Yu et al. (2019), Srinivasan and Swink (2018)

SCC_1. We continuously share our resources (i.e., data, information, knowledge, and infrastructure) with our suppliers, partners …etc.

SCC_2. We cooperate tightly with our partners to define and implement response strategies

SCC_3. We share our risks and benefits

Supply Chain dynamism (SCD)

Dubey et al. (2020), Yu et al. (2019)

SCD_1. Operating processes become outdated at a high rate

SCD_2. Customers' requirements change at a high rate

SCD_3. Unexpected and disruptive events (i.e. shocks, outbreaks, disruptive technologies) occur at a high rate

SCD_4. Competitors' capabilities change at a high rate

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Belhadi, A., Mani, V., Kamble, S.S. et al. Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Ann Oper Res 333, 627–652 (2024). https://doi.org/10.1007/s10479-021-03956-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-021-03956-x

Keywords

Profiles

  1. Venkatesh Mani View author profile