Due to the rapid progress of information technology, organizations anticipate significant changes in the planning, scheduling, and optimization aspects of operation and supply chain management (SCM) shortly. Two primary types of risk have an impact on supply chain management and design. The first group deals with the difficulties in matching supply and demand, whereas the second group deals with disruptions to regular business operations. The essay offers a theoretical framework that combines the cooperative efforts of risk assessment and mitigation, which are critical for effectively handling potential supply chain interruptions. This content provides insightful viewpoints on the strategic resources and operational structure needed to improve organizational success. We utilized the partial least squares (PLS) method to address the problem of multicollinearity and measurement mistakes in examining cause-and-effect constructs. The statistical method, Least Squares (PLS), used in structural equation modeling, is based on partial variance. The Partial Least Squares (PLS) strategy uses a two-stage estimate procedure to calculate weights, loadings, and route estimations. Initially, several simple and complex regressions were performed with the provided model. The procedure was repeated until a solution was found, resulting in a set of weights used to determine the latent variable scores. In the second step, non-iterative PLS regression yields loadings, path coefficients, mean scores, and location parameters. According to the structural study, implementing Sustainable Supply Chain Management (SSCM) can significantly improve a business’s operational and financial performance. The findings offer a comprehensive understanding of several elements of supply chain management (SSCM), including information systems, organizational configurations, supply chain network architecture (SCND), and supply chain strategy (SCS). The supply chain is essential for effectively moving goods over great distances and encouraging cooperation between parties. Therefore, these connections are established precisely, quickly, and cheaply via a knowledgeable and efficient supply chain. Two key components are necessary for a supply chain (SC) to be successful: efficient collaboration and the smooth integration of information-sharing platforms.
References
[1]
Newhart, D.D., Stott, K.L. and Vasko, F.J. (1993) Consolidating Product Sizes to Minimize Inventory Levels for a Multi-Stage Production and Distribution System. TheJournaloftheOperationalResearchSociety, 44, 637-644. https://doi.org/10.2307/2584038
[2]
Gates, A.F., Natkovich, O., Chopra, S., Kamath, P., Narayanamurthy, S.M., Olston, C., etal. (2009) Building a High-Level Dataflow System on Top of Map-Reduce: The Pig Experience. ProceedingsoftheVLDBEndowment, 2, 1414-1425. https://doi.org/10.14778/1687553.1687568
[3]
Zhao, X., Yeung, K., Huang, Q. and Song, X. (2015) Improving the Predictability of Business Failure of Supply Chain Finance Clients by Using External Big Dataset. IndustrialManagement&DataSystems, 115, 1683-1703. https://doi.org/10.1108/imds-04-2015-0161
[4]
Andersen, T.J. (2001) Information Technology, Strategic Decision Making Approaches and Organizational Performance in Different Industrial Settings. TheJournalofStrategicInformationSystems, 10, 101-119. https://doi.org/10.1016/s0963-8687(01)00043-9
[5]
Au, K.F. and Ho, D.C.K. (2002) Electronic Commerce and Supply Chain Management: Value‐Adding Service for Clothing Manufacturers. IntegratedManufacturingSystems, 13, 247-255. https://doi.org/10.1108/09576060210426949
[6]
Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., etal. (2008) Bigtable: A Distributed Storage System for Structured Data. ACMTransactionsonComputerSystems, 26, 1-26. https://doi.org/10.1145/1365815.1365816
[7]
Lee, S., Gautam, N., Kumara, S., Hong, Y., Gupta, H., Surana, A., et al. (2002) Situation Identification Using Dynamic Parameters in Complex Agent-Based Planning Systems. Intelligent Engineering Systems through Artificial Neural Networks, 12, 555-560.
[8]
Abouzeid, A., Bajda-Pawlikowski, K., Abadi, D., Silberschatz, A. and Rasin, A. (2009) HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads. ProceedingsoftheVLDBEndowment, 2, 922-933. https://doi.org/10.14778/1687627.1687731
[9]
Agrawal, D., Abbadi, A.E., Emekci, F. and Metwally, A. (2009) Database Management as a Service: Challenges and Opportunities. 2009 IEEE 25thInternationalConferenceonDataEngineering, Shanghai, 29 March-2 April 2009, 1709-1716. https://doi.org/10.1109/icde.2009.151
[10]
Wu, Y.-M., Zhu, S. and Fan, J.-Q. (2011) Spatial-Temporal Statistical Analysis of Chengdu-Chongqing Regional Economic Structure Evolution—Based on Comparative Study with Five Major Economic Regions and Four Major Urban Groups. 2011 InternationalConferenceonManagementScience&Engineering 18thAnnualConferenceProceedings, Rome, 13-15 September 2011, 702-708. https://doi.org/10.1109/icmse.2011.6070039
[11]
Bao, X., Zhang, S. and Li, Y. (2023) Study on the Strength of Economic and Social Development of Cities in Liaoning Province in China: Data Analysis Based on Stata. 2023 8th International Conference on Computer and Communication Systems (ICCCS), Guangzhou, 21-23 April 2023, 1092-1096. https://doi.org/10.1109/icccs57501.2023.10151306
[12]
Chen, Z. (2021) Analysis on the Application of Data Analysis in Economic Management. 2021 InternationalConferenceonBigDataAnalysisandComputerScience (BDACS), Kunming, 25-27 June 2021, 84-87. https://doi.org/10.1109/bdacs53596.2021.00026
[13]
Zhou, J., Shi, P. and Kong, L. (2011) Temporal and Spatial Characteristics of County Level Economic Disparities of Baiyin Region during the Period of 1994-2008. 2011 InternationalConferenceonMultimediaTechnology, Hangzhou, 26-28 July 2011, 790-793. https://doi.org/10.1109/icmt.2011.6001871