CFP last date
20 December 2024
Reseach Article

Studying the Inter-Relationship amongst the Barriers to Implementation of Analytics in Manufacturing Supply Chains

by Bhoomica Aggarwal, Remica Aggarwal, S. P. Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 34
Year of Publication: 2018
Authors: Bhoomica Aggarwal, Remica Aggarwal, S. P. Singh
10.5120/ijca2018918236

Bhoomica Aggarwal, Remica Aggarwal, S. P. Singh . Studying the Inter-Relationship amongst the Barriers to Implementation of Analytics in Manufacturing Supply Chains. International Journal of Computer Applications. 181, 34 ( Dec 2018), 12-19. DOI=10.5120/ijca2018918236

@article{ 10.5120/ijca2018918236,
author = { Bhoomica Aggarwal, Remica Aggarwal, S. P. Singh },
title = { Studying the Inter-Relationship amongst the Barriers to Implementation of Analytics in Manufacturing Supply Chains },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2018 },
volume = { 181 },
number = { 34 },
month = { Dec },
year = { 2018 },
issn = { 0975-8887 },
pages = { 12-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number34/30209-2018918236/ },
doi = { 10.5120/ijca2018918236 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:08:06.159393+05:30
%A Bhoomica Aggarwal
%A Remica Aggarwal
%A S. P. Singh
%T Studying the Inter-Relationship amongst the Barriers to Implementation of Analytics in Manufacturing Supply Chains
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 34
%P 12-19
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With every economy becoming globalized , operations of global manufacturing and logistics teams are becoming complex and challenging . Delayed shipments, inefficient plants, inconsistent suppliers can stall and delay the shipments thereby increasing the company’s supply chain costs. Managing demand volatility and cost fluctuations in supply chain and making it visible globally are some of the challenges which supply chain managers are facing. As per Accenture report , only up to 17 % of the supply chain managers are comfortable implementing analytics to supply chain functions which means despite being a need for these supply chain managers and despite being the fact that analytics can serve as their problem solver , it cannot , and still has a long way to go to prove itself in this domain . The required foundation is still in its nascent stage . This research work thus focuses on studying and exploring the barriers to implementation of analytics or big data analytics to manufacturing supply chains . After exploring , it further study the interrelationship amongst them with the help of Interpretive Structural Modelling (ISM) methodology .

References
  1. Wang, G., Gunasekaran , A., Ngai, E. W. T. and Papadopoulos, T. 2016 a. Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.
  2. Waller and facwcet Waller , M.A. and Fawcett , S. E. 2013a. Big data, predictive analytics and theory development in the era of a maker movement supply chain. Journal of Business Logistics, 34, 249-252.
  3. Waller , M. A. and Fawcett, S. E. 2013b. Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34, 77-84.
  4. Wang, Y. and Hajli, N. 2017. Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70, 287–299. https://doi.org/10.1016/j.jbusres.2016.08.002
  5. Gandomi, A., & Haider, M. 2015. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
  6. Tsai, C.-W. W., Lai, C.-F. F., Chao, H.-C. C. and Vasilakos, A. V. 2015. Big data analytics : a survey. Journal of Big Data, 2, 1–32. https://doi.org/10.1186/s40537-015-0030-3.
  7. Alharthi, A., Krotov, V. and Bowman, M. 2017. Addressing barriers to big data. Business Horizons, 60(3), 285–292. https://doi.org/10.1016/j.bushor.2017.01.002.
  8. Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M. and Vasilakos, A. V. 2017. The role of big data analytics in Internet of Things. Computer Networks, in press, 1–13. https://doi.org/10.1016/j.comnet.2017.06.013 .
  9. Zhong, R. Y., Newman, S. T., Huang, G. Q. and Lan, S. 2016. Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers and Industrial Engineering, http://dx.doi.org/10.1016/j.cie.2016.07.013.
  10. Malaka, I. and Brown, I. 2015b. Challenges to the organisational adoption of big data analytics : A case study in the South African telecommunications industry. In Proceedings of the 2015 Annual Research Conference on South African Institute of Computer Scientists and Information Technologists (pp. 1–9). Stellenbosch, South Africa: ACM, New York. https://doi.org/10.1145/2815782.2815793.
  11. Addo-Tenkorang, R. and Helo, P. T. 2016. Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering, 101, 528–543. https://doi.org/10.1016/j.cie.2016.09.023
  12. Arunachalam, D., Kumar, N. and Kawalek, J. P. 2017. Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, in press, 1–21. https://doi.org/10.1016/j.tre.2017.04.001
  13. Singh, A., Shukla, N. and Mishra, N. 2017. Social media data analytics to improve supply chain management in food industries. Transportation Research Part E: Logistics and Transportation Review. https://doi.org/10.1016/j.tre.2017.05.008.
  14. Chae, B. 2015. Insights from supply chain and Twitter analytics: considering twitter and twitter data for supply chain practice and research. International Journal of Production Economics, 165, 247–259. http://dx.doi.org/10.1016/j.ijpe.2014.12.037.
  15. Bi, Z. and Cochran, D. 2014. Big data analytics with applications. Journal of Management Analytics,1(4), 249–265. https://doi.org/10.1080/23270012.2014.992985
  16. Li, J., Tao, F., Cheng, Y.and Zhao, L. 2015. Big data in product lifecycle management. International Journal of Advanced Manufacturing Technology, 81(1–4), 667–684. https://doi.org/10.1007/s00170-015-7151-x .
  17. Hazen, B. T., Boone, C. A., Ezell, J. D. and Jones-Farmer, L. A. 2014. Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.
  18. Meriton, R.F. and Graham, G. 2016. Big data and supply chain management: A marriage of convenience. Presented at the 20th International Manufacturing Symposium, Cambridge, UK, 29-30th September.
  19. Opresnik, D. and Taisch, M. 2015. The value of big data in servitization. International Journal of Production Economics, 165, 174-184.
  20. Radke, A.M., Tseng, M.M. 2015. Design considerations for building distributed supply chain management systems based on cloud computing. Journal of Manufacturing Science Engineering, 137. http://dx.doi.org/10.1115/1.4030736.
  21. Öberg, C. and Graham, G. 2016. How smart cities will change supply chain management: A technical viewpoint. Production Planning and Control, 27, 529-538.
  22. Hahn, G. J. and Packowski, J. 2015. A perspective on applications of in-memory analytics in supply chain management. Decision Support Systems, 76, 45-52.
  23. Lee, I. 2017. Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293–303. https://doi.org/10.1016/j.bushor.2017.01.004.
  24. Kang, Y., Park, I., Rhee, J. and Lee, Y. 2016. MongoDB-based repository design for IoT-generated RFID/sensor big data. IEEE Sens. J. 16, 485–497. http://dx.doi.org/ 10.1109/JSEN.2015.2483499.
  25. Liu, Y. Q. and Wang, H. 2016 . Order allocation for service supply chain base on the customer best delivery time under the background of big data. International Journal of Computer Science and Applications, 13, 84-92.
  26. Giannkis, M. and Louis, M. 2016. A multi-agent based system with big data processing for enhanced supply chain agility. Journal of Enterprise Information Management, 29, 706-727.
  27. Chen, D. Q., Presteon, D. S. and Swink, M. 2015. How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32, 4-39.
  28. Schoenherr, T. and Speier-Pero, C. 2015. Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36, 120- 132.
  29. Wu, C. X., Zhao, D. Z. and Pan, X. Y. 2016. Comparison on dynamic cooperation strategies of a three echelon supply chain involving big data service provider. Kongzhi yu Juece/Control and Decision, 31, 1169-1177.
  30. Vander Spoel, S., Amrit, C. and Van Hillegersberg , J. 2015. Predictive analytics for truck arrival time estimation: a field study at a European distribution center. International Journal of Production Research, 1–17. http://dx.doi.org/10.1080/00207543.2015.1064183.
  31. Hofmann, E. 2015. Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, 7543,1–19. http://dx.doi.org/10.1080/00207543.2015.1061222.
  32. Hilbert, M. 2016. Big Data for Development: A review of promises and challenges. Development Policy Review, 34(1), 135–174. https://doi.org/10.1111/dpr.12142.
  33. Sivarajah, U., Kamal, M. M., Irani, Z. and Weerakkody, V. 2017. Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001.
  34. Mangla, S. K., Govindan, K. and Luthra, S. 2017. Prioritizing the barriers to achieve sustainable consumption and production trends in supply chains using fuzzy Analytical Hierarchy Process. Journal of Cleaner Production. 151, 509–525. https://doi.org/10.1016/j.jclepro.2017.02.099.
  35. Moktadir , M.A. , Ali , S.M. ,Paul, S. and Shukla, N. 2018. Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh, Computers & Industrial Engineering . DOI: 10.1016/j.cie.2018.04.013.
  36. Warfield, J., N. 1974. Developing interconnection matrices in structural modeling. In the proceedings of IEEE Transactions on System, Man, and Cybernetics (SMC), 4 (1), 81-87.
Index Terms

Computer Science
Information Sciences

Keywords

Manufacturing supply chain operations supply chain analytics real time decision making