Abstract
In recent years, big data analytics has received more attention from companies around the world. The explosive impact of big data analytics on globalized companies has brought new opportunities implementing big data-driven decisions that are sweeping many industries and business functions. Big data analytics has a lot of potential to improve supply chain performance in logistics industry. Big data analytics is frequently used in the logistics/supply chain management industry as an instrument to improve the performance of the system. As the supply chain performance depends on information on a high degree, big data analytics seems to be very useful in improving supply chain performance. However, many companies have not been able to apply to the same degree of the “big data analytics” techniques that could transform the way they manage their supply chains. This research demonstrated how companies can take control of the big data opportunity with a systematic approach. This study utilized survey questionnaires using Google Form to collect data from university students and people working in Klang Valley, Malaysia. It may be viewed that this result unlikely can be an appropriate representation of the whole population of Malaysia. It was concluded that several factors such as improved forecasting, supply chain system integration, human capital and risk and security governance have significant relationship towards supply chain performance in logistics industry. However, two other factors operational efficiency and partner transparency do not have significant relationship with supply chain performance. This research offers a bigger picture of how the use of big data analytics can improve the supply chain performance in logistics industry. Logistics industry could benefit from the results of this research by understanding the key success factors of big data analytics to improve supply chain performance in logistics industry.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beamon BM (1999) Measuring supply chain performance. Int J Oper Manag 2:275–292. Retrieve 10 June 2017 from http://www.emeraldinsight.com/doi/full/10.1108/01443579910249714
Davenport TH (2006) Competing on analytics. Harv Bus Rev 84(1):98–107
Genpact (2014) Supply chain analytics. Retrieved 9 October 2017 from http://www.genpact.com/docs/resource-/supply-chain-analytics
Hwang HJ, Seruga J (2011) An intelligent supply chain management system to enhance collaboration in textile industry. Int J u- and e-Service, Sci Technol 4(4):47–62
IBM (2017) Big Data—IBM analytics. IBM. Retrieved 12 June 2017, from https://www.ibm.com/analytics/us/en/technology/big-data/
Intrieri C (2016) Measuring supply chain performance: 3 Core & 10 Soft Metrics. Transportation Management Company | Cerasis. Retrieved 13 June 2017, from http://cerasis.com/2016/04/25/measuring-supply-chain-performance/
Investopedia (2017) Operational efficiency. Investopedia. Retrieved 12 June 2017, from http://www.investopedia.com/terms/o/operationalefficiency.asp
Kache F, Seuring S (2015) Challenges and opportunities of digital information at the intersection of Big Data analytics and supply chain management. Int J Oper Prod Manag 37:10–36. Retrieved 10 June 2017 from http://www.emeraldinsight.com/doi/full/10.1108/IJOPM-02-2015-0078
McAfee A, Brynjolfsson E (2012) Big Data: the management revolution. Harvard Bus Rev 90(10):60–66
New S (2015) McDonald’s and the challenges of a modern supply chain. Harv Bus Rev February 4, Retrieved 12 June 2017 from https://hbr.org/2015/02/mcdonalds-and-the-challenges-of-a-modern-supply-chain
Richards NM, King JH (2013) Three paradoxes of Big Data. Stanf Law Rev Online 66(41):41–46
Richey RG, Morgan TR, Hall KL, Adams FG (2016) A global exploration of Big Data in the supply chain. Int J Phys Distrib Logist Manag 49:710–739. Retrieved 9 June 2017 from http://www.emeraldinsight.com/doi/full/10.1108/IJPDLM-05-2016-0134
Sahay BS, Ranjan J (2008) Real time business intelligence in supply chain analytics. Inf Manag Comput Secur 16(1):28–48
Shmueli G, Koppius O (2011) Predictive analytics and informations systems research. MIS Q 35(3):553–572
Tan KH, Zhan Y, Ji G, Ye F, Chang C (2015) Harvesting Big Data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph. Int J Prod Econ 165:223–233
Tavakol M, Dennick R (2011) Making sense of cronbach’s alpha. Int J Med Educ 2:53–55. Retrieved 17 June 2017, from https://www.ijme.net/archive/2/cronbachs-alpha.pdf
Tucker P (2013) The future is not a destination. Available via: http://www.slate.com/articles/technology/future_tense/2013/10/futurist_magazine_s_ predictions_on_quantum_computing_big_data_and_more.html
Waller MA, Fawcett SE (2013) Data science, predictive analytics, and Big Data: a revolution that will transform supply chain design and management. J Bus Logist 2:77–84. Retrieved 5 June 2017 from https://pdfs.semanticscholar.org/9c1b/9598f82f9ed7d75ef1a9e627496759aa2387.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Xiang, L.Y., Hwang, H.J., Kim, H.K., Mahmood, M., Dawi, N.M. (2021). The Use of Big Data Analytics to Improve the Supply Chain Performance in Logistics Industry. In: Kim, H., Lee, R. (eds) Software Engineering in IoT, Big Data, Cloud and Mobile Computing. Studies in Computational Intelligence, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-030-64773-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-64773-5_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64772-8
Online ISBN: 978-3-030-64773-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)