Business Intelligence and Analytics in Supply Chain Management

B. R. Sarath  Kumar; B. V.  Ramana

doi:10.17762/turcomat.v10i1.14266

PDF

Published: Apr 10, 2019

DOI: https://doi.org/10.17762/turcomat.v10i1.14266

Keywords:

Business Intelligence, Analytics, Operations Management, Supply Chain Management

B. R. Sarath Kumar

Dept of CSE, Lenora College of Engineering, Rampachodavaram, A.P, India.

B. V. Ramana

Dept of IT, Aditya Institute of Technology and Management, Tekkali, AP, India.

Abstract

The objective of this study is to provide informative information to assist practitioners, researchers, and decision-makers in navigating the ever-changing environment of operations management. The constantly shifting terrain of the contemporary corporate world places a premium on strong operations management as an essential component of any organization. It is impossible to satisfy the needs of customers, bring down operating expenses, and achieve operational excellence without first developing a seamless orchestration of resources, processes, and people. Business Intelligence (BI) and Analytics are becoming more important to enterprises as crucial tools for making decisions based on data for them to succeed in the current fast-paced environment.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

How to Cite

Kumar, B. R. S. ., & Ramana, B. V. . (2019). Business Intelligence and Analytics in Supply Chain Management. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 10(1), 666–670. https://doi.org/10.17762/turcomat.v10i1.14266

Issue

Vol. 10 No. 1 (2019)

Section

Articles

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Licensing

TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.

Detailed Licensing Terms

Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.

No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

References

C. Wilder and C. Ozgur, “Business analytics for business analysts in manufacturing,” Operations

and Service Management, pp. 272–280, 2018. doi:10.4018/978-1-5225-3909-4.ch013

R. Saxena and A. Srinivasan, “Business intelligence,” International Series in Operations Research

& Management Science, pp. 85–99, 2012. doi:10.1007/978-1-4614-6080-0_7

“Supply Chain Performance Management (SCPM) Als interorganizational steuerungskonzeption

für das Supply Chain Management,” Supply chain Performance Management, pp. 211–263.

doi:10.1007/978-3-8350-9026-2_4

S. Zeng, M. A. Cohen, B. J. Steele, and J. Sairamesh, “A supplier performance evaluation solution

for proactive supplier Quality Management,” 2008 IEEE International Conference on e-Business

Engineering, 2008. doi:10.1109/icebe.2008.90

“Inventory control,” Springer Reference. doi:10.1007/springerreference_6796

A. J. Noga, Adjustable bandwidth concept (ABC) performance evaluation, 2003.

doi:10.21236/ada416986

C. R. Silversides and U. Sundberg, “Operational efficiency,” Operational Efficiency in Forestry,

pp. 3–4, 1989. doi:10.1007/978-94-017-0506-6_2

“Key performance indicators,” Project Management Metrics, KPIs, and Dashboards, pp. 117–164,

doi: 10.1002/9781118826751.ch4

Y. Cook and T. Naidoo, “Business process automation: Automating and monitoring the

mortgage process,” Business Process Automation, pp. 73–82, 2004. doi:10.1007/978-3-540-24702-

_6.

Business Intelligence and Analytics in Supply Chain Management